{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics Coded: How do women and men use their time - statistics \n",
    "\n",
    "**By [Mészáros Mátyás](https://github.com/mmatyi)**\n",
    "\n",
    "This noteboook reproduces step by step the figures avialable in the original [Statistics Explaind page](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=How_do_women_and_men_use_their_time_-_statistics) and in the [source excel file](https://ec.europa.eu/eurostat/statistics-explained/images/2/2e/SE_Article_Charts_HETUS_2010_FINAL_06.09.2019.xlsx)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the year in a variable `yr<-2010` so if i want to rerun the code for a different year we have to change only this line. In addition we will retrieve the EU28 country names which will be used in later steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "restatapi: - config file with the API version 1 loaded from GitHub (the 'current' API version number is 1).\n",
      "           - 4 from the 32 cores are used for parallel computing, can be changed with 'options(restatapi_cores=...)'\n",
      "           - 'auto' method will be used for file download, can be changed with 'options(restatapi_dmethod=...)'\n",
      "           - the Table of contents (TOC) was not pre-loaded into the deafult cache ('.restatapi_env').\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(restatapi)\n",
    "library(data.table)\n",
    "library(ggplot2)\n",
    "library(chron)\n",
    "yr<-\"2010\"\n",
    "eu_ctry_names<-do.call(rbind,lapply(get(\"cc\",envir=.restatapi_env)$EU28,search_eurostat_dsd,dsd=get_eurostat_dsd(\"tus_00age\"),exact_match=TRUE))$name\n",
    "eu_ctry_names<-gsub(\" \\\\(.*\\\\)\",\"\",eu_ctry_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 1: Mean time spent on daily activities, all individuals by country, (hh:mm; 2008 to 2015)\n",
    "\n",
    "\n",
    "The data is in the *tus_00age* dataset. We use the [restatapi](https://github.com/eurostat/restatapi) package to download the data. We apply filter to the data for the year (`date_filter=yr`) . We also filter for the other values in the graph (`filters=list(unit=\"spent\",age=\"total\",sex=\"total\",acl00=c(\"sleep\",\"eat\",\"^employ\",\" (family|personal) care\",\"^leisure\",\"^study\",\"except travel\"))`. If we use the [REST SDMX](https://ec.europa.eu/eurostat/web/sdmx-web-services/rest-sdmx-2.1) service to get the filtered dataset, then we get no data because all the values are **NaN** (Not a Number) as the values are time spans. In order to get the data we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 0 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>age</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 0 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & age & geo & time & values\\\\\n",
       " <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 0 × 7\n",
       "\n",
       "| unit &lt;fct&gt; | sex &lt;fct&gt; | acl00 &lt;fct&gt; | age &lt;fct&gt; | geo &lt;fct&gt; | time &lt;fct&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "\n"
      ],
      "text/plain": [
       "     unit sex acl00 age geo time values"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 144 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Austria                                         </td><td>2010</td><td>8:31</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Belgium                                         </td><td>2010</td><td>8:36</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>8:26</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Estonia                                         </td><td>2010</td><td>8:50</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Greece                                          </td><td>2010</td><td>8:36</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Spain                                           </td><td>2010</td><td>8:38</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Finland                                         </td><td>2010</td><td>8:35</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>France                                          </td><td>2010</td><td>8:30</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Hungary                                         </td><td>2010</td><td>8:34</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Italy                                           </td><td>2010</td><td>8:32</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Luxembourg                                      </td><td>2010</td><td>8:40</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Netherlands                                     </td><td>2010</td><td>8:26</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Norway                                          </td><td>2010</td><td>8:07</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Poland                                          </td><td>2010</td><td>8:37</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Romania                                         </td><td>2010</td><td>8:52</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Serbia                                          </td><td>2010</td><td>8:21</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>Turkey                                          </td><td>2010</td><td>8:42</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Sleep </td><td>United Kingdom                                  </td><td>2010</td><td>8:22</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Austria                                         </td><td>2010</td><td>1:24</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Belgium                                         </td><td>2010</td><td>1:44</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>1:43</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Estonia                                         </td><td>2010</td><td>1:21</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Greece                                          </td><td>2010</td><td>2:13</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Spain                                           </td><td>2010</td><td>1:59</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Finland                                         </td><td>2010</td><td>1:23</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>France                                          </td><td>2010</td><td>2:12</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Hungary                                         </td><td>2010</td><td>1:42</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Italy                                           </td><td>2010</td><td>1:57</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Luxembourg                                      </td><td>2010</td><td>1:54</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Eating</td><td>Netherlands                                     </td><td>2010</td><td>1:57</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Finland                                         </td><td>2010</td><td>6:05</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>France                                          </td><td>2010</td><td>5:03</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Hungary                                         </td><td>2010</td><td>5:00</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Italy                                           </td><td>2010</td><td>4:54</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Luxembourg                                      </td><td>2010</td><td>4:35</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Netherlands                                     </td><td>2010</td><td>5:34</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Norway                                          </td><td>2010</td><td>5:53</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Poland                                          </td><td>2010</td><td>5:02</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Romania                                         </td><td>2010</td><td>4:26</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Serbia                                          </td><td>2010</td><td>5:29</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>Turkey                                          </td><td>2010</td><td>5:27</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Leisure, social and associative life</td><td>United Kingdom                                  </td><td>2010</td><td>5:15</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Austria                                         </td><td>2010</td><td>1:09</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Belgium                                         </td><td>2010</td><td>1:20</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>1:17</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Estonia                                         </td><td>2010</td><td>1:16</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Greece                                          </td><td>2010</td><td>1:02</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Spain                                           </td><td>2010</td><td>1:10</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Finland                                         </td><td>2010</td><td>1:22</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>France                                          </td><td>2010</td><td>1:23</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Hungary                                         </td><td>2010</td><td>1:03</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Italy                                           </td><td>2010</td><td>1:19</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Luxembourg                                      </td><td>2010</td><td>1:32</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Netherlands                                     </td><td>2010</td><td>1:31</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Norway                                          </td><td>2010</td><td>1:26</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Poland                                          </td><td>2010</td><td>1:06</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Romania                                         </td><td>2010</td><td>0:58</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Serbia                                          </td><td>2010</td><td>1:07</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>Turkey                                          </td><td>2010</td><td>1:04</td></tr>\n",
       "\t<tr><td>Time spent (hh:mm)</td><td>Total</td><td>Total</td><td>Travel except travel related to jobs</td><td>United Kingdom                                  </td><td>2010</td><td>2:09</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 144 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & age & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Austria                                          & 2010 & 8:31\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Belgium                                          & 2010 & 8:36\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Germany (until 1990 former territory of the FRG) & 2010 & 8:26\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Estonia                                          & 2010 & 8:50\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Greece                                           & 2010 & 8:36\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Spain                                            & 2010 & 8:38\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Finland                                          & 2010 & 8:35\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & France                                           & 2010 & 8:30\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Hungary                                          & 2010 & 8:34\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Italy                                            & 2010 & 8:32\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Luxembourg                                       & 2010 & 8:40\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Netherlands                                      & 2010 & 8:26\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Norway                                           & 2010 & 8:07\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Poland                                           & 2010 & 8:37\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Romania                                          & 2010 & 8:52\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Serbia                                           & 2010 & 8:21\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & Turkey                                           & 2010 & 8:42\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Sleep  & United Kingdom                                   & 2010 & 8:22\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Austria                                          & 2010 & 1:24\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Belgium                                          & 2010 & 1:44\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Germany (until 1990 former territory of the FRG) & 2010 & 1:43\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Estonia                                          & 2010 & 1:21\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Greece                                           & 2010 & 2:13\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Spain                                            & 2010 & 1:59\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Finland                                          & 2010 & 1:23\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & France                                           & 2010 & 2:12\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Hungary                                          & 2010 & 1:42\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Italy                                            & 2010 & 1:57\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Luxembourg                                       & 2010 & 1:54\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Eating & Netherlands                                      & 2010 & 1:57\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Finland                                          & 2010 & 6:05\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & France                                           & 2010 & 5:03\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Hungary                                          & 2010 & 5:00\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Italy                                            & 2010 & 4:54\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Luxembourg                                       & 2010 & 4:35\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Netherlands                                      & 2010 & 5:34\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Norway                                           & 2010 & 5:53\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Poland                                           & 2010 & 5:02\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Romania                                          & 2010 & 4:26\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Serbia                                           & 2010 & 5:29\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & Turkey                                           & 2010 & 5:27\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Leisure, social and associative life & United Kingdom                                   & 2010 & 5:15\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Austria                                          & 2010 & 1:09\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Belgium                                          & 2010 & 1:20\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Germany (until 1990 former territory of the FRG) & 2010 & 1:17\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Estonia                                          & 2010 & 1:16\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Greece                                           & 2010 & 1:02\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Spain                                            & 2010 & 1:10\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Finland                                          & 2010 & 1:22\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & France                                           & 2010 & 1:23\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Hungary                                          & 2010 & 1:03\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Italy                                            & 2010 & 1:19\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Luxembourg                                       & 2010 & 1:32\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Netherlands                                      & 2010 & 1:31\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Norway                                           & 2010 & 1:26\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Poland                                           & 2010 & 1:06\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Romania                                          & 2010 & 0:58\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Serbia                                           & 2010 & 1:07\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & Turkey                                           & 2010 & 1:04\\\\\n",
       "\t Time spent (hh:mm) & Total & Total & Travel except travel related to jobs & United Kingdom                                   & 2010 & 2:09\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 144 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | age &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Austria                                          | 2010 | 8:31 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Belgium                                          | 2010 | 8:36 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Germany (until 1990 former territory of the FRG) | 2010 | 8:26 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Estonia                                          | 2010 | 8:50 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Greece                                           | 2010 | 8:36 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Spain                                            | 2010 | 8:38 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Finland                                          | 2010 | 8:35 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | France                                           | 2010 | 8:30 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Hungary                                          | 2010 | 8:34 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Italy                                            | 2010 | 8:32 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Luxembourg                                       | 2010 | 8:40 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Netherlands                                      | 2010 | 8:26 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Norway                                           | 2010 | 8:07 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Poland                                           | 2010 | 8:37 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Romania                                          | 2010 | 8:52 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Serbia                                           | 2010 | 8:21 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | Turkey                                           | 2010 | 8:42 |\n",
       "| Time spent (hh:mm) | Total | Total | Sleep  | United Kingdom                                   | 2010 | 8:22 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Austria                                          | 2010 | 1:24 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Belgium                                          | 2010 | 1:44 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Germany (until 1990 former territory of the FRG) | 2010 | 1:43 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Estonia                                          | 2010 | 1:21 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Greece                                           | 2010 | 2:13 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Spain                                            | 2010 | 1:59 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Finland                                          | 2010 | 1:23 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | France                                           | 2010 | 2:12 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Hungary                                          | 2010 | 1:42 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Italy                                            | 2010 | 1:57 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Luxembourg                                       | 2010 | 1:54 |\n",
       "| Time spent (hh:mm) | Total | Total | Eating | Netherlands                                      | 2010 | 1:57 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Finland                                          | 2010 | 6:05 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | France                                           | 2010 | 5:03 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Hungary                                          | 2010 | 5:00 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Italy                                            | 2010 | 4:54 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Luxembourg                                       | 2010 | 4:35 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Netherlands                                      | 2010 | 5:34 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Norway                                           | 2010 | 5:53 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Poland                                           | 2010 | 5:02 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Romania                                          | 2010 | 4:26 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Serbia                                           | 2010 | 5:29 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | Turkey                                           | 2010 | 5:27 |\n",
       "| Time spent (hh:mm) | Total | Total | Leisure, social and associative life | United Kingdom                                   | 2010 | 5:15 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Austria                                          | 2010 | 1:09 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Belgium                                          | 2010 | 1:20 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Germany (until 1990 former territory of the FRG) | 2010 | 1:17 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Estonia                                          | 2010 | 1:16 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Greece                                           | 2010 | 1:02 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Spain                                            | 2010 | 1:10 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Finland                                          | 2010 | 1:22 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | France                                           | 2010 | 1:23 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Hungary                                          | 2010 | 1:03 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Italy                                            | 2010 | 1:19 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Luxembourg                                       | 2010 | 1:32 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Netherlands                                      | 2010 | 1:31 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Norway                                           | 2010 | 1:26 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Poland                                           | 2010 | 1:06 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Romania                                          | 2010 | 0:58 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Serbia                                           | 2010 | 1:07 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | Turkey                                           | 2010 | 1:04 |\n",
       "| Time spent (hh:mm) | Total | Total | Travel except travel related to jobs | United Kingdom                                   | 2010 | 2:09 |\n",
       "\n"
      ],
      "text/plain": [
       "       unit               sex    age    acl00                               \n",
       "1      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "2      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "3      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "4      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "5      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "6      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "7      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "8      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "9      Time spent (hh:mm) Total  Total  Sleep                               \n",
       "10     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "11     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "12     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "13     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "14     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "15     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "16     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "17     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "18     Time spent (hh:mm) Total  Total  Sleep                               \n",
       "19     Time spent (hh:mm) Total  Total  Eating                              \n",
       "20     Time spent (hh:mm) Total  Total  Eating                              \n",
       "21     Time spent (hh:mm) Total  Total  Eating                              \n",
       "22     Time spent (hh:mm) Total  Total  Eating                              \n",
       "23     Time spent (hh:mm) Total  Total  Eating                              \n",
       "24     Time spent (hh:mm) Total  Total  Eating                              \n",
       "25     Time spent (hh:mm) Total  Total  Eating                              \n",
       "26     Time spent (hh:mm) Total  Total  Eating                              \n",
       "27     Time spent (hh:mm) Total  Total  Eating                              \n",
       "28     Time spent (hh:mm) Total  Total  Eating                              \n",
       "29     Time spent (hh:mm) Total  Total  Eating                              \n",
       "30     Time spent (hh:mm) Total  Total  Eating                              \n",
       "<U+22EE> <U+22EE>           <U+22EE> <U+22EE> <U+22EE>                            \n",
       "115    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "116    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "117    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "118    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "119    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "120    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "121    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "122    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "123    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "124    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "125    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "126    Time spent (hh:mm) Total  Total  Leisure, social and associative life\n",
       "127    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "128    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "129    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "130    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "131    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "132    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "133    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "134    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "135    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "136    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "137    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "138    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "139    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "140    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "141    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "142    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "143    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "144    Time spent (hh:mm) Total  Total  Travel except travel related to jobs\n",
       "       geo                                              time   values\n",
       "1      Austria                                          2010   8:31  \n",
       "2      Belgium                                          2010   8:36  \n",
       "3      Germany (until 1990 former territory of the FRG) 2010   8:26  \n",
       "4      Estonia                                          2010   8:50  \n",
       "5      Greece                                           2010   8:36  \n",
       "6      Spain                                            2010   8:38  \n",
       "7      Finland                                          2010   8:35  \n",
       "8      France                                           2010   8:30  \n",
       "9      Hungary                                          2010   8:34  \n",
       "10     Italy                                            2010   8:32  \n",
       "11     Luxembourg                                       2010   8:40  \n",
       "12     Netherlands                                      2010   8:26  \n",
       "13     Norway                                           2010   8:07  \n",
       "14     Poland                                           2010   8:37  \n",
       "15     Romania                                          2010   8:52  \n",
       "16     Serbia                                           2010   8:21  \n",
       "17     Turkey                                           2010   8:42  \n",
       "18     United Kingdom                                   2010   8:22  \n",
       "19     Austria                                          2010   1:24  \n",
       "20     Belgium                                          2010   1:44  \n",
       "21     Germany (until 1990 former territory of the FRG) 2010   1:43  \n",
       "22     Estonia                                          2010   1:21  \n",
       "23     Greece                                           2010   2:13  \n",
       "24     Spain                                            2010   1:59  \n",
       "25     Finland                                          2010   1:23  \n",
       "26     France                                           2010   2:12  \n",
       "27     Hungary                                          2010   1:42  \n",
       "28     Italy                                            2010   1:57  \n",
       "29     Luxembourg                                       2010   1:54  \n",
       "30     Netherlands                                      2010   1:57  \n",
       "<U+22EE> <U+22EE>                                         <U+22EE> <U+22EE>\n",
       "115    Finland                                          2010   6:05  \n",
       "116    France                                           2010   5:03  \n",
       "117    Hungary                                          2010   5:00  \n",
       "118    Italy                                            2010   4:54  \n",
       "119    Luxembourg                                       2010   4:35  \n",
       "120    Netherlands                                      2010   5:34  \n",
       "121    Norway                                           2010   5:53  \n",
       "122    Poland                                           2010   5:02  \n",
       "123    Romania                                          2010   4:26  \n",
       "124    Serbia                                           2010   5:29  \n",
       "125    Turkey                                           2010   5:27  \n",
       "126    United Kingdom                                   2010   5:15  \n",
       "127    Austria                                          2010   1:09  \n",
       "128    Belgium                                          2010   1:20  \n",
       "129    Germany (until 1990 former territory of the FRG) 2010   1:17  \n",
       "130    Estonia                                          2010   1:16  \n",
       "131    Greece                                           2010   1:02  \n",
       "132    Spain                                            2010   1:10  \n",
       "133    Finland                                          2010   1:22  \n",
       "134    France                                           2010   1:23  \n",
       "135    Hungary                                          2010   1:03  \n",
       "136    Italy                                            2010   1:19  \n",
       "137    Luxembourg                                       2010   1:32  \n",
       "138    Netherlands                                      2010   1:31  \n",
       "139    Norway                                           2010   1:26  \n",
       "140    Poland                                           2010   1:06  \n",
       "141    Romania                                          2010   0:58  \n",
       "142    Serbia                                           2010   1:07  \n",
       "143    Turkey                                           2010   1:04  \n",
       "144    United Kingdom                                   2010   2:09  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dt<-get_eurostat_data(\"tus_00age\",filters=list(unit=\"spent\",age=\"total\",sex=\"total\",acl00=\"^(?!total).*$\",geo=\"be\"),date_filter=2010,label=F,ignore.case=T,exact_match=F,force_local_filter=T,perl=T)\n",
    "#dt<-get_eurostat_data(\"tus_00age\",filters=list(unit=\"spent\",age=\"total\",sex=\"total\",acl00=c(\"ac01\",\"ac02\",\"ac03\",\"ac1a\",\"ac1b\",\"ac2$\",\"ac3$\",\"ac4-8$\",\"ac9a\"),geo=\"be\"),date_filter=2010,label=T,ignore.case=T,exact_match=F,force_local_filter=T,perl=T)\n",
    "#dt<-get_eurostat_data(\"tus_00age\",filters=list(unit=\"spent\",age=\"total\",sex=\"total\",geo=\"be\"),date_filter=2010,label=T,ignore.case=T,exact_match=F,force_local_filter=T,perl=T)\n",
    "dt<-get_eurostat_data(\"tus_00age\",filters=list(unit=\"spent\",age=\"total\",sex=\"total\",acl00=c(\"sleep\",\"eat\",\"^employ\",\" (family|personal) care\",\"^leisure\",\"^study\",\"except travel\")),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T)\n",
    "dt\n",
    "dt<-get_eurostat_data(\"tus_00age\",filters=list(unit=\"spent\",age=\"total\",sex=\"total\",acl00=c(\"sleep\",\"eat\",\"^employ\",\" (family|personal) care\",\"^leisure\",\"^study\",\"except travel\")),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we convert the values from characters/factors to time values using the *chron* package and keep only the columns with activities, countries and values. Before plotting the values, we need to sum the eating and other personal care for each country and cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 18 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>acl00</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td>Eating and other personal care</td><td>02:14:00</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>Eating and other personal care</td><td>02:37:00</td></tr>\n",
       "\t<tr><td>Germany       </td><td>Eating and other personal care</td><td>02:38:00</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>Eating and other personal care</td><td>02:16:00</td></tr>\n",
       "\t<tr><td>Greece        </td><td>Eating and other personal care</td><td>03:10:00</td></tr>\n",
       "\t<tr><td>Spain         </td><td>Eating and other personal care</td><td>02:50:00</td></tr>\n",
       "\t<tr><td>Finland       </td><td>Eating and other personal care</td><td>02:12:00</td></tr>\n",
       "\t<tr><td>France        </td><td>Eating and other personal care</td><td>03:05:00</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>Eating and other personal care</td><td>02:58:00</td></tr>\n",
       "\t<tr><td>Italy         </td><td>Eating and other personal care</td><td>02:52:00</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>Eating and other personal care</td><td>02:50:00</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>Eating and other personal care</td><td>02:50:00</td></tr>\n",
       "\t<tr><td>Norway        </td><td>Eating and other personal care</td><td>02:20:00</td></tr>\n",
       "\t<tr><td>Poland        </td><td>Eating and other personal care</td><td>02:33:00</td></tr>\n",
       "\t<tr><td>Romania       </td><td>Eating and other personal care</td><td>02:58:00</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>Eating and other personal care</td><td>02:48:00</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>Eating and other personal care</td><td>02:46:00</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>Eating and other personal care</td><td>02:21:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 18 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & acl00 & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Austria        & Eating and other personal care & 02:14:00\\\\\n",
       "\t Belgium        & Eating and other personal care & 02:37:00\\\\\n",
       "\t Germany        & Eating and other personal care & 02:38:00\\\\\n",
       "\t Estonia        & Eating and other personal care & 02:16:00\\\\\n",
       "\t Greece         & Eating and other personal care & 03:10:00\\\\\n",
       "\t Spain          & Eating and other personal care & 02:50:00\\\\\n",
       "\t Finland        & Eating and other personal care & 02:12:00\\\\\n",
       "\t France         & Eating and other personal care & 03:05:00\\\\\n",
       "\t Hungary        & Eating and other personal care & 02:58:00\\\\\n",
       "\t Italy          & Eating and other personal care & 02:52:00\\\\\n",
       "\t Luxembourg     & Eating and other personal care & 02:50:00\\\\\n",
       "\t Netherlands    & Eating and other personal care & 02:50:00\\\\\n",
       "\t Norway         & Eating and other personal care & 02:20:00\\\\\n",
       "\t Poland         & Eating and other personal care & 02:33:00\\\\\n",
       "\t Romania        & Eating and other personal care & 02:58:00\\\\\n",
       "\t Serbia         & Eating and other personal care & 02:48:00\\\\\n",
       "\t Turkey         & Eating and other personal care & 02:46:00\\\\\n",
       "\t United Kingdom & Eating and other personal care & 02:21:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 18 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | acl00 &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Austria        | Eating and other personal care | 02:14:00 |\n",
       "| Belgium        | Eating and other personal care | 02:37:00 |\n",
       "| Germany        | Eating and other personal care | 02:38:00 |\n",
       "| Estonia        | Eating and other personal care | 02:16:00 |\n",
       "| Greece         | Eating and other personal care | 03:10:00 |\n",
       "| Spain          | Eating and other personal care | 02:50:00 |\n",
       "| Finland        | Eating and other personal care | 02:12:00 |\n",
       "| France         | Eating and other personal care | 03:05:00 |\n",
       "| Hungary        | Eating and other personal care | 02:58:00 |\n",
       "| Italy          | Eating and other personal care | 02:52:00 |\n",
       "| Luxembourg     | Eating and other personal care | 02:50:00 |\n",
       "| Netherlands    | Eating and other personal care | 02:50:00 |\n",
       "| Norway         | Eating and other personal care | 02:20:00 |\n",
       "| Poland         | Eating and other personal care | 02:33:00 |\n",
       "| Romania        | Eating and other personal care | 02:58:00 |\n",
       "| Serbia         | Eating and other personal care | 02:48:00 |\n",
       "| Turkey         | Eating and other personal care | 02:46:00 |\n",
       "| United Kingdom | Eating and other personal care | 02:21:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   geo            acl00                          values  \n",
       "1  Austria        Eating and other personal care 02:14:00\n",
       "2  Belgium        Eating and other personal care 02:37:00\n",
       "3  Germany        Eating and other personal care 02:38:00\n",
       "4  Estonia        Eating and other personal care 02:16:00\n",
       "5  Greece         Eating and other personal care 03:10:00\n",
       "6  Spain          Eating and other personal care 02:50:00\n",
       "7  Finland        Eating and other personal care 02:12:00\n",
       "8  France         Eating and other personal care 03:05:00\n",
       "9  Hungary        Eating and other personal care 02:58:00\n",
       "10 Italy          Eating and other personal care 02:52:00\n",
       "11 Luxembourg     Eating and other personal care 02:50:00\n",
       "12 Netherlands    Eating and other personal care 02:50:00\n",
       "13 Norway         Eating and other personal care 02:20:00\n",
       "14 Poland         Eating and other personal care 02:33:00\n",
       "15 Romania        Eating and other personal care 02:58:00\n",
       "16 Serbia         Eating and other personal care 02:48:00\n",
       "17 Turkey         Eating and other personal care 02:46:00\n",
       "18 United Kingdom Eating and other personal care 02:21:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))]\n",
    "dt<-dt[,c(\"acl00\",\"geo\",\"values\")]\n",
    "sdt<-dt[grepl(\"(ating|ther)\",acl00),.(acl00=\"Eating and other personal care\",values=sum(values)),by=geo]\n",
    "sdt\n",
    "dt<-rbind(dt[!grepl(\"(ating|ther)\",acl00)],sdt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then plot the data using the *ggplot2* library, and using the color codes of the original figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CIIdgAAABJBsAMAAJAIgh0AAIBEEOwA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JAAAAJRyCHd24cUP14fHtm0REpC+k6FvhmQCdcz4AABpKSURBVK61y5OaYc2MlAfb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\nAAAgEwQ7AAAAmSDYAQAAyMT/Aanu3NkeaVPLAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1_colors<-c(\"#F6A26B\",\"#F06423\",\"#71A8DF\",\"#286EB4\",\"#FDDBA3\",\"#FCC975\",\"#FAA519\")\n",
    "ggplot(dt, aes(x=geo, y=values,fill=acl00)) + \n",
    "  geom_bar(position=\"stack\",stat=\"identity\")+\n",
    "  scale_y_chron(format=\"%H:%M\") +\n",
    "  scale_fill_manual(values = fig1_colors)+\n",
    "  ggtitle(\"Figure 1\") +\n",
    "  ylab (\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(axis.text.x = element_text(angle = 90, hjust = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph not exactly as in the SE article, because in our case it is sorted alphabetically. We have to add the empty spaces before the EFTA and accession countries and reorder the values. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (position_stack).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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S4IpfS+3qGOpDOwIkx2L4oXw9ptDXGUFSmKZgUpdLlaX56k127FjrSuZnM5RXrg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\nmf9jQCxxs9OgispCyjN3G098nWpe4jG1N3A6OSe5iQeJTwKZl5u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tGP2qVO1917oS7j+/b94k+Xx26j3JZy7PbFvvilr8XkRnyYBczAuS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1/05LQQQl6Tng/e3b+mX35DF4njWzL++Xv/GT3u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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_sep<-data.table::data.table(acl00=c(\"Sleep\",\"Sleep\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "acls_ord<-c('Travel except travel related to jobs','Leisure, social and associative life','Household and family care','Study','Employment, related activities and travel as part of/during main and second job','Eating and other personal care','Sleep')\n",
    "dt$acl00<-factor(dt$acl00,levels=acls_ord)\n",
    "geo_ord<-c('Belgium','Germany','Estonia','Greece','Spain','France','Italy','Luxembourg','Hungary','Netherlands','Austria','Poland','Romania','Finland','United Kingdom',' ','Norway','  ','Serbia','Turkey')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt, aes(x=geo, y=values,fill=acl00)) + theme_minimal() +\n",
    "  geom_bar(position=\"stack\",stat=\"identity\",width=0.5)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,4/24)) +\n",
    "  scale_fill_manual(values = fig1_colors)+\n",
    "  ggtitle(\"Figure 1: Mean time spent on daily activities, all individuals by country, (hh:mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(), \n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 2: Participation time per day in study and employment, only individuals taking part in the activity, by country, (hh mm; 2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00age* dataset. We use the same method as for Figure 1. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation time\",age=\"total\",sex=\"total\",acl00=c(\"^study\",\"^empl\"))`. Again in order to get the data we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Austria                                         </td><td>2010</td><td>7:45</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Belgium                                         </td><td>2010</td><td>7:24</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>7:02</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Estonia                                         </td><td>2010</td><td>7:52</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Greece                                          </td><td>2010</td><td>7:20</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Spain                                           </td><td>2010</td><td>7:24</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Finland                                         </td><td>2010</td><td>7:17</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>France                                          </td><td>2010</td><td>6:51</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Hungary                                         </td><td>2010</td><td>7:12</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Italy                                           </td><td>2010</td><td>7:33</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Luxembourg                                      </td><td>2010</td><td>7:29</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Netherlands                                     </td><td>2010</td><td>6:42</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Norway                                          </td><td>2010</td><td>6:59</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Poland                                          </td><td>2010</td><td>7:38</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Romania                                         </td><td>2010</td><td>7:08</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Serbia                                          </td><td>2010</td><td>6:44</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>Turkey                                          </td><td>2010</td><td>7:41</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Employment, related activities and travel as part of/during main and second job</td><td>United Kingdom                                  </td><td>2010</td><td>6:54</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Austria                                         </td><td>2010</td><td>5:11</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Belgium                                         </td><td>2010</td><td>5:30</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>4:14</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Estonia                                         </td><td>2010</td><td>5:02</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Greece                                          </td><td>2010</td><td>6:13</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Spain                                           </td><td>2010</td><td>4:48</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Finland                                         </td><td>2010</td><td>4:24</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>France                                          </td><td>2010</td><td>5:16</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Hungary                                         </td><td>2010</td><td>5:14</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Italy                                           </td><td>2010</td><td>5:41</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Luxembourg                                      </td><td>2010</td><td>5:23</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Netherlands                                     </td><td>2010</td><td>4:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Norway                                          </td><td>2010</td><td>4:44</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Poland                                          </td><td>2010</td><td>5:13</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Romania                                         </td><td>2010</td><td>5:44</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Serbia                                          </td><td>2010</td><td>5:05</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>Turkey                                          </td><td>2010</td><td>4:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>Total</td><td>Study                                                                          </td><td>United Kingdom                                  </td><td>2010</td><td>4:36</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & age & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Austria                                          & 2010 & 7:45\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Belgium                                          & 2010 & 7:24\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Germany (until 1990 former territory of the FRG) & 2010 & 7:02\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Estonia                                          & 2010 & 7:52\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Greece                                           & 2010 & 7:20\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Spain                                            & 2010 & 7:24\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Finland                                          & 2010 & 7:17\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & France                                           & 2010 & 6:51\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Hungary                                          & 2010 & 7:12\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Italy                                            & 2010 & 7:33\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Luxembourg                                       & 2010 & 7:29\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Netherlands                                      & 2010 & 6:42\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Norway                                           & 2010 & 6:59\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Poland                                           & 2010 & 7:38\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Romania                                          & 2010 & 7:08\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Serbia                                           & 2010 & 6:44\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & Turkey                                           & 2010 & 7:41\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Employment, related activities and travel as part of/during main and second job & United Kingdom                                   & 2010 & 6:54\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Austria                                          & 2010 & 5:11\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Belgium                                          & 2010 & 5:30\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Germany (until 1990 former territory of the FRG) & 2010 & 4:14\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Estonia                                          & 2010 & 5:02\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Greece                                           & 2010 & 6:13\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Spain                                            & 2010 & 4:48\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Finland                                          & 2010 & 4:24\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & France                                           & 2010 & 5:16\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Hungary                                          & 2010 & 5:14\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Italy                                            & 2010 & 5:41\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Luxembourg                                       & 2010 & 5:23\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Netherlands                                      & 2010 & 4:00\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Norway                                           & 2010 & 4:44\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Poland                                           & 2010 & 5:13\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Romania                                          & 2010 & 5:44\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Serbia                                           & 2010 & 5:05\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & Turkey                                           & 2010 & 4:46\\\\\n",
       "\t Participation time (hh:mm) & Total & Total & Study                                                                           & United Kingdom                                   & 2010 & 4:36\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | age &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Austria                                          | 2010 | 7:45 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Belgium                                          | 2010 | 7:24 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Germany (until 1990 former territory of the FRG) | 2010 | 7:02 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Estonia                                          | 2010 | 7:52 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Greece                                           | 2010 | 7:20 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Spain                                            | 2010 | 7:24 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Finland                                          | 2010 | 7:17 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | France                                           | 2010 | 6:51 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Hungary                                          | 2010 | 7:12 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Italy                                            | 2010 | 7:33 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Luxembourg                                       | 2010 | 7:29 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Netherlands                                      | 2010 | 6:42 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Norway                                           | 2010 | 6:59 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Poland                                           | 2010 | 7:38 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Romania                                          | 2010 | 7:08 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Serbia                                           | 2010 | 6:44 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | Turkey                                           | 2010 | 7:41 |\n",
       "| Participation time (hh:mm) | Total | Total | Employment, related activities and travel as part of/during main and second job | United Kingdom                                   | 2010 | 6:54 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Austria                                          | 2010 | 5:11 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Belgium                                          | 2010 | 5:30 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Germany (until 1990 former territory of the FRG) | 2010 | 4:14 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Estonia                                          | 2010 | 5:02 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Greece                                           | 2010 | 6:13 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Spain                                            | 2010 | 4:48 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Finland                                          | 2010 | 4:24 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | France                                           | 2010 | 5:16 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Hungary                                          | 2010 | 5:14 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Italy                                            | 2010 | 5:41 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Luxembourg                                       | 2010 | 5:23 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Netherlands                                      | 2010 | 4:00 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Norway                                           | 2010 | 4:44 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Poland                                           | 2010 | 5:13 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Romania                                          | 2010 | 5:44 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Serbia                                           | 2010 | 5:05 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | Turkey                                           | 2010 | 4:46 |\n",
       "| Participation time (hh:mm) | Total | Total | Study                                                                           | United Kingdom                                   | 2010 | 4:36 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                       sex   age  \n",
       "1  Participation time (hh:mm) Total Total\n",
       "2  Participation time (hh:mm) Total Total\n",
       "3  Participation time (hh:mm) Total Total\n",
       "4  Participation time (hh:mm) Total Total\n",
       "5  Participation time (hh:mm) Total Total\n",
       "6  Participation time (hh:mm) Total Total\n",
       "7  Participation time (hh:mm) Total Total\n",
       "8  Participation time (hh:mm) Total Total\n",
       "9  Participation time (hh:mm) Total Total\n",
       "10 Participation time (hh:mm) Total Total\n",
       "11 Participation time (hh:mm) Total Total\n",
       "12 Participation time (hh:mm) Total Total\n",
       "13 Participation time (hh:mm) Total Total\n",
       "14 Participation time (hh:mm) Total Total\n",
       "15 Participation time (hh:mm) Total Total\n",
       "16 Participation time (hh:mm) Total Total\n",
       "17 Participation time (hh:mm) Total Total\n",
       "18 Participation time (hh:mm) Total Total\n",
       "19 Participation time (hh:mm) Total Total\n",
       "20 Participation time (hh:mm) Total Total\n",
       "21 Participation time (hh:mm) Total Total\n",
       "22 Participation time (hh:mm) Total Total\n",
       "23 Participation time (hh:mm) Total Total\n",
       "24 Participation time (hh:mm) Total Total\n",
       "25 Participation time (hh:mm) Total Total\n",
       "26 Participation time (hh:mm) Total Total\n",
       "27 Participation time (hh:mm) Total Total\n",
       "28 Participation time (hh:mm) Total Total\n",
       "29 Participation time (hh:mm) Total Total\n",
       "30 Participation time (hh:mm) Total Total\n",
       "31 Participation time (hh:mm) Total Total\n",
       "32 Participation time (hh:mm) Total Total\n",
       "33 Participation time (hh:mm) Total Total\n",
       "34 Participation time (hh:mm) Total Total\n",
       "35 Participation time (hh:mm) Total Total\n",
       "36 Participation time (hh:mm) Total Total\n",
       "   acl00                                                                          \n",
       "1  Employment, related activities and travel as part of/during main and second job\n",
       "2  Employment, related activities and travel as part of/during main and second job\n",
       "3  Employment, related activities and travel as part of/during main and second job\n",
       "4  Employment, related activities and travel as part of/during main and second job\n",
       "5  Employment, related activities and travel as part of/during main and second job\n",
       "6  Employment, related activities and travel as part of/during main and second job\n",
       "7  Employment, related activities and travel as part of/during main and second job\n",
       "8  Employment, related activities and travel as part of/during main and second job\n",
       "9  Employment, related activities and travel as part of/during main and second job\n",
       "10 Employment, related activities and travel as part of/during main and second job\n",
       "11 Employment, related activities and travel as part of/during main and second job\n",
       "12 Employment, related activities and travel as part of/during main and second job\n",
       "13 Employment, related activities and travel as part of/during main and second job\n",
       "14 Employment, related activities and travel as part of/during main and second job\n",
       "15 Employment, related activities and travel as part of/during main and second job\n",
       "16 Employment, related activities and travel as part of/during main and second job\n",
       "17 Employment, related activities and travel as part of/during main and second job\n",
       "18 Employment, related activities and travel as part of/during main and second job\n",
       "19 Study                                                                          \n",
       "20 Study                                                                          \n",
       "21 Study                                                                          \n",
       "22 Study                                                                          \n",
       "23 Study                                                                          \n",
       "24 Study                                                                          \n",
       "25 Study                                                                          \n",
       "26 Study                                                                          \n",
       "27 Study                                                                          \n",
       "28 Study                                                                          \n",
       "29 Study                                                                          \n",
       "30 Study                                                                          \n",
       "31 Study                                                                          \n",
       "32 Study                                                                          \n",
       "33 Study                                                                          \n",
       "34 Study                                                                          \n",
       "35 Study                                                                          \n",
       "36 Study                                                                          \n",
       "   geo                                              time values\n",
       "1  Austria                                          2010 7:45  \n",
       "2  Belgium                                          2010 7:24  \n",
       "3  Germany (until 1990 former territory of the FRG) 2010 7:02  \n",
       "4  Estonia                                          2010 7:52  \n",
       "5  Greece                                           2010 7:20  \n",
       "6  Spain                                            2010 7:24  \n",
       "7  Finland                                          2010 7:17  \n",
       "8  France                                           2010 6:51  \n",
       "9  Hungary                                          2010 7:12  \n",
       "10 Italy                                            2010 7:33  \n",
       "11 Luxembourg                                       2010 7:29  \n",
       "12 Netherlands                                      2010 6:42  \n",
       "13 Norway                                           2010 6:59  \n",
       "14 Poland                                           2010 7:38  \n",
       "15 Romania                                          2010 7:08  \n",
       "16 Serbia                                           2010 6:44  \n",
       "17 Turkey                                           2010 7:41  \n",
       "18 United Kingdom                                   2010 6:54  \n",
       "19 Austria                                          2010 5:11  \n",
       "20 Belgium                                          2010 5:30  \n",
       "21 Germany (until 1990 former territory of the FRG) 2010 4:14  \n",
       "22 Estonia                                          2010 5:02  \n",
       "23 Greece                                           2010 6:13  \n",
       "24 Spain                                            2010 4:48  \n",
       "25 Finland                                          2010 4:24  \n",
       "26 France                                           2010 5:16  \n",
       "27 Hungary                                          2010 5:14  \n",
       "28 Italy                                            2010 5:41  \n",
       "29 Luxembourg                                       2010 5:23  \n",
       "30 Netherlands                                      2010 4:00  \n",
       "31 Norway                                           2010 4:44  \n",
       "32 Poland                                           2010 5:13  \n",
       "33 Romania                                          2010 5:44  \n",
       "34 Serbia                                           2010 5:05  \n",
       "35 Turkey                                           2010 4:46  \n",
       "36 United Kingdom                                   2010 4:36  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00age\",filters=list(unit=\"Participation time\",age=\"total\",sex=\"total\",acl00=c(\"^study\",\"^empl\")),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we convert the values from characters/factors to time values using the *chron* package and keep only the columns with activities, countries and values. Before plotting the values we need to cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Austria       </td><td>07:45:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Belgium       </td><td>07:24:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Germany       </td><td>07:02:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Estonia       </td><td>07:52:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Greece        </td><td>07:20:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Spain         </td><td>07:24:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Finland       </td><td>07:17:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>France        </td><td>06:51:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Hungary       </td><td>07:12:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Italy         </td><td>07:33:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Luxembourg    </td><td>07:29:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Netherlands   </td><td>06:42:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Norway        </td><td>06:59:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Poland        </td><td>07:38:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Romania       </td><td>07:08:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Serbia        </td><td>06:44:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>Turkey        </td><td>07:41:00</td></tr>\n",
       "\t<tr><td>Employment, related activities and travel as part of/during main and second job</td><td>United Kingdom</td><td>06:54:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Austria       </td><td>05:11:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Belgium       </td><td>05:30:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Germany       </td><td>04:14:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Estonia       </td><td>05:02:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Greece        </td><td>06:13:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Spain         </td><td>04:48:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Finland       </td><td>04:24:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>France        </td><td>05:16:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Hungary       </td><td>05:14:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Italy         </td><td>05:41:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Luxembourg    </td><td>05:23:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Netherlands   </td><td>04:00:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Norway        </td><td>04:44:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Poland        </td><td>05:13:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Romania       </td><td>05:44:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Serbia        </td><td>05:05:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>Turkey        </td><td>04:46:00</td></tr>\n",
       "\t<tr><td>Study                                                                          </td><td>United Kingdom</td><td>04:36:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 3\n",
       "\\begin{tabular}{lll}\n",
       " acl00 & geo & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Employment, related activities and travel as part of/during main and second job & Austria        & 07:45:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Belgium        & 07:24:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Germany        & 07:02:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Estonia        & 07:52:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Greece         & 07:20:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Spain          & 07:24:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Finland        & 07:17:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & France         & 06:51:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Hungary        & 07:12:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Italy          & 07:33:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Luxembourg     & 07:29:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Netherlands    & 06:42:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Norway         & 06:59:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Poland         & 07:38:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Romania        & 07:08:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Serbia         & 06:44:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & Turkey         & 07:41:00\\\\\n",
       "\t Employment, related activities and travel as part of/during main and second job & United Kingdom & 06:54:00\\\\\n",
       "\t Study                                                                           & Austria        & 05:11:00\\\\\n",
       "\t Study                                                                           & Belgium        & 05:30:00\\\\\n",
       "\t Study                                                                           & Germany        & 04:14:00\\\\\n",
       "\t Study                                                                           & Estonia        & 05:02:00\\\\\n",
       "\t Study                                                                           & Greece         & 06:13:00\\\\\n",
       "\t Study                                                                           & Spain          & 04:48:00\\\\\n",
       "\t Study                                                                           & Finland        & 04:24:00\\\\\n",
       "\t Study                                                                           & France         & 05:16:00\\\\\n",
       "\t Study                                                                           & Hungary        & 05:14:00\\\\\n",
       "\t Study                                                                           & Italy          & 05:41:00\\\\\n",
       "\t Study                                                                           & Luxembourg     & 05:23:00\\\\\n",
       "\t Study                                                                           & Netherlands    & 04:00:00\\\\\n",
       "\t Study                                                                           & Norway         & 04:44:00\\\\\n",
       "\t Study                                                                           & Poland         & 05:13:00\\\\\n",
       "\t Study                                                                           & Romania        & 05:44:00\\\\\n",
       "\t Study                                                                           & Serbia         & 05:05:00\\\\\n",
       "\t Study                                                                           & Turkey         & 04:46:00\\\\\n",
       "\t Study                                                                           & United Kingdom & 04:36:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 3\n",
       "\n",
       "| acl00 &lt;chr&gt; | geo &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Employment, related activities and travel as part of/during main and second job | Austria        | 07:45:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Belgium        | 07:24:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Germany        | 07:02:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Estonia        | 07:52:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Greece         | 07:20:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Spain          | 07:24:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Finland        | 07:17:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | France         | 06:51:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Hungary        | 07:12:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Italy          | 07:33:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Luxembourg     | 07:29:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Netherlands    | 06:42:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Norway         | 06:59:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Poland         | 07:38:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Romania        | 07:08:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Serbia         | 06:44:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | Turkey         | 07:41:00 |\n",
       "| Employment, related activities and travel as part of/during main and second job | United Kingdom | 06:54:00 |\n",
       "| Study                                                                           | Austria        | 05:11:00 |\n",
       "| Study                                                                           | Belgium        | 05:30:00 |\n",
       "| Study                                                                           | Germany        | 04:14:00 |\n",
       "| Study                                                                           | Estonia        | 05:02:00 |\n",
       "| Study                                                                           | Greece         | 06:13:00 |\n",
       "| Study                                                                           | Spain          | 04:48:00 |\n",
       "| Study                                                                           | Finland        | 04:24:00 |\n",
       "| Study                                                                           | France         | 05:16:00 |\n",
       "| Study                                                                           | Hungary        | 05:14:00 |\n",
       "| Study                                                                           | Italy          | 05:41:00 |\n",
       "| Study                                                                           | Luxembourg     | 05:23:00 |\n",
       "| Study                                                                           | Netherlands    | 04:00:00 |\n",
       "| Study                                                                           | Norway         | 04:44:00 |\n",
       "| Study                                                                           | Poland         | 05:13:00 |\n",
       "| Study                                                                           | Romania        | 05:44:00 |\n",
       "| Study                                                                           | Serbia         | 05:05:00 |\n",
       "| Study                                                                           | Turkey         | 04:46:00 |\n",
       "| Study                                                                           | United Kingdom | 04:36:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   acl00                                                                          \n",
       "1  Employment, related activities and travel as part of/during main and second job\n",
       "2  Employment, related activities and travel as part of/during main and second job\n",
       "3  Employment, related activities and travel as part of/during main and second job\n",
       "4  Employment, related activities and travel as part of/during main and second job\n",
       "5  Employment, related activities and travel as part of/during main and second job\n",
       "6  Employment, related activities and travel as part of/during main and second job\n",
       "7  Employment, related activities and travel as part of/during main and second job\n",
       "8  Employment, related activities and travel as part of/during main and second job\n",
       "9  Employment, related activities and travel as part of/during main and second job\n",
       "10 Employment, related activities and travel as part of/during main and second job\n",
       "11 Employment, related activities and travel as part of/during main and second job\n",
       "12 Employment, related activities and travel as part of/during main and second job\n",
       "13 Employment, related activities and travel as part of/during main and second job\n",
       "14 Employment, related activities and travel as part of/during main and second job\n",
       "15 Employment, related activities and travel as part of/during main and second job\n",
       "16 Employment, related activities and travel as part of/during main and second job\n",
       "17 Employment, related activities and travel as part of/during main and second job\n",
       "18 Employment, related activities and travel as part of/during main and second job\n",
       "19 Study                                                                          \n",
       "20 Study                                                                          \n",
       "21 Study                                                                          \n",
       "22 Study                                                                          \n",
       "23 Study                                                                          \n",
       "24 Study                                                                          \n",
       "25 Study                                                                          \n",
       "26 Study                                                                          \n",
       "27 Study                                                                          \n",
       "28 Study                                                                          \n",
       "29 Study                                                                          \n",
       "30 Study                                                                          \n",
       "31 Study                                                                          \n",
       "32 Study                                                                          \n",
       "33 Study                                                                          \n",
       "34 Study                                                                          \n",
       "35 Study                                                                          \n",
       "36 Study                                                                          \n",
       "   geo            values  \n",
       "1  Austria        07:45:00\n",
       "2  Belgium        07:24:00\n",
       "3  Germany        07:02:00\n",
       "4  Estonia        07:52:00\n",
       "5  Greece         07:20:00\n",
       "6  Spain          07:24:00\n",
       "7  Finland        07:17:00\n",
       "8  France         06:51:00\n",
       "9  Hungary        07:12:00\n",
       "10 Italy          07:33:00\n",
       "11 Luxembourg     07:29:00\n",
       "12 Netherlands    06:42:00\n",
       "13 Norway         06:59:00\n",
       "14 Poland         07:38:00\n",
       "15 Romania        07:08:00\n",
       "16 Serbia         06:44:00\n",
       "17 Turkey         07:41:00\n",
       "18 United Kingdom 06:54:00\n",
       "19 Austria        05:11:00\n",
       "20 Belgium        05:30:00\n",
       "21 Germany        04:14:00\n",
       "22 Estonia        05:02:00\n",
       "23 Greece         06:13:00\n",
       "24 Spain          04:48:00\n",
       "25 Finland        04:24:00\n",
       "26 France         05:16:00\n",
       "27 Hungary        05:14:00\n",
       "28 Italy          05:41:00\n",
       "29 Luxembourg     05:23:00\n",
       "30 Netherlands    04:00:00\n",
       "31 Norway         04:44:00\n",
       "32 Poland         05:13:00\n",
       "33 Romania        05:44:00\n",
       "34 Serbia         05:05:00\n",
       "35 Turkey         04:46:00\n",
       "36 United Kingdom 04:36:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))]\n",
    "dt<-dt[,c(\"acl00\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Employment*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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zdEuZDSvHoFPMORid+KJ1t/PD3Iy8IYqhnvwKbf9qvWe8VZxfLloH4/zgqxnn9D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\nKlgkfUPkCilzrm0WDID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+EXAuhDJxuzwGT5cwVr6EAqqCyZTF3ip+gkNja4nQUfLftWNehIDBGxipgwudpDjOTT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\nxMFNLcy+OcuKN676cHDziv+g6u1wSAgbS/qf7+gPE2Abkpubm+4IkGq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0myUpJxU+bxp9Zaktr3k/XdK4UU3nzE6q3yQLTszH9mnU9xL5Ey/7B7JlW1hMSGKXsKr3Dz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\nN77+MSnLwZrzv7Pc563St0vhdwMjHxhaPkQIIZmzJv5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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_sep<-data.table::data.table(acl00=c(\"Study\",\"Study\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Empl\",acl00)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia','Turkey')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "fig2_colors<-c(\"#FAA519\",\"#286EB4\")\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt, aes(x=geo, y=values,fill=acl00)) + theme_minimal() +\n",
    "  geom_bar(position=\"dodge\",stat=\"identity\",width=0.7)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,1/24)) +\n",
    "  scale_fill_manual(values = fig2_colors)+\n",
    "  ggtitle(\"Figure 2: Participation time per day in study and employment, only individuals taking part in the activity, by country, (hh mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 3: Participation time per day in household and family care, by gender, (hh mm; 2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00age* dataset. We use the same method as for Figure 1. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation time\",age=\"total\",acl00=\"household.*care\")`. Again in order to get the data we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 54 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Austria                                         </td><td>2010</td><td>4:32</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Belgium                                         </td><td>2010</td><td>3:58</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>3:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Estonia                                         </td><td>2010</td><td>4:05</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Greece                                          </td><td>2010</td><td>4:28</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Spain                                           </td><td>2010</td><td>4:36</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Finland                                         </td><td>2010</td><td>3:41</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>France                                          </td><td>2010</td><td>4:04</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Hungary                                         </td><td>2010</td><td>4:43</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Italy                                           </td><td>2010</td><td>5:09</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Luxembourg                                      </td><td>2010</td><td>3:54</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Netherlands                                     </td><td>2010</td><td>3:29</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Norway                                          </td><td>2010</td><td>3:30</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Poland                                          </td><td>2010</td><td>4:33</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Romania                                         </td><td>2010</td><td>5:02</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Serbia                                          </td><td>2010</td><td>4:48</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>Turkey                                          </td><td>2010</td><td>4:59</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Total</td><td>Household and family care</td><td>United Kingdom                                  </td><td>2010</td><td>3:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Austria                                         </td><td>2010</td><td>2:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Belgium                                         </td><td>2010</td><td>2:42</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>2:35</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Estonia                                         </td><td>2010</td><td>2:52</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Greece                                          </td><td>2010</td><td>2:07</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Spain                                           </td><td>2010</td><td>2:36</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Finland                                         </td><td>2010</td><td>2:32</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>France                                          </td><td>2010</td><td>2:53</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Hungary                                         </td><td>2010</td><td>2:55</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Italy                                           </td><td>2010</td><td>2:22</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Luxembourg                                      </td><td>2010</td><td>2:14</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Netherlands                                     </td><td>2010</td><td>2:27</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Norway                                          </td><td>2010</td><td>2:43</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Poland                                          </td><td>2010</td><td>2:48</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Romania                                         </td><td>2010</td><td>2:45</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Serbia                                          </td><td>2010</td><td>2:33</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>Turkey                                          </td><td>2010</td><td>1:43</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Total</td><td>Household and family care</td><td>United Kingdom                                  </td><td>2010</td><td>2:27</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Austria                                         </td><td>2010</td><td>3:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Belgium                                         </td><td>2010</td><td>3:23</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>3:15</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Estonia                                         </td><td>2010</td><td>3:35</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Greece                                          </td><td>2010</td><td>3:31</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Spain                                           </td><td>2010</td><td>3:43</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Finland                                         </td><td>2010</td><td>3:08</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>France                                          </td><td>2010</td><td>3:33</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Hungary                                         </td><td>2010</td><td>3:56</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Italy                                           </td><td>2010</td><td>4:01</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Luxembourg                                      </td><td>2010</td><td>3:07</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Netherlands                                     </td><td>2010</td><td>2:59</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Norway                                          </td><td>2010</td><td>3:07</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Poland                                          </td><td>2010</td><td>3:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Romania                                         </td><td>2010</td><td>4:03</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Serbia                                          </td><td>2010</td><td>3:51</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>Turkey                                          </td><td>2010</td><td>3:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total  </td><td>Total</td><td>Household and family care</td><td>United Kingdom                                  </td><td>2010</td><td>3:11</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 54 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & age & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Austria                                          & 2010 & 4:32\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Belgium                                          & 2010 & 3:58\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Germany (until 1990 former territory of the FRG) & 2010 & 3:50\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Estonia                                          & 2010 & 4:05\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Greece                                           & 2010 & 4:28\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Spain                                            & 2010 & 4:36\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Finland                                          & 2010 & 3:41\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & France                                           & 2010 & 4:04\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Hungary                                          & 2010 & 4:43\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Italy                                            & 2010 & 5:09\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Luxembourg                                       & 2010 & 3:54\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Netherlands                                      & 2010 & 3:29\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Norway                                           & 2010 & 3:30\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Poland                                           & 2010 & 4:33\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Romania                                          & 2010 & 5:02\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Serbia                                           & 2010 & 4:48\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & Turkey                                           & 2010 & 4:59\\\\\n",
       "\t Participation time (hh:mm) & Females & Total & Household and family care & United Kingdom                                   & 2010 & 3:50\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Austria                                          & 2010 & 2:47\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Belgium                                          & 2010 & 2:42\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Germany (until 1990 former territory of the FRG) & 2010 & 2:35\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Estonia                                          & 2010 & 2:52\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Greece                                           & 2010 & 2:07\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Spain                                            & 2010 & 2:36\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Finland                                          & 2010 & 2:32\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & France                                           & 2010 & 2:53\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Hungary                                          & 2010 & 2:55\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Italy                                            & 2010 & 2:22\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Luxembourg                                       & 2010 & 2:14\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Netherlands                                      & 2010 & 2:27\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Norway                                           & 2010 & 2:43\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Poland                                           & 2010 & 2:48\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Romania                                          & 2010 & 2:45\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Serbia                                           & 2010 & 2:33\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & Turkey                                           & 2010 & 1:43\\\\\n",
       "\t Participation time (hh:mm) & Males   & Total & Household and family care & United Kingdom                                   & 2010 & 2:27\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Austria                                          & 2010 & 3:46\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Belgium                                          & 2010 & 3:23\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Germany (until 1990 former territory of the FRG) & 2010 & 3:15\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Estonia                                          & 2010 & 3:35\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Greece                                           & 2010 & 3:31\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Spain                                            & 2010 & 3:43\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Finland                                          & 2010 & 3:08\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & France                                           & 2010 & 3:33\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Hungary                                          & 2010 & 3:56\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Italy                                            & 2010 & 4:01\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Luxembourg                                       & 2010 & 3:07\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Netherlands                                      & 2010 & 2:59\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Norway                                           & 2010 & 3:07\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Poland                                           & 2010 & 3:46\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Romania                                          & 2010 & 4:03\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Serbia                                           & 2010 & 3:51\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & Turkey                                           & 2010 & 3:50\\\\\n",
       "\t Participation time (hh:mm) & Total   & Total & Household and family care & United Kingdom                                   & 2010 & 3:11\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 54 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | age &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Austria                                          | 2010 | 4:32 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Belgium                                          | 2010 | 3:58 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Germany (until 1990 former territory of the FRG) | 2010 | 3:50 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Estonia                                          | 2010 | 4:05 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Greece                                           | 2010 | 4:28 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Spain                                            | 2010 | 4:36 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Finland                                          | 2010 | 3:41 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | France                                           | 2010 | 4:04 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Hungary                                          | 2010 | 4:43 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Italy                                            | 2010 | 5:09 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Luxembourg                                       | 2010 | 3:54 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Netherlands                                      | 2010 | 3:29 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Norway                                           | 2010 | 3:30 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Poland                                           | 2010 | 4:33 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Romania                                          | 2010 | 5:02 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Serbia                                           | 2010 | 4:48 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | Turkey                                           | 2010 | 4:59 |\n",
       "| Participation time (hh:mm) | Females | Total | Household and family care | United Kingdom                                   | 2010 | 3:50 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Austria                                          | 2010 | 2:47 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Belgium                                          | 2010 | 2:42 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Germany (until 1990 former territory of the FRG) | 2010 | 2:35 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Estonia                                          | 2010 | 2:52 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Greece                                           | 2010 | 2:07 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Spain                                            | 2010 | 2:36 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Finland                                          | 2010 | 2:32 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | France                                           | 2010 | 2:53 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Hungary                                          | 2010 | 2:55 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Italy                                            | 2010 | 2:22 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Luxembourg                                       | 2010 | 2:14 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Netherlands                                      | 2010 | 2:27 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Norway                                           | 2010 | 2:43 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Poland                                           | 2010 | 2:48 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Romania                                          | 2010 | 2:45 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Serbia                                           | 2010 | 2:33 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | Turkey                                           | 2010 | 1:43 |\n",
       "| Participation time (hh:mm) | Males   | Total | Household and family care | United Kingdom                                   | 2010 | 2:27 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Austria                                          | 2010 | 3:46 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Belgium                                          | 2010 | 3:23 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Germany (until 1990 former territory of the FRG) | 2010 | 3:15 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Estonia                                          | 2010 | 3:35 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Greece                                           | 2010 | 3:31 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Spain                                            | 2010 | 3:43 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Finland                                          | 2010 | 3:08 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | France                                           | 2010 | 3:33 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Hungary                                          | 2010 | 3:56 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Italy                                            | 2010 | 4:01 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Luxembourg                                       | 2010 | 3:07 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Netherlands                                      | 2010 | 2:59 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Norway                                           | 2010 | 3:07 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Poland                                           | 2010 | 3:46 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Romania                                          | 2010 | 4:03 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Serbia                                           | 2010 | 3:51 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | Turkey                                           | 2010 | 3:50 |\n",
       "| Participation time (hh:mm) | Total   | Total | Household and family care | United Kingdom                                   | 2010 | 3:11 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                       sex     age   acl00                    \n",
       "1  Participation time (hh:mm) Females Total Household and family care\n",
       "2  Participation time (hh:mm) Females Total Household and family care\n",
       "3  Participation time (hh:mm) Females Total Household and family care\n",
       "4  Participation time (hh:mm) Females Total Household and family care\n",
       "5  Participation time (hh:mm) Females Total Household and family care\n",
       "6  Participation time (hh:mm) Females Total Household and family care\n",
       "7  Participation time (hh:mm) Females Total Household and family care\n",
       "8  Participation time (hh:mm) Females Total Household and family care\n",
       "9  Participation time (hh:mm) Females Total Household and family care\n",
       "10 Participation time (hh:mm) Females Total Household and family care\n",
       "11 Participation time (hh:mm) Females Total Household and family care\n",
       "12 Participation time (hh:mm) Females Total Household and family care\n",
       "13 Participation time (hh:mm) Females Total Household and family care\n",
       "14 Participation time (hh:mm) Females Total Household and family care\n",
       "15 Participation time (hh:mm) Females Total Household and family care\n",
       "16 Participation time (hh:mm) Females Total Household and family care\n",
       "17 Participation time (hh:mm) Females Total Household and family care\n",
       "18 Participation time (hh:mm) Females Total Household and family care\n",
       "19 Participation time (hh:mm) Males   Total Household and family care\n",
       "20 Participation time (hh:mm) Males   Total Household and family care\n",
       "21 Participation time (hh:mm) Males   Total Household and family care\n",
       "22 Participation time (hh:mm) Males   Total Household and family care\n",
       "23 Participation time (hh:mm) Males   Total Household and family care\n",
       "24 Participation time (hh:mm) Males   Total Household and family care\n",
       "25 Participation time (hh:mm) Males   Total Household and family care\n",
       "26 Participation time (hh:mm) Males   Total Household and family care\n",
       "27 Participation time (hh:mm) Males   Total Household and family care\n",
       "28 Participation time (hh:mm) Males   Total Household and family care\n",
       "29 Participation time (hh:mm) Males   Total Household and family care\n",
       "30 Participation time (hh:mm) Males   Total Household and family care\n",
       "31 Participation time (hh:mm) Males   Total Household and family care\n",
       "32 Participation time (hh:mm) Males   Total Household and family care\n",
       "33 Participation time (hh:mm) Males   Total Household and family care\n",
       "34 Participation time (hh:mm) Males   Total Household and family care\n",
       "35 Participation time (hh:mm) Males   Total Household and family care\n",
       "36 Participation time (hh:mm) Males   Total Household and family care\n",
       "37 Participation time (hh:mm) Total   Total Household and family care\n",
       "38 Participation time (hh:mm) Total   Total Household and family care\n",
       "39 Participation time (hh:mm) Total   Total Household and family care\n",
       "40 Participation time (hh:mm) Total   Total Household and family care\n",
       "41 Participation time (hh:mm) Total   Total Household and family care\n",
       "42 Participation time (hh:mm) Total   Total Household and family care\n",
       "43 Participation time (hh:mm) Total   Total Household and family care\n",
       "44 Participation time (hh:mm) Total   Total Household and family care\n",
       "45 Participation time (hh:mm) Total   Total Household and family care\n",
       "46 Participation time (hh:mm) Total   Total Household and family care\n",
       "47 Participation time (hh:mm) Total   Total Household and family care\n",
       "48 Participation time (hh:mm) Total   Total Household and family care\n",
       "49 Participation time (hh:mm) Total   Total Household and family care\n",
       "50 Participation time (hh:mm) Total   Total Household and family care\n",
       "51 Participation time (hh:mm) Total   Total Household and family care\n",
       "52 Participation time (hh:mm) Total   Total Household and family care\n",
       "53 Participation time (hh:mm) Total   Total Household and family care\n",
       "54 Participation time (hh:mm) Total   Total Household and family care\n",
       "   geo                                              time values\n",
       "1  Austria                                          2010 4:32  \n",
       "2  Belgium                                          2010 3:58  \n",
       "3  Germany (until 1990 former territory of the FRG) 2010 3:50  \n",
       "4  Estonia                                          2010 4:05  \n",
       "5  Greece                                           2010 4:28  \n",
       "6  Spain                                            2010 4:36  \n",
       "7  Finland                                          2010 3:41  \n",
       "8  France                                           2010 4:04  \n",
       "9  Hungary                                          2010 4:43  \n",
       "10 Italy                                            2010 5:09  \n",
       "11 Luxembourg                                       2010 3:54  \n",
       "12 Netherlands                                      2010 3:29  \n",
       "13 Norway                                           2010 3:30  \n",
       "14 Poland                                           2010 4:33  \n",
       "15 Romania                                          2010 5:02  \n",
       "16 Serbia                                           2010 4:48  \n",
       "17 Turkey                                           2010 4:59  \n",
       "18 United Kingdom                                   2010 3:50  \n",
       "19 Austria                                          2010 2:47  \n",
       "20 Belgium                                          2010 2:42  \n",
       "21 Germany (until 1990 former territory of the FRG) 2010 2:35  \n",
       "22 Estonia                                          2010 2:52  \n",
       "23 Greece                                           2010 2:07  \n",
       "24 Spain                                            2010 2:36  \n",
       "25 Finland                                          2010 2:32  \n",
       "26 France                                           2010 2:53  \n",
       "27 Hungary                                          2010 2:55  \n",
       "28 Italy                                            2010 2:22  \n",
       "29 Luxembourg                                       2010 2:14  \n",
       "30 Netherlands                                      2010 2:27  \n",
       "31 Norway                                           2010 2:43  \n",
       "32 Poland                                           2010 2:48  \n",
       "33 Romania                                          2010 2:45  \n",
       "34 Serbia                                           2010 2:33  \n",
       "35 Turkey                                           2010 1:43  \n",
       "36 United Kingdom                                   2010 2:27  \n",
       "37 Austria                                          2010 3:46  \n",
       "38 Belgium                                          2010 3:23  \n",
       "39 Germany (until 1990 former territory of the FRG) 2010 3:15  \n",
       "40 Estonia                                          2010 3:35  \n",
       "41 Greece                                           2010 3:31  \n",
       "42 Spain                                            2010 3:43  \n",
       "43 Finland                                          2010 3:08  \n",
       "44 France                                           2010 3:33  \n",
       "45 Hungary                                          2010 3:56  \n",
       "46 Italy                                            2010 4:01  \n",
       "47 Luxembourg                                       2010 3:07  \n",
       "48 Netherlands                                      2010 2:59  \n",
       "49 Norway                                           2010 3:07  \n",
       "50 Poland                                           2010 3:46  \n",
       "51 Romania                                          2010 4:03  \n",
       "52 Serbia                                           2010 3:51  \n",
       "53 Turkey                                           2010 3:50  \n",
       "54 United Kingdom                                   2010 3:11  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00age\",filters=list(unit=\"Participation time\",age=\"total\",acl00=\"household.*care\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we convert the values from characters/factors to time values using the *chron* package and keep only the columns with sex, countries and values. Before plotting the values we need to cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 54 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Females</td><td>Austria       </td><td>04:32:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Belgium       </td><td>03:58:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Germany       </td><td>03:50:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Estonia       </td><td>04:05:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Greece        </td><td>04:28:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Spain         </td><td>04:36:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Finland       </td><td>03:41:00</td></tr>\n",
       "\t<tr><td>Females</td><td>France        </td><td>04:04:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Hungary       </td><td>04:43:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Italy         </td><td>05:09:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Luxembourg    </td><td>03:54:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Netherlands   </td><td>03:29:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Norway        </td><td>03:30:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Poland        </td><td>04:33:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Romania       </td><td>05:02:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Serbia        </td><td>04:48:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Turkey        </td><td>04:59:00</td></tr>\n",
       "\t<tr><td>Females</td><td>United Kingdom</td><td>03:50:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Austria       </td><td>02:47:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Belgium       </td><td>02:42:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Germany       </td><td>02:35:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Estonia       </td><td>02:52:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Greece        </td><td>02:07:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Spain         </td><td>02:36:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Finland       </td><td>02:32:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>France        </td><td>02:53:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Hungary       </td><td>02:55:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Italy         </td><td>02:22:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Luxembourg    </td><td>02:14:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Netherlands   </td><td>02:27:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Norway        </td><td>02:43:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Poland        </td><td>02:48:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Romania       </td><td>02:45:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Serbia        </td><td>02:33:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Turkey        </td><td>01:43:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>United Kingdom</td><td>02:27:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Austria       </td><td>03:46:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Belgium       </td><td>03:23:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Germany       </td><td>03:15:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Estonia       </td><td>03:35:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Greece        </td><td>03:31:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Spain         </td><td>03:43:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Finland       </td><td>03:08:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>France        </td><td>03:33:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Hungary       </td><td>03:56:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Italy         </td><td>04:01:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Luxembourg    </td><td>03:07:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Netherlands   </td><td>02:59:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Norway        </td><td>03:07:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Poland        </td><td>03:46:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Romania       </td><td>04:03:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Serbia        </td><td>03:51:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>Turkey        </td><td>03:50:00</td></tr>\n",
       "\t<tr><td>Total  </td><td>United Kingdom</td><td>03:11:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 54 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Females & Austria        & 04:32:00\\\\\n",
       "\t Females & Belgium        & 03:58:00\\\\\n",
       "\t Females & Germany        & 03:50:00\\\\\n",
       "\t Females & Estonia        & 04:05:00\\\\\n",
       "\t Females & Greece         & 04:28:00\\\\\n",
       "\t Females & Spain          & 04:36:00\\\\\n",
       "\t Females & Finland        & 03:41:00\\\\\n",
       "\t Females & France         & 04:04:00\\\\\n",
       "\t Females & Hungary        & 04:43:00\\\\\n",
       "\t Females & Italy          & 05:09:00\\\\\n",
       "\t Females & Luxembourg     & 03:54:00\\\\\n",
       "\t Females & Netherlands    & 03:29:00\\\\\n",
       "\t Females & Norway         & 03:30:00\\\\\n",
       "\t Females & Poland         & 04:33:00\\\\\n",
       "\t Females & Romania        & 05:02:00\\\\\n",
       "\t Females & Serbia         & 04:48:00\\\\\n",
       "\t Females & Turkey         & 04:59:00\\\\\n",
       "\t Females & United Kingdom & 03:50:00\\\\\n",
       "\t Males   & Austria        & 02:47:00\\\\\n",
       "\t Males   & Belgium        & 02:42:00\\\\\n",
       "\t Males   & Germany        & 02:35:00\\\\\n",
       "\t Males   & Estonia        & 02:52:00\\\\\n",
       "\t Males   & Greece         & 02:07:00\\\\\n",
       "\t Males   & Spain          & 02:36:00\\\\\n",
       "\t Males   & Finland        & 02:32:00\\\\\n",
       "\t Males   & France         & 02:53:00\\\\\n",
       "\t Males   & Hungary        & 02:55:00\\\\\n",
       "\t Males   & Italy          & 02:22:00\\\\\n",
       "\t Males   & Luxembourg     & 02:14:00\\\\\n",
       "\t Males   & Netherlands    & 02:27:00\\\\\n",
       "\t Males   & Norway         & 02:43:00\\\\\n",
       "\t Males   & Poland         & 02:48:00\\\\\n",
       "\t Males   & Romania        & 02:45:00\\\\\n",
       "\t Males   & Serbia         & 02:33:00\\\\\n",
       "\t Males   & Turkey         & 01:43:00\\\\\n",
       "\t Males   & United Kingdom & 02:27:00\\\\\n",
       "\t Total   & Austria        & 03:46:00\\\\\n",
       "\t Total   & Belgium        & 03:23:00\\\\\n",
       "\t Total   & Germany        & 03:15:00\\\\\n",
       "\t Total   & Estonia        & 03:35:00\\\\\n",
       "\t Total   & Greece         & 03:31:00\\\\\n",
       "\t Total   & Spain          & 03:43:00\\\\\n",
       "\t Total   & Finland        & 03:08:00\\\\\n",
       "\t Total   & France         & 03:33:00\\\\\n",
       "\t Total   & Hungary        & 03:56:00\\\\\n",
       "\t Total   & Italy          & 04:01:00\\\\\n",
       "\t Total   & Luxembourg     & 03:07:00\\\\\n",
       "\t Total   & Netherlands    & 02:59:00\\\\\n",
       "\t Total   & Norway         & 03:07:00\\\\\n",
       "\t Total   & Poland         & 03:46:00\\\\\n",
       "\t Total   & Romania        & 04:03:00\\\\\n",
       "\t Total   & Serbia         & 03:51:00\\\\\n",
       "\t Total   & Turkey         & 03:50:00\\\\\n",
       "\t Total   & United Kingdom & 03:11:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 54 × 3\n",
       "\n",
       "| sex &lt;chr&gt; | geo &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Females | Austria        | 04:32:00 |\n",
       "| Females | Belgium        | 03:58:00 |\n",
       "| Females | Germany        | 03:50:00 |\n",
       "| Females | Estonia        | 04:05:00 |\n",
       "| Females | Greece         | 04:28:00 |\n",
       "| Females | Spain          | 04:36:00 |\n",
       "| Females | Finland        | 03:41:00 |\n",
       "| Females | France         | 04:04:00 |\n",
       "| Females | Hungary        | 04:43:00 |\n",
       "| Females | Italy          | 05:09:00 |\n",
       "| Females | Luxembourg     | 03:54:00 |\n",
       "| Females | Netherlands    | 03:29:00 |\n",
       "| Females | Norway         | 03:30:00 |\n",
       "| Females | Poland         | 04:33:00 |\n",
       "| Females | Romania        | 05:02:00 |\n",
       "| Females | Serbia         | 04:48:00 |\n",
       "| Females | Turkey         | 04:59:00 |\n",
       "| Females | United Kingdom | 03:50:00 |\n",
       "| Males   | Austria        | 02:47:00 |\n",
       "| Males   | Belgium        | 02:42:00 |\n",
       "| Males   | Germany        | 02:35:00 |\n",
       "| Males   | Estonia        | 02:52:00 |\n",
       "| Males   | Greece         | 02:07:00 |\n",
       "| Males   | Spain          | 02:36:00 |\n",
       "| Males   | Finland        | 02:32:00 |\n",
       "| Males   | France         | 02:53:00 |\n",
       "| Males   | Hungary        | 02:55:00 |\n",
       "| Males   | Italy          | 02:22:00 |\n",
       "| Males   | Luxembourg     | 02:14:00 |\n",
       "| Males   | Netherlands    | 02:27:00 |\n",
       "| Males   | Norway         | 02:43:00 |\n",
       "| Males   | Poland         | 02:48:00 |\n",
       "| Males   | Romania        | 02:45:00 |\n",
       "| Males   | Serbia         | 02:33:00 |\n",
       "| Males   | Turkey         | 01:43:00 |\n",
       "| Males   | United Kingdom | 02:27:00 |\n",
       "| Total   | Austria        | 03:46:00 |\n",
       "| Total   | Belgium        | 03:23:00 |\n",
       "| Total   | Germany        | 03:15:00 |\n",
       "| Total   | Estonia        | 03:35:00 |\n",
       "| Total   | Greece         | 03:31:00 |\n",
       "| Total   | Spain          | 03:43:00 |\n",
       "| Total   | Finland        | 03:08:00 |\n",
       "| Total   | France         | 03:33:00 |\n",
       "| Total   | Hungary        | 03:56:00 |\n",
       "| Total   | Italy          | 04:01:00 |\n",
       "| Total   | Luxembourg     | 03:07:00 |\n",
       "| Total   | Netherlands    | 02:59:00 |\n",
       "| Total   | Norway         | 03:07:00 |\n",
       "| Total   | Poland         | 03:46:00 |\n",
       "| Total   | Romania        | 04:03:00 |\n",
       "| Total   | Serbia         | 03:51:00 |\n",
       "| Total   | Turkey         | 03:50:00 |\n",
       "| Total   | United Kingdom | 03:11:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   sex     geo            values  \n",
       "1  Females Austria        04:32:00\n",
       "2  Females Belgium        03:58:00\n",
       "3  Females Germany        03:50:00\n",
       "4  Females Estonia        04:05:00\n",
       "5  Females Greece         04:28:00\n",
       "6  Females Spain          04:36:00\n",
       "7  Females Finland        03:41:00\n",
       "8  Females France         04:04:00\n",
       "9  Females Hungary        04:43:00\n",
       "10 Females Italy          05:09:00\n",
       "11 Females Luxembourg     03:54:00\n",
       "12 Females Netherlands    03:29:00\n",
       "13 Females Norway         03:30:00\n",
       "14 Females Poland         04:33:00\n",
       "15 Females Romania        05:02:00\n",
       "16 Females Serbia         04:48:00\n",
       "17 Females Turkey         04:59:00\n",
       "18 Females United Kingdom 03:50:00\n",
       "19 Males   Austria        02:47:00\n",
       "20 Males   Belgium        02:42:00\n",
       "21 Males   Germany        02:35:00\n",
       "22 Males   Estonia        02:52:00\n",
       "23 Males   Greece         02:07:00\n",
       "24 Males   Spain          02:36:00\n",
       "25 Males   Finland        02:32:00\n",
       "26 Males   France         02:53:00\n",
       "27 Males   Hungary        02:55:00\n",
       "28 Males   Italy          02:22:00\n",
       "29 Males   Luxembourg     02:14:00\n",
       "30 Males   Netherlands    02:27:00\n",
       "31 Males   Norway         02:43:00\n",
       "32 Males   Poland         02:48:00\n",
       "33 Males   Romania        02:45:00\n",
       "34 Males   Serbia         02:33:00\n",
       "35 Males   Turkey         01:43:00\n",
       "36 Males   United Kingdom 02:27:00\n",
       "37 Total   Austria        03:46:00\n",
       "38 Total   Belgium        03:23:00\n",
       "39 Total   Germany        03:15:00\n",
       "40 Total   Estonia        03:35:00\n",
       "41 Total   Greece         03:31:00\n",
       "42 Total   Spain          03:43:00\n",
       "43 Total   Finland        03:08:00\n",
       "44 Total   France         03:33:00\n",
       "45 Total   Hungary        03:56:00\n",
       "46 Total   Italy          04:01:00\n",
       "47 Total   Luxembourg     03:07:00\n",
       "48 Total   Netherlands    02:59:00\n",
       "49 Total   Norway         03:07:00\n",
       "50 Total   Poland         03:46:00\n",
       "51 Total   Romania        04:03:00\n",
       "52 Total   Serbia         03:51:00\n",
       "53 Total   Turkey         03:50:00\n",
       "54 Total   United Kingdom 03:11:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))]\n",
    "dt<-dt[,c(\"sex\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing total value of *sex*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(sex=c(\"Males\",\"Males\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Total\",sex)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia','Turkey')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "sex_ord<-c('Males','Females',\"Total\")\n",
    "dt$sex<-factor(dt$sex,levels=sex_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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LIGjGWOIIi930QEuScNZnyZ9zpvVFMzdRfuIAjyYsW/2KB1rVkERVJE60k8nY+w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dLrqBu5fMuxn3/H7/610usA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\nQg8AAAAAQLmJ9AB1J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3/6pZ2fyWBmZd3Dtm9iFrueIT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\nL3BXw5+mrEgUU1Lxu60P09f44NpNVEulvrxz8ca9p89eZ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bj8OPTQk2tHjC3tGthZspUYM65cubKmw0Po6+nxvH/dNHvNqp0xOcUAQNOy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\noSjpATPJGVcnLPrJOPao2XL0+2EN/FyEEDEJmx+d5uhaf+oXKw6vnTp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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3_colors<-c(\"#FAA519\",\"#286EB4\",\"#F06423\")\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt[sex!=\"Total\"], aes(fill=sex),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  geom_point(data=dt[grepl(\"Total\",sex)],aes(shape=sex),colour=\"#F06423\",fill=\"#F06423\",size=3)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,1/24)) +\n",
    "  scale_fill_manual(values = fig3_colors)+\n",
    "  scale_shape_manual(values=c(\"Males\"=NA,\"Females\"=NA,\"Total\"=23))+\n",
    "  ggtitle(\"Figure 3: Participation time per day in household and family care, by gender, (hh mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 4: Participation rate per day in household and family care, main activity, %, by gender (2008 to 2015)\n",
    "\n",
    "The data is this time in the *tus_00educ* dataset. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"household.*care\",sex=\"male\",isced97=\"^all\")`. This time we can use the SDMX REST API to get the data as it is numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>isced97</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>96.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>95.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>96.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>95.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>94.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>94.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>97.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>94.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>97.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>95.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>94.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>96.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>98.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>97.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>96.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>96.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>95.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>96.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>81.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>86.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>88.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>83.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>71.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>77.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>93.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>82.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>85.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>69.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>80.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>88.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>91.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>86.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>78.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>77.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>53.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Household and family care</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>88.9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & isced97 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Austria                                          & 2010 & 96.4\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Belgium                                          & 2010 & 95.6\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 96.3\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Estonia                                          & 2010 & 95.7\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Greece                                           & 2010 & 94.6\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Spain                                            & 2010 & 94.8\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Finland                                          & 2010 & 97.3\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & France                                           & 2010 & 94.8\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Hungary                                          & 2010 & 97.4\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Italy                                            & 2010 & 95.4\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Luxembourg                                       & 2010 & 94.2\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Netherlands                                      & 2010 & 96.3\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Norway                                           & 2010 & 98.3\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Poland                                           & 2010 & 97.9\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Romania                                          & 2010 & 96.3\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Serbia                                           & 2010 & 96.6\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & Turkey                                           & 2010 & 95.0\\\\\n",
       "\t Participation rate (\\%) & Females & Household and family care & All ISCED 1997 levels & United Kingdom                                   & 2010 & 96.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Austria                                          & 2010 & 81.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Belgium                                          & 2010 & 86.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 88.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Estonia                                          & 2010 & 83.1\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Greece                                           & 2010 & 71.6\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Spain                                            & 2010 & 77.2\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Finland                                          & 2010 & 93.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & France                                           & 2010 & 82.1\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Hungary                                          & 2010 & 85.9\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Italy                                            & 2010 & 69.7\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Luxembourg                                       & 2010 & 80.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Netherlands                                      & 2010 & 88.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Norway                                           & 2010 & 91.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Poland                                           & 2010 & 86.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Romania                                          & 2010 & 78.9\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Serbia                                           & 2010 & 77.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & Turkey                                           & 2010 & 53.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Household and family care & All ISCED 1997 levels & United Kingdom                                   & 2010 & 88.9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | isced97 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Austria                                          | 2010 | 96.4 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Belgium                                          | 2010 | 95.6 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 96.3 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Estonia                                          | 2010 | 95.7 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Greece                                           | 2010 | 94.6 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Spain                                            | 2010 | 94.8 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Finland                                          | 2010 | 97.3 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | France                                           | 2010 | 94.8 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Hungary                                          | 2010 | 97.4 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Italy                                            | 2010 | 95.4 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Luxembourg                                       | 2010 | 94.2 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Netherlands                                      | 2010 | 96.3 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Norway                                           | 2010 | 98.3 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Poland                                           | 2010 | 97.9 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Romania                                          | 2010 | 96.3 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Serbia                                           | 2010 | 96.6 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | Turkey                                           | 2010 | 95.0 |\n",
       "| Participation rate (%) | Females | Household and family care | All ISCED 1997 levels | United Kingdom                                   | 2010 | 96.8 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Austria                                          | 2010 | 81.5 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Belgium                                          | 2010 | 86.5 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 88.5 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Estonia                                          | 2010 | 83.1 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Greece                                           | 2010 | 71.6 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Spain                                            | 2010 | 77.2 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Finland                                          | 2010 | 93.0 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | France                                           | 2010 | 82.1 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Hungary                                          | 2010 | 85.9 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Italy                                            | 2010 | 69.7 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Luxembourg                                       | 2010 | 80.8 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Netherlands                                      | 2010 | 88.8 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Norway                                           | 2010 | 91.8 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Poland                                           | 2010 | 86.4 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Romania                                          | 2010 | 78.9 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Serbia                                           | 2010 | 77.5 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | Turkey                                           | 2010 | 53.4 |\n",
       "| Participation rate (%) | Males   | Household and family care | All ISCED 1997 levels | United Kingdom                                   | 2010 | 88.9 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                   sex     acl00                    \n",
       "1  Participation rate (%) Females Household and family care\n",
       "2  Participation rate (%) Females Household and family care\n",
       "3  Participation rate (%) Females Household and family care\n",
       "4  Participation rate (%) Females Household and family care\n",
       "5  Participation rate (%) Females Household and family care\n",
       "6  Participation rate (%) Females Household and family care\n",
       "7  Participation rate (%) Females Household and family care\n",
       "8  Participation rate (%) Females Household and family care\n",
       "9  Participation rate (%) Females Household and family care\n",
       "10 Participation rate (%) Females Household and family care\n",
       "11 Participation rate (%) Females Household and family care\n",
       "12 Participation rate (%) Females Household and family care\n",
       "13 Participation rate (%) Females Household and family care\n",
       "14 Participation rate (%) Females Household and family care\n",
       "15 Participation rate (%) Females Household and family care\n",
       "16 Participation rate (%) Females Household and family care\n",
       "17 Participation rate (%) Females Household and family care\n",
       "18 Participation rate (%) Females Household and family care\n",
       "19 Participation rate (%) Males   Household and family care\n",
       "20 Participation rate (%) Males   Household and family care\n",
       "21 Participation rate (%) Males   Household and family care\n",
       "22 Participation rate (%) Males   Household and family care\n",
       "23 Participation rate (%) Males   Household and family care\n",
       "24 Participation rate (%) Males   Household and family care\n",
       "25 Participation rate (%) Males   Household and family care\n",
       "26 Participation rate (%) Males   Household and family care\n",
       "27 Participation rate (%) Males   Household and family care\n",
       "28 Participation rate (%) Males   Household and family care\n",
       "29 Participation rate (%) Males   Household and family care\n",
       "30 Participation rate (%) Males   Household and family care\n",
       "31 Participation rate (%) Males   Household and family care\n",
       "32 Participation rate (%) Males   Household and family care\n",
       "33 Participation rate (%) Males   Household and family care\n",
       "34 Participation rate (%) Males   Household and family care\n",
       "35 Participation rate (%) Males   Household and family care\n",
       "36 Participation rate (%) Males   Household and family care\n",
       "   isced97               geo                                              time\n",
       "1  All ISCED 1997 levels Austria                                          2010\n",
       "2  All ISCED 1997 levels Belgium                                          2010\n",
       "3  All ISCED 1997 levels Germany (until 1990 former territory of the FRG) 2010\n",
       "4  All ISCED 1997 levels Estonia                                          2010\n",
       "5  All ISCED 1997 levels Greece                                           2010\n",
       "6  All ISCED 1997 levels Spain                                            2010\n",
       "7  All ISCED 1997 levels Finland                                          2010\n",
       "8  All ISCED 1997 levels France                                           2010\n",
       "9  All ISCED 1997 levels Hungary                                          2010\n",
       "10 All ISCED 1997 levels Italy                                            2010\n",
       "11 All ISCED 1997 levels Luxembourg                                       2010\n",
       "12 All ISCED 1997 levels Netherlands                                      2010\n",
       "13 All ISCED 1997 levels Norway                                           2010\n",
       "14 All ISCED 1997 levels Poland                                           2010\n",
       "15 All ISCED 1997 levels Romania                                          2010\n",
       "16 All ISCED 1997 levels Serbia                                           2010\n",
       "17 All ISCED 1997 levels Turkey                                           2010\n",
       "18 All ISCED 1997 levels United Kingdom                                   2010\n",
       "19 All ISCED 1997 levels Austria                                          2010\n",
       "20 All ISCED 1997 levels Belgium                                          2010\n",
       "21 All ISCED 1997 levels Germany (until 1990 former territory of the FRG) 2010\n",
       "22 All ISCED 1997 levels Estonia                                          2010\n",
       "23 All ISCED 1997 levels Greece                                           2010\n",
       "24 All ISCED 1997 levels Spain                                            2010\n",
       "25 All ISCED 1997 levels Finland                                          2010\n",
       "26 All ISCED 1997 levels France                                           2010\n",
       "27 All ISCED 1997 levels Hungary                                          2010\n",
       "28 All ISCED 1997 levels Italy                                            2010\n",
       "29 All ISCED 1997 levels Luxembourg                                       2010\n",
       "30 All ISCED 1997 levels Netherlands                                      2010\n",
       "31 All ISCED 1997 levels Norway                                           2010\n",
       "32 All ISCED 1997 levels Poland                                           2010\n",
       "33 All ISCED 1997 levels Romania                                          2010\n",
       "34 All ISCED 1997 levels Serbia                                           2010\n",
       "35 All ISCED 1997 levels Turkey                                           2010\n",
       "36 All ISCED 1997 levels United Kingdom                                   2010\n",
       "   values\n",
       "1  96.4  \n",
       "2  95.6  \n",
       "3  96.3  \n",
       "4  95.7  \n",
       "5  94.6  \n",
       "6  94.8  \n",
       "7  97.3  \n",
       "8  94.8  \n",
       "9  97.4  \n",
       "10 95.4  \n",
       "11 94.2  \n",
       "12 96.3  \n",
       "13 98.3  \n",
       "14 97.9  \n",
       "15 96.3  \n",
       "16 96.6  \n",
       "17 95.0  \n",
       "18 96.8  \n",
       "19 81.5  \n",
       "20 86.5  \n",
       "21 88.5  \n",
       "22 83.1  \n",
       "23 71.6  \n",
       "24 77.2  \n",
       "25 93.0  \n",
       "26 82.1  \n",
       "27 85.9  \n",
       "28 69.7  \n",
       "29 80.8  \n",
       "30 88.8  \n",
       "31 91.8  \n",
       "32 86.4  \n",
       "33 78.9  \n",
       "34 77.5  \n",
       "35 53.4  \n",
       "36 88.9  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ\",filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"household.*care\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we keep only the columns with sex, countries and values. Before plotting the values we need to cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Females</td><td>Austria       </td><td>96.4</td></tr>\n",
       "\t<tr><td>Females</td><td>Belgium       </td><td>95.6</td></tr>\n",
       "\t<tr><td>Females</td><td>Germany       </td><td>96.3</td></tr>\n",
       "\t<tr><td>Females</td><td>Estonia       </td><td>95.7</td></tr>\n",
       "\t<tr><td>Females</td><td>Greece        </td><td>94.6</td></tr>\n",
       "\t<tr><td>Females</td><td>Spain         </td><td>94.8</td></tr>\n",
       "\t<tr><td>Females</td><td>Finland       </td><td>97.3</td></tr>\n",
       "\t<tr><td>Females</td><td>France        </td><td>94.8</td></tr>\n",
       "\t<tr><td>Females</td><td>Hungary       </td><td>97.4</td></tr>\n",
       "\t<tr><td>Females</td><td>Italy         </td><td>95.4</td></tr>\n",
       "\t<tr><td>Females</td><td>Luxembourg    </td><td>94.2</td></tr>\n",
       "\t<tr><td>Females</td><td>Netherlands   </td><td>96.3</td></tr>\n",
       "\t<tr><td>Females</td><td>Norway        </td><td>98.3</td></tr>\n",
       "\t<tr><td>Females</td><td>Poland        </td><td>97.9</td></tr>\n",
       "\t<tr><td>Females</td><td>Romania       </td><td>96.3</td></tr>\n",
       "\t<tr><td>Females</td><td>Serbia        </td><td>96.6</td></tr>\n",
       "\t<tr><td>Females</td><td>Turkey        </td><td>95.0</td></tr>\n",
       "\t<tr><td>Females</td><td>United Kingdom</td><td>96.8</td></tr>\n",
       "\t<tr><td>Males  </td><td>Austria       </td><td>81.5</td></tr>\n",
       "\t<tr><td>Males  </td><td>Belgium       </td><td>86.5</td></tr>\n",
       "\t<tr><td>Males  </td><td>Germany       </td><td>88.5</td></tr>\n",
       "\t<tr><td>Males  </td><td>Estonia       </td><td>83.1</td></tr>\n",
       "\t<tr><td>Males  </td><td>Greece        </td><td>71.6</td></tr>\n",
       "\t<tr><td>Males  </td><td>Spain         </td><td>77.2</td></tr>\n",
       "\t<tr><td>Males  </td><td>Finland       </td><td>93.0</td></tr>\n",
       "\t<tr><td>Males  </td><td>France        </td><td>82.1</td></tr>\n",
       "\t<tr><td>Males  </td><td>Hungary       </td><td>85.9</td></tr>\n",
       "\t<tr><td>Males  </td><td>Italy         </td><td>69.7</td></tr>\n",
       "\t<tr><td>Males  </td><td>Luxembourg    </td><td>80.8</td></tr>\n",
       "\t<tr><td>Males  </td><td>Netherlands   </td><td>88.8</td></tr>\n",
       "\t<tr><td>Males  </td><td>Norway        </td><td>91.8</td></tr>\n",
       "\t<tr><td>Males  </td><td>Poland        </td><td>86.4</td></tr>\n",
       "\t<tr><td>Males  </td><td>Romania       </td><td>78.9</td></tr>\n",
       "\t<tr><td>Males  </td><td>Serbia        </td><td>77.5</td></tr>\n",
       "\t<tr><td>Males  </td><td>Turkey        </td><td>53.4</td></tr>\n",
       "\t<tr><td>Males  </td><td>United Kingdom</td><td>88.9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Females & Austria        & 96.4\\\\\n",
       "\t Females & Belgium        & 95.6\\\\\n",
       "\t Females & Germany        & 96.3\\\\\n",
       "\t Females & Estonia        & 95.7\\\\\n",
       "\t Females & Greece         & 94.6\\\\\n",
       "\t Females & Spain          & 94.8\\\\\n",
       "\t Females & Finland        & 97.3\\\\\n",
       "\t Females & France         & 94.8\\\\\n",
       "\t Females & Hungary        & 97.4\\\\\n",
       "\t Females & Italy          & 95.4\\\\\n",
       "\t Females & Luxembourg     & 94.2\\\\\n",
       "\t Females & Netherlands    & 96.3\\\\\n",
       "\t Females & Norway         & 98.3\\\\\n",
       "\t Females & Poland         & 97.9\\\\\n",
       "\t Females & Romania        & 96.3\\\\\n",
       "\t Females & Serbia         & 96.6\\\\\n",
       "\t Females & Turkey         & 95.0\\\\\n",
       "\t Females & United Kingdom & 96.8\\\\\n",
       "\t Males   & Austria        & 81.5\\\\\n",
       "\t Males   & Belgium        & 86.5\\\\\n",
       "\t Males   & Germany        & 88.5\\\\\n",
       "\t Males   & Estonia        & 83.1\\\\\n",
       "\t Males   & Greece         & 71.6\\\\\n",
       "\t Males   & Spain          & 77.2\\\\\n",
       "\t Males   & Finland        & 93.0\\\\\n",
       "\t Males   & France         & 82.1\\\\\n",
       "\t Males   & Hungary        & 85.9\\\\\n",
       "\t Males   & Italy          & 69.7\\\\\n",
       "\t Males   & Luxembourg     & 80.8\\\\\n",
       "\t Males   & Netherlands    & 88.8\\\\\n",
       "\t Males   & Norway         & 91.8\\\\\n",
       "\t Males   & Poland         & 86.4\\\\\n",
       "\t Males   & Romania        & 78.9\\\\\n",
       "\t Males   & Serbia         & 77.5\\\\\n",
       "\t Males   & Turkey         & 53.4\\\\\n",
       "\t Males   & United Kingdom & 88.9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 3\n",
       "\n",
       "| sex &lt;chr&gt; | geo &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Females | Austria        | 96.4 |\n",
       "| Females | Belgium        | 95.6 |\n",
       "| Females | Germany        | 96.3 |\n",
       "| Females | Estonia        | 95.7 |\n",
       "| Females | Greece         | 94.6 |\n",
       "| Females | Spain          | 94.8 |\n",
       "| Females | Finland        | 97.3 |\n",
       "| Females | France         | 94.8 |\n",
       "| Females | Hungary        | 97.4 |\n",
       "| Females | Italy          | 95.4 |\n",
       "| Females | Luxembourg     | 94.2 |\n",
       "| Females | Netherlands    | 96.3 |\n",
       "| Females | Norway         | 98.3 |\n",
       "| Females | Poland         | 97.9 |\n",
       "| Females | Romania        | 96.3 |\n",
       "| Females | Serbia         | 96.6 |\n",
       "| Females | Turkey         | 95.0 |\n",
       "| Females | United Kingdom | 96.8 |\n",
       "| Males   | Austria        | 81.5 |\n",
       "| Males   | Belgium        | 86.5 |\n",
       "| Males   | Germany        | 88.5 |\n",
       "| Males   | Estonia        | 83.1 |\n",
       "| Males   | Greece         | 71.6 |\n",
       "| Males   | Spain          | 77.2 |\n",
       "| Males   | Finland        | 93.0 |\n",
       "| Males   | France         | 82.1 |\n",
       "| Males   | Hungary        | 85.9 |\n",
       "| Males   | Italy          | 69.7 |\n",
       "| Males   | Luxembourg     | 80.8 |\n",
       "| Males   | Netherlands    | 88.8 |\n",
       "| Males   | Norway         | 91.8 |\n",
       "| Males   | Poland         | 86.4 |\n",
       "| Males   | Romania        | 78.9 |\n",
       "| Males   | Serbia         | 77.5 |\n",
       "| Males   | Turkey         | 53.4 |\n",
       "| Males   | United Kingdom | 88.9 |\n",
       "\n"
      ],
      "text/plain": [
       "   sex     geo            values\n",
       "1  Females Austria        96.4  \n",
       "2  Females Belgium        95.6  \n",
       "3  Females Germany        96.3  \n",
       "4  Females Estonia        95.7  \n",
       "5  Females Greece         94.6  \n",
       "6  Females Spain          94.8  \n",
       "7  Females Finland        97.3  \n",
       "8  Females France         94.8  \n",
       "9  Females Hungary        97.4  \n",
       "10 Females Italy          95.4  \n",
       "11 Females Luxembourg     94.2  \n",
       "12 Females Netherlands    96.3  \n",
       "13 Females Norway         98.3  \n",
       "14 Females Poland         97.9  \n",
       "15 Females Romania        96.3  \n",
       "16 Females Serbia         96.6  \n",
       "17 Females Turkey         95.0  \n",
       "18 Females United Kingdom 96.8  \n",
       "19 Males   Austria        81.5  \n",
       "20 Males   Belgium        86.5  \n",
       "21 Males   Germany        88.5  \n",
       "22 Males   Estonia        83.1  \n",
       "23 Males   Greece         71.6  \n",
       "24 Males   Spain          77.2  \n",
       "25 Males   Finland        93.0  \n",
       "26 Males   France         82.1  \n",
       "27 Males   Hungary        85.9  \n",
       "28 Males   Italy          69.7  \n",
       "29 Males   Luxembourg     80.8  \n",
       "30 Males   Netherlands    88.8  \n",
       "31 Males   Norway         91.8  \n",
       "32 Males   Poland         86.4  \n",
       "33 Males   Romania        78.9  \n",
       "34 Males   Serbia         77.5  \n",
       "35 Males   Turkey         53.4  \n",
       "36 Males   United Kingdom 88.9  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "dt<-dt[,c(\"sex\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(sex=c(\"Males\",\"Males\"),geo=c(\" \",\"  \"),values=c(NA,NA))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Females\",sex)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Turkey','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "sex_ord<-c('Males','Females')\n",
    "dt$sex<-factor(dt$sex,levels=sex_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
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hOz5iwpb13a/OtmqJuLefa0J5aeP2oqKi23yRpk4TadPP1yUd7bHg26bQ55VZE4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\nj6752llFEES1Pusq2bQZ7KHlNllTl6Y8A8f7d+/e+epqyKoUajN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cT1nb3bmx+zJPa8FDHXZoj/8q5QKD/+/ZqZniOOznIz\nvONLTe1EbrsgvfbMqJriCuX77jf/3rVabVLkKnETF9/Xs4cSbBukz0i5Lw66uPh0sTjIG8ta\nGQxgqukB2ENmWoR4oadCoer4yawj524kZmi02oyQ6+dWThssjlkq1d77Y4wjuLOaPg8/OLrX\nMv0jXEDkEKR/dcIeM42SIw838TXeGX3aDePwVUG47w93DhZ3qFC5fLs7xEz9S0sNHmmdnIL0\n0xsbbHPgXqLriahkM/1HX15f091gGbF/nekma9r1M/aHZgbhMYVC8d6Pp810G3VmsZ+DSsgm\npyC9bJfF48XnbYXXRhy9I3VHbSmWVPfTnaXyoKM27NmAJu3D8gZr9CsONP4Mv73aIMl52Z4H\npHefkXxL3DZ7pNaunWeJf7jZaMZGjbfGh+bwnXtr1zyjL/TKfRdIH5J50r95k59sdTFcVh7c\ndlT2iZViJ+a/LRjq9tstW43cavn9XEmRlyB9WvwZcVuF5PxDgiC4F2+947YV54o831fXVu1S\ntgB/nQMAAAAAYAFBegCFW/YgvU+1L82vCVvVKtioSfl+q8yeRNPDz0VcP3v8T6vVJkasFNfx\nKjNSyvh3vl5a3GpdlOmFhrkO0o859Nj8ANISzolj2Ep1ESnDNs+ul2J6LYP9dKt/tstiz3s/\nM8jS/MpIE6EvGwbpk6LWiSs4etQz/0BqtdrTI18RN2k8x0SYzbZBeq1W+2Ntgyu55LHpZbI6\nQ0vos1AoFKqcQqR28uTcIs9sS4dVjp5F3Y1XkCsUio/X3DRq/vTydF2F15Yb/2vBYipIH9x2\nrsV2CaHb/Q2jtsUaLs5eLb/vu6Z/gEHSiDazz1pss+m9itmvicmRxD1YKK6jdi65y9SkHCNP\nzv8gjv8pFIqfQuOzV7PfZ2xS1EajVb8Nxuy1OOx7Wz7OPh6TQXr5L0utj3+x+LlnJU01UbqI\nLgfCbNu7zrIPaonfiELpvCnbl9H5b+uI69Sd9K9Vp/AQPw8qVzk714k4Ps/oE9XRs+x7I8b/\n/Nv6Q6cuPrhz9e+dfyyaPbVvm6qCoaDWXydm2uzWSv/C2tnHYLKLi1/HqDTLwdmNAw0+Olx8\nOuVXQDffnysp8hKkj30wUcgDtVOJ9felzulJjt4hbrs2Mk8pOgAAAAAAyEdS97sFgMLi/zaO\nUZtdwNN0RGVxUan2XLmol9kuFZ+1NPjDZXh6ZvZKyVHbxcVqo96zMFBBEAShbDeDGQPxmdqc\nauZCQJOFk5sHmK/j4Fb7y+LuuqIm41lqnodgv0uRGvvPmAv6DewdXCtt/b69xZ5bTPnFUbSo\n6+7KpVLGk2sufm8OCtRf0rT4M9Pvx5ptof1myU1dQaF0/mGQiZCkzXWb2Upc/GHONTOVU6K3\nzQ9N0BWLVBhntJmuvfnW/uj8pklGKbgz0+IiE9LFryiUToPmHVzYp4Jha83Y16dlHbkW7b6u\nv9G/FnRKtfdvGz+0WM0tuMuubw3SQUedGXErOcOoWv7e9/iQuSvD9bsyu/h12fK55Y2TX1uw\nsYST2mI1QRD+GTpTXGz23c6OxSxveOxb69PNomCeVqudN+2ylNPZ6jP20vSJmaKt4l18O+6e\n0Nbi2Uu/tnCs4ZSLnMh8WdyDe/0z/33zX8TWSonefiVR/8Pes4q5DelzK3PjmHbv/fyv+KWq\nH25+w3CWniAIiXcNdhZ39LWwobsRXwf9/780mUmP0zSyda5TtNHnN46saBqonzGTFnd32cyJ\nH7z7ZosGr5QqV7VN5zc+Hj569d9XdRUUClWnz+Zf2jPF1ZpF0jahzYz95M8H4lc+/XOZn4Pl\n/8a+vmhLeRf9R0dy9K7vDLe0l0WBeK7sLe7WsewvKlXuHd7+dMW2wzfvhsQlpyfFPr1389If\ny+a+/1o9heG0pIzUkIENe99LMfHbdXbO3h3Fm4MsOxOVx8EDAAAAAJBfCNIDeKk4eTUfVdnb\nfB2vSgbbpRep8G0jDwt/CfVv4me+giAIDi5dJ4iMeKOUxSaCIKjdJUWecud/S/pIqda4pLvl\nStaw36W4v/EbcSirXN8lZZxNJHw24ujRYHiwfjVwUtTqDFvOhTDhi9EGa/dXTvw3p5qCIMQ/\nnLM7Rr942r/2zLrZVofbQ0Cz2d6ilZS3fp1ipvK1+eO1oivfcvZA+w0sJ2VfG33twh9vNy+T\nUwXvKu1+2nfr589aGL0eunvIojvP50kM2rDIubD97hPYYlGbIpJC43VG/FlWlMVak5kw4dJT\nozr5e98vT10iLtabMtNdZTngp3atsaRLScu9a9O+2B2ib+VSfsPHVSQOrPWc5WpR0OjBxkVS\nWtnqM3beyjsGg1m8qIi0EPfwtR9ZriT7Zeny21xPCbfVKrG39FtuKxQOvbIFOPMoOfLU4Nbl\nek/dL37Rs8yb++d2yF457VmauOhYxLpgqo/hEvYnGQbBVLt2LhbQsN8/d69+06OslG7VzmUX\n/HVj5/zPfGw7+UKa2LuT7qXo5xu5+PX4vqmFyTFZ1C6VfzFcgr9qrrlpSTZXcJ4rewvf/djo\nFa/yr24492D36h/e7dqsQpniHs5qF0+f0hWq9xg4dOmW0w+Pb2gdZJBVJSlid4f3dwiSKN/0\n108zuvnzHTNVAQAAAAAoyOwYHAIA+RWpMNxiHaXaIOJedkBri02krGfyLNtv/HiLtYwl3E6w\nXClX1M5lJlb1kVLTydfGS6LtdykOzr0uLv5vXK2cahp555N3bovWWj1KyyzpZDm6n2vl352h\n/rx5xovo5oPNX2uWH8spNHxuws/iYo9Fve03MDGVY/EZdfwHnYrIKiY/+XNFRNKAHNbXjltw\nXdSw6IL2xeUYYjZelV9bfei1r/euW71p81+HzoY+ehSX7hAYFFStQevuPXoP6NnKMVv8SJuZ\nMKjvqqxjnypfzmsRaFyjwOs5p53Emgq1z489Sr+65rbulTMLbwkNionr5O99X7FFHy1WKFQz\n35YUIBQEocHUt4Q/ppmvk/Bo0W1R5oDApt/7ZdsiISeOHo0+DXKfGxafVUyK2vAsY4X5SLmt\nPmMzkq6uiUrWFVUO/ou7SZiRIAiCIBSpOK6513eHY1PN1JH5sqgc/Oc3t/1PWfg+/ZPj4F7L\nw3aTALSaxM0/jB8+at6DFIO0E84+DbecXlHM1HLtjCSDmo7e1gVTvQ2vf2KmQTDVrp0byjy4\nasmG/aFSus1Iubvil58a1pxYt5iNp0dIcXvpXnGx6hdW/IZRZ+KHwrIvdMWHmzcJ8xrabGQ5\nK2jPlb09+DvCYDBV+104t7xEzr9oFW/Yc/eNKm9XbbQpJF734t317/zz45OWEvK1NPFxnhP6\nvGHMxfOCYPk3eQAAAAAACqDCtpoMAMwqUkNC7l/Dv+1717aw8l4QBIVgl6VjGUlXh8+5arle\nrniW+sKOUWhbk3gpljzQJ6pVqr0/L+5hprJY9a8WbRCxa4ReEARHz6Zjynnpiqlxx79/kEOK\nXW3a8A339A3da82pJyl/tU10mtFGXJz7w3WT1eJD5m5/qg8iBrb8IdgxP39/qNH+rWmL156/\neivqWWJq4rP7t67uWLVwcC8TEXpBEK4v6bk7OlkQBIVCOWHrWLnHmmcKhcOoypIiwVnqftNK\nXIw8bCIFcX7dd21m/G+R+nzOzt4dG1jKYqLjVW6Ek6Uk25HidjfGAAAgAElEQVTHt4iLVb+q\nb9XwutX11R1rNSmbRe/dJFt9xiZGLBOnK/Ao8aWZyFY2qjENi5qvIfNlcS3ar6iEPOTWerQ3\nXHfs5NXMVt2e/mNey/KBPYfNMoqkupdsv/f6wVa+zqabGeVisfLtGsVOU43Kdu38hfj7f/Vp\nXLLdoMk34tJM18jm1IYZDUuW/WTGFjtnojHhwrYwcbHzOznmU8nOo/jH/g76H6ikiN9liF0X\nxOfKzkKDqjV6oVmrvkdPL7P4OebgXnXF8eU+Bln6Ez788qSU0wXW038tpsTsyt2YAQAAAADI\ndwTpAbxUHLyszhPu6GXdWiUb0KY9uH5u5cyRjSo2+NtSwCPXilSvYblSvrPmUmSm3Dsbrw8n\nuPq/ZTIoW0AMmN5IXFwx+aLJajE3xp9L0L+p8gMXusi43W9A49ni6MXNn78zWe3MNz+Ki33n\nSl3Yne8yU+/3/PJA1nFw24WflfcyWS3p0eXff5jU5/VOjeu+UrFazZbtOr0//Nst/1yQN8Zh\nmotvt0BrQuOepT4RF1NiD2Svk1/3Pfnp1uRMUTS6dD/pbZVqny4+Fpbwhv5hsCa4ZSXTtzsn\nXtUM6p9NsBC8tNVn7LOr58XF4K6trGpe/j0L2QhkvixelUzk8c674/f0y22dvGywEvrR6T/6\nNi/doOeww6Kes9R8c+yVm7ua+ecQSRUEtZtBKrL0Z+lWnTrGMA+50Y4Pdu08y9NzS+tUe23d\niUe6VxQKdYNug35YvunfWw+exiZmpCU/CQ89c2Dr7AmfvRKgT7ORmRa+aGT3uu/OtveWMUb2\niFJNKBSqQQFuZiobUzj2Kyp+C49Px0udl5ALBfa5srehW/cff+HwgVVVXCWl63MLfmNNX4Mt\nqO5t/EZKQ9+G+slDqbGHkjTyTx0BAAAAAMAGSHcPAPaVFP3o5s1bt27dvHXrVtbB1Ss3nqVm\n2vu87uVsvNN83uXxUqTGHxcXXfzsEgqylRKd57mrqiS8yDd7b8NozZJD2WOtR79aqztWKBQT\nxteWa4CCIAhKh4AZ9fwHHn++RDUpav3qqOV9/Q1DoZqUYZvu60pOno2nVLFiYXf+OvpN92tJ\n6YIgKFVuP60dmL1CZuqDuV8Nn7DgzwRRZuBbVy8e2v/XsjnjA+p2nzH7h34t8ie3fxYnb+u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eH2Uy65b/HH/5jXIlPQbo+fnH/MXlvk/rhs5pWHjHoiN+7/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\nADBtjNMddKelMQcASNkdpgOpr/gNB6wb3U7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uBv/zGvp/P0Ko1vXqZnfxVBoAHAuO6r26d5Xt4w8f1mzYdKi6MaocRYqO7fglPCqBpiue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\no04JAAAAUNFsd0+1kD9z7H13DP1w4oLkYXqNJif3vvS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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4_colors<-c(\"#FAA519\",\"#286EB4\")\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(aes(fill=sex),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  scale_fill_manual(values = fig4_colors)+\n",
    "  scale_y_continuous(breaks=seq(0,100,10)) +\n",
    "  ggtitle(\"Figure 4: Participation rate per day in household and family care,\\n                main activity, %, by gender (2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5: Participation rate per day in cleaning and food management, by gender, % (2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00educ* dataset as in Figure 4. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^food|^clean\",sex=\"male\",isced97=\"^all\")`. This time we can use again the SDMX REST API to get the values are it is numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 72 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>isced97</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>81.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>77.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>76.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>85.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>85.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>81.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>84.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>79.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>85.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>87.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>78.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>78.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>86.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>90.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>86.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>88.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>88.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>80.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>68.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>30.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>59.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>50.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>62.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>56.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>51.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>65.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>54.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>79.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>33.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>51.6</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>60.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>43.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>43.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>40.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>42.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>52.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>69.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>53.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>29.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>31.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>19.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Food management except dish washing</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>56.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>24.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>10.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>28.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>18.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td> 9.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>18.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>23.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>25.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>16.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>16.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>10.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>22.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>35.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>25.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>16.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>16.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 7.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Cleaning dwelling                  </td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>20.2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 72 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & isced97 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Austria                                          & 2010 & 81.1\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Belgium                                          & 2010 & 77.0\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 76.6\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Estonia                                          & 2010 & 85.0\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Greece                                           & 2010 & 85.5\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Spain                                            & 2010 & 81.3\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Finland                                          & 2010 & 84.4\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & France                                           & 2010 & 79.7\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Hungary                                          & 2010 & 85.1\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Italy                                            & 2010 & 87.9\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Luxembourg                                       & 2010 & 78.4\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Netherlands                                      & 2010 & 78.5\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Norway                                           & 2010 & 86.2\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Poland                                           & 2010 & 90.9\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Romania                                          & 2010 & 86.9\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Serbia                                           & 2010 & 88.5\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & Turkey                                           & 2010 & 88.1\\\\\n",
       "\t Participation rate (\\%) & Females & Food management except dish washing & All ISCED 1997 levels & United Kingdom                                   & 2010 & 80.4\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Austria                                          & 2010 & 68.6\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Belgium                                          & 2010 & 30.9\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 59.8\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Estonia                                          & 2010 & 50.3\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Greece                                           & 2010 & 62.1\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Spain                                            & 2010 & 56.0\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Finland                                          & 2010 & 51.8\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & France                                           & 2010 & 65.6\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Hungary                                          & 2010 & 54.1\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Italy                                            & 2010 & 79.8\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Luxembourg                                       & 2010 & 33.7\\\\\n",
       "\t Participation rate (\\%) & Females & Cleaning dwelling                   & All ISCED 1997 levels & Netherlands                                      & 2010 & 51.6\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Finland                                          & 2010 & 60.9\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & France                                           & 2010 & 43.2\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Hungary                                          & 2010 & 43.3\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Italy                                            & 2010 & 40.5\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Luxembourg                                       & 2010 & 42.3\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Netherlands                                      & 2010 & 52.0\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Norway                                           & 2010 & 69.5\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Poland                                           & 2010 & 53.6\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Romania                                          & 2010 & 29.8\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Serbia                                           & 2010 & 31.4\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & Turkey                                           & 2010 & 19.0\\\\\n",
       "\t Participation rate (\\%) & Males & Food management except dish washing & All ISCED 1997 levels & United Kingdom                                   & 2010 & 56.0\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Austria                                          & 2010 & 24.5\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Belgium                                          & 2010 & 10.9\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 28.9\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Estonia                                          & 2010 & 18.3\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Greece                                           & 2010 &  9.2\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Spain                                            & 2010 & 18.8\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Finland                                          & 2010 & 23.9\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & France                                           & 2010 & 25.5\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Hungary                                          & 2010 & 16.4\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Italy                                            & 2010 & 16.8\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Luxembourg                                       & 2010 & 10.8\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Netherlands                                      & 2010 & 22.6\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Norway                                           & 2010 & 35.7\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Poland                                           & 2010 & 25.9\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Romania                                          & 2010 & 16.4\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Serbia                                           & 2010 & 16.5\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & Turkey                                           & 2010 &  7.0\\\\\n",
       "\t Participation rate (\\%) & Males & Cleaning dwelling                   & All ISCED 1997 levels & United Kingdom                                   & 2010 & 20.2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 72 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | isced97 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Austria                                          | 2010 | 81.1 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Belgium                                          | 2010 | 77.0 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 76.6 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Estonia                                          | 2010 | 85.0 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Greece                                           | 2010 | 85.5 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Spain                                            | 2010 | 81.3 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Finland                                          | 2010 | 84.4 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | France                                           | 2010 | 79.7 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Hungary                                          | 2010 | 85.1 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Italy                                            | 2010 | 87.9 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Luxembourg                                       | 2010 | 78.4 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Netherlands                                      | 2010 | 78.5 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Norway                                           | 2010 | 86.2 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Poland                                           | 2010 | 90.9 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Romania                                          | 2010 | 86.9 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Serbia                                           | 2010 | 88.5 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | Turkey                                           | 2010 | 88.1 |\n",
       "| Participation rate (%) | Females | Food management except dish washing | All ISCED 1997 levels | United Kingdom                                   | 2010 | 80.4 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Austria                                          | 2010 | 68.6 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Belgium                                          | 2010 | 30.9 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 59.8 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Estonia                                          | 2010 | 50.3 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Greece                                           | 2010 | 62.1 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Spain                                            | 2010 | 56.0 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Finland                                          | 2010 | 51.8 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | France                                           | 2010 | 65.6 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Hungary                                          | 2010 | 54.1 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Italy                                            | 2010 | 79.8 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Luxembourg                                       | 2010 | 33.7 |\n",
       "| Participation rate (%) | Females | Cleaning dwelling                   | All ISCED 1997 levels | Netherlands                                      | 2010 | 51.6 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Finland                                          | 2010 | 60.9 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | France                                           | 2010 | 43.2 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Hungary                                          | 2010 | 43.3 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Italy                                            | 2010 | 40.5 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Luxembourg                                       | 2010 | 42.3 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Netherlands                                      | 2010 | 52.0 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Norway                                           | 2010 | 69.5 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Poland                                           | 2010 | 53.6 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Romania                                          | 2010 | 29.8 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Serbia                                           | 2010 | 31.4 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | Turkey                                           | 2010 | 19.0 |\n",
       "| Participation rate (%) | Males | Food management except dish washing | All ISCED 1997 levels | United Kingdom                                   | 2010 | 56.0 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Austria                                          | 2010 | 24.5 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Belgium                                          | 2010 | 10.9 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 28.9 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Estonia                                          | 2010 | 18.3 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Greece                                           | 2010 |  9.2 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Spain                                            | 2010 | 18.8 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Finland                                          | 2010 | 23.9 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | France                                           | 2010 | 25.5 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Hungary                                          | 2010 | 16.4 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Italy                                            | 2010 | 16.8 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Luxembourg                                       | 2010 | 10.8 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Netherlands                                      | 2010 | 22.6 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Norway                                           | 2010 | 35.7 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Poland                                           | 2010 | 25.9 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Romania                                          | 2010 | 16.4 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Serbia                                           | 2010 | 16.5 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | Turkey                                           | 2010 |  7.0 |\n",
       "| Participation rate (%) | Males | Cleaning dwelling                   | All ISCED 1997 levels | United Kingdom                                   | 2010 | 20.2 |\n",
       "\n"
      ],
      "text/plain": [
       "       unit                   sex     acl00                              \n",
       "1      Participation rate (%) Females Food management except dish washing\n",
       "2      Participation rate (%) Females Food management except dish washing\n",
       "3      Participation rate (%) Females Food management except dish washing\n",
       "4      Participation rate (%) Females Food management except dish washing\n",
       "5      Participation rate (%) Females Food management except dish washing\n",
       "6      Participation rate (%) Females Food management except dish washing\n",
       "7      Participation rate (%) Females Food management except dish washing\n",
       "8      Participation rate (%) Females Food management except dish washing\n",
       "9      Participation rate (%) Females Food management except dish washing\n",
       "10     Participation rate (%) Females Food management except dish washing\n",
       "11     Participation rate (%) Females Food management except dish washing\n",
       "12     Participation rate (%) Females Food management except dish washing\n",
       "13     Participation rate (%) Females Food management except dish washing\n",
       "14     Participation rate (%) Females Food management except dish washing\n",
       "15     Participation rate (%) Females Food management except dish washing\n",
       "16     Participation rate (%) Females Food management except dish washing\n",
       "17     Participation rate (%) Females Food management except dish washing\n",
       "18     Participation rate (%) Females Food management except dish washing\n",
       "19     Participation rate (%) Females Cleaning dwelling                  \n",
       "20     Participation rate (%) Females Cleaning dwelling                  \n",
       "21     Participation rate (%) Females Cleaning dwelling                  \n",
       "22     Participation rate (%) Females Cleaning dwelling                  \n",
       "23     Participation rate (%) Females Cleaning dwelling                  \n",
       "24     Participation rate (%) Females Cleaning dwelling                  \n",
       "25     Participation rate (%) Females Cleaning dwelling                  \n",
       "26     Participation rate (%) Females Cleaning dwelling                  \n",
       "27     Participation rate (%) Females Cleaning dwelling                  \n",
       "28     Participation rate (%) Females Cleaning dwelling                  \n",
       "29     Participation rate (%) Females Cleaning dwelling                  \n",
       "30     Participation rate (%) Females Cleaning dwelling                  \n",
       "<U+22EE> <U+22EE>               <U+22EE> <U+22EE>                           \n",
       "43     Participation rate (%) Males   Food management except dish washing\n",
       "44     Participation rate (%) Males   Food management except dish washing\n",
       "45     Participation rate (%) Males   Food management except dish washing\n",
       "46     Participation rate (%) Males   Food management except dish washing\n",
       "47     Participation rate (%) Males   Food management except dish washing\n",
       "48     Participation rate (%) Males   Food management except dish washing\n",
       "49     Participation rate (%) Males   Food management except dish washing\n",
       "50     Participation rate (%) Males   Food management except dish washing\n",
       "51     Participation rate (%) Males   Food management except dish washing\n",
       "52     Participation rate (%) Males   Food management except dish washing\n",
       "53     Participation rate (%) Males   Food management except dish washing\n",
       "54     Participation rate (%) Males   Food management except dish washing\n",
       "55     Participation rate (%) Males   Cleaning dwelling                  \n",
       "56     Participation rate (%) Males   Cleaning dwelling                  \n",
       "57     Participation rate (%) Males   Cleaning dwelling                  \n",
       "58     Participation rate (%) Males   Cleaning dwelling                  \n",
       "59     Participation rate (%) Males   Cleaning dwelling                  \n",
       "60     Participation rate (%) Males   Cleaning dwelling                  \n",
       "61     Participation rate (%) Males   Cleaning dwelling                  \n",
       "62     Participation rate (%) Males   Cleaning dwelling                  \n",
       "63     Participation rate (%) Males   Cleaning dwelling                  \n",
       "64     Participation rate (%) Males   Cleaning dwelling                  \n",
       "65     Participation rate (%) Males   Cleaning dwelling                  \n",
       "66     Participation rate (%) Males   Cleaning dwelling                  \n",
       "67     Participation rate (%) Males   Cleaning dwelling                  \n",
       "68     Participation rate (%) Males   Cleaning dwelling                  \n",
       "69     Participation rate (%) Males   Cleaning dwelling                  \n",
       "70     Participation rate (%) Males   Cleaning dwelling                  \n",
       "71     Participation rate (%) Males   Cleaning dwelling                  \n",
       "72     Participation rate (%) Males   Cleaning dwelling                  \n",
       "       isced97               geo                                             \n",
       "1      All ISCED 1997 levels Austria                                         \n",
       "2      All ISCED 1997 levels Belgium                                         \n",
       "3      All ISCED 1997 levels Germany (until 1990 former territory of the FRG)\n",
       "4      All ISCED 1997 levels Estonia                                         \n",
       "5      All ISCED 1997 levels Greece                                          \n",
       "6      All ISCED 1997 levels Spain                                           \n",
       "7      All ISCED 1997 levels Finland                                         \n",
       "8      All ISCED 1997 levels France                                          \n",
       "9      All ISCED 1997 levels Hungary                                         \n",
       "10     All ISCED 1997 levels Italy                                           \n",
       "11     All ISCED 1997 levels Luxembourg                                      \n",
       "12     All ISCED 1997 levels Netherlands                                     \n",
       "13     All ISCED 1997 levels Norway                                          \n",
       "14     All ISCED 1997 levels Poland                                          \n",
       "15     All ISCED 1997 levels Romania                                         \n",
       "16     All ISCED 1997 levels Serbia                                          \n",
       "17     All ISCED 1997 levels Turkey                                          \n",
       "18     All ISCED 1997 levels United Kingdom                                  \n",
       "19     All ISCED 1997 levels Austria                                         \n",
       "20     All ISCED 1997 levels Belgium                                         \n",
       "21     All ISCED 1997 levels Germany (until 1990 former territory of the FRG)\n",
       "22     All ISCED 1997 levels Estonia                                         \n",
       "23     All ISCED 1997 levels Greece                                          \n",
       "24     All ISCED 1997 levels Spain                                           \n",
       "25     All ISCED 1997 levels Finland                                         \n",
       "26     All ISCED 1997 levels France                                          \n",
       "27     All ISCED 1997 levels Hungary                                         \n",
       "28     All ISCED 1997 levels Italy                                           \n",
       "29     All ISCED 1997 levels Luxembourg                                      \n",
       "30     All ISCED 1997 levels Netherlands                                     \n",
       "<U+22EE> <U+22EE>              <U+22EE>                                        \n",
       "43     All ISCED 1997 levels Finland                                         \n",
       "44     All ISCED 1997 levels France                                          \n",
       "45     All ISCED 1997 levels Hungary                                         \n",
       "46     All ISCED 1997 levels Italy                                           \n",
       "47     All ISCED 1997 levels Luxembourg                                      \n",
       "48     All ISCED 1997 levels Netherlands                                     \n",
       "49     All ISCED 1997 levels Norway                                          \n",
       "50     All ISCED 1997 levels Poland                                          \n",
       "51     All ISCED 1997 levels Romania                                         \n",
       "52     All ISCED 1997 levels Serbia                                          \n",
       "53     All ISCED 1997 levels Turkey                                          \n",
       "54     All ISCED 1997 levels United Kingdom                                  \n",
       "55     All ISCED 1997 levels Austria                                         \n",
       "56     All ISCED 1997 levels Belgium                                         \n",
       "57     All ISCED 1997 levels Germany (until 1990 former territory of the FRG)\n",
       "58     All ISCED 1997 levels Estonia                                         \n",
       "59     All ISCED 1997 levels Greece                                          \n",
       "60     All ISCED 1997 levels Spain                                           \n",
       "61     All ISCED 1997 levels Finland                                         \n",
       "62     All ISCED 1997 levels France                                          \n",
       "63     All ISCED 1997 levels Hungary                                         \n",
       "64     All ISCED 1997 levels Italy                                           \n",
       "65     All ISCED 1997 levels Luxembourg                                      \n",
       "66     All ISCED 1997 levels Netherlands                                     \n",
       "67     All ISCED 1997 levels Norway                                          \n",
       "68     All ISCED 1997 levels Poland                                          \n",
       "69     All ISCED 1997 levels Romania                                         \n",
       "70     All ISCED 1997 levels Serbia                                          \n",
       "71     All ISCED 1997 levels Turkey                                          \n",
       "72     All ISCED 1997 levels United Kingdom                                  \n",
       "       time   values\n",
       "1      2010   81.1  \n",
       "2      2010   77.0  \n",
       "3      2010   76.6  \n",
       "4      2010   85.0  \n",
       "5      2010   85.5  \n",
       "6      2010   81.3  \n",
       "7      2010   84.4  \n",
       "8      2010   79.7  \n",
       "9      2010   85.1  \n",
       "10     2010   87.9  \n",
       "11     2010   78.4  \n",
       "12     2010   78.5  \n",
       "13     2010   86.2  \n",
       "14     2010   90.9  \n",
       "15     2010   86.9  \n",
       "16     2010   88.5  \n",
       "17     2010   88.1  \n",
       "18     2010   80.4  \n",
       "19     2010   68.6  \n",
       "20     2010   30.9  \n",
       "21     2010   59.8  \n",
       "22     2010   50.3  \n",
       "23     2010   62.1  \n",
       "24     2010   56.0  \n",
       "25     2010   51.8  \n",
       "26     2010   65.6  \n",
       "27     2010   54.1  \n",
       "28     2010   79.8  \n",
       "29     2010   33.7  \n",
       "30     2010   51.6  \n",
       "<U+22EE> <U+22EE> <U+22EE>\n",
       "43     2010   60.9  \n",
       "44     2010   43.2  \n",
       "45     2010   43.3  \n",
       "46     2010   40.5  \n",
       "47     2010   42.3  \n",
       "48     2010   52.0  \n",
       "49     2010   69.5  \n",
       "50     2010   53.6  \n",
       "51     2010   29.8  \n",
       "52     2010   31.4  \n",
       "53     2010   19.0  \n",
       "54     2010   56.0  \n",
       "55     2010   24.5  \n",
       "56     2010   10.9  \n",
       "57     2010   28.9  \n",
       "58     2010   18.3  \n",
       "59     2010    9.2  \n",
       "60     2010   18.8  \n",
       "61     2010   23.9  \n",
       "62     2010   25.5  \n",
       "63     2010   16.4  \n",
       "64     2010   16.8  \n",
       "65     2010   10.8  \n",
       "66     2010   22.6  \n",
       "67     2010   35.7  \n",
       "68     2010   25.9  \n",
       "69     2010   16.4  \n",
       "70     2010   16.5  \n",
       "71     2010    7.0  \n",
       "72     2010   20.2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ\",filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^food|^clean\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we keep only the columns with sex, activities, countries and values. Before plotting the values we need to merge the columns sex and activities and cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 72 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>values</th><th scope=col>bd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td>81.1</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>77.0</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Germany       </td><td>76.6</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>85.0</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Greece        </td><td>85.5</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Spain         </td><td>81.3</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Finland       </td><td>84.4</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>France        </td><td>79.7</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>85.1</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Italy         </td><td>87.9</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>78.4</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>78.5</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Norway        </td><td>86.2</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Poland        </td><td>90.9</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Romania       </td><td>86.9</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>88.5</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>88.1</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>80.4</td><td>Food management except dish washing, females</td></tr>\n",
       "\t<tr><td>Austria       </td><td>68.6</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Belgium       </td><td>30.9</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Germany       </td><td>59.8</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Estonia       </td><td>50.3</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Greece        </td><td>62.1</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Spain         </td><td>56.0</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Finland       </td><td>51.8</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>France        </td><td>65.6</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Hungary       </td><td>54.1</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Italy         </td><td>79.8</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>33.7</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>51.6</td><td>Cleaning dwelling, females                  </td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Finland       </td><td>60.9</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>France        </td><td>43.2</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>43.3</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Italy         </td><td>40.5</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>42.3</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>52.0</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Norway        </td><td>69.5</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Poland        </td><td>53.6</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Romania       </td><td>29.8</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>31.4</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>19.0</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>56.0</td><td>Food management except dish washing, males</td></tr>\n",
       "\t<tr><td>Austria       </td><td>24.5</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Belgium       </td><td>10.9</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Germany       </td><td>28.9</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Estonia       </td><td>18.3</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Greece        </td><td> 9.2</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Spain         </td><td>18.8</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Finland       </td><td>23.9</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>France        </td><td>25.5</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Hungary       </td><td>16.4</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Italy         </td><td>16.8</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>10.8</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>22.6</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Norway        </td><td>35.7</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Poland        </td><td>25.9</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Romania       </td><td>16.4</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Serbia        </td><td>16.5</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 7.0</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>20.2</td><td>Cleaning dwelling, males                  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 72 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & values & bd\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Austria        & 81.1 & Food management except dish washing, females\\\\\n",
       "\t Belgium        & 77.0 & Food management except dish washing, females\\\\\n",
       "\t Germany        & 76.6 & Food management except dish washing, females\\\\\n",
       "\t Estonia        & 85.0 & Food management except dish washing, females\\\\\n",
       "\t Greece         & 85.5 & Food management except dish washing, females\\\\\n",
       "\t Spain          & 81.3 & Food management except dish washing, females\\\\\n",
       "\t Finland        & 84.4 & Food management except dish washing, females\\\\\n",
       "\t France         & 79.7 & Food management except dish washing, females\\\\\n",
       "\t Hungary        & 85.1 & Food management except dish washing, females\\\\\n",
       "\t Italy          & 87.9 & Food management except dish washing, females\\\\\n",
       "\t Luxembourg     & 78.4 & Food management except dish washing, females\\\\\n",
       "\t Netherlands    & 78.5 & Food management except dish washing, females\\\\\n",
       "\t Norway         & 86.2 & Food management except dish washing, females\\\\\n",
       "\t Poland         & 90.9 & Food management except dish washing, females\\\\\n",
       "\t Romania        & 86.9 & Food management except dish washing, females\\\\\n",
       "\t Serbia         & 88.5 & Food management except dish washing, females\\\\\n",
       "\t Turkey         & 88.1 & Food management except dish washing, females\\\\\n",
       "\t United Kingdom & 80.4 & Food management except dish washing, females\\\\\n",
       "\t Austria        & 68.6 & Cleaning dwelling, females                  \\\\\n",
       "\t Belgium        & 30.9 & Cleaning dwelling, females                  \\\\\n",
       "\t Germany        & 59.8 & Cleaning dwelling, females                  \\\\\n",
       "\t Estonia        & 50.3 & Cleaning dwelling, females                  \\\\\n",
       "\t Greece         & 62.1 & Cleaning dwelling, females                  \\\\\n",
       "\t Spain          & 56.0 & Cleaning dwelling, females                  \\\\\n",
       "\t Finland        & 51.8 & Cleaning dwelling, females                  \\\\\n",
       "\t France         & 65.6 & Cleaning dwelling, females                  \\\\\n",
       "\t Hungary        & 54.1 & Cleaning dwelling, females                  \\\\\n",
       "\t Italy          & 79.8 & Cleaning dwelling, females                  \\\\\n",
       "\t Luxembourg     & 33.7 & Cleaning dwelling, females                  \\\\\n",
       "\t Netherlands    & 51.6 & Cleaning dwelling, females                  \\\\\n",
       "\t ⋮ & ⋮ & ⋮\\\\\n",
       "\t Finland        & 60.9 & Food management except dish washing, males\\\\\n",
       "\t France         & 43.2 & Food management except dish washing, males\\\\\n",
       "\t Hungary        & 43.3 & Food management except dish washing, males\\\\\n",
       "\t Italy          & 40.5 & Food management except dish washing, males\\\\\n",
       "\t Luxembourg     & 42.3 & Food management except dish washing, males\\\\\n",
       "\t Netherlands    & 52.0 & Food management except dish washing, males\\\\\n",
       "\t Norway         & 69.5 & Food management except dish washing, males\\\\\n",
       "\t Poland         & 53.6 & Food management except dish washing, males\\\\\n",
       "\t Romania        & 29.8 & Food management except dish washing, males\\\\\n",
       "\t Serbia         & 31.4 & Food management except dish washing, males\\\\\n",
       "\t Turkey         & 19.0 & Food management except dish washing, males\\\\\n",
       "\t United Kingdom & 56.0 & Food management except dish washing, males\\\\\n",
       "\t Austria        & 24.5 & Cleaning dwelling, males                  \\\\\n",
       "\t Belgium        & 10.9 & Cleaning dwelling, males                  \\\\\n",
       "\t Germany        & 28.9 & Cleaning dwelling, males                  \\\\\n",
       "\t Estonia        & 18.3 & Cleaning dwelling, males                  \\\\\n",
       "\t Greece         &  9.2 & Cleaning dwelling, males                  \\\\\n",
       "\t Spain          & 18.8 & Cleaning dwelling, males                  \\\\\n",
       "\t Finland        & 23.9 & Cleaning dwelling, males                  \\\\\n",
       "\t France         & 25.5 & Cleaning dwelling, males                  \\\\\n",
       "\t Hungary        & 16.4 & Cleaning dwelling, males                  \\\\\n",
       "\t Italy          & 16.8 & Cleaning dwelling, males                  \\\\\n",
       "\t Luxembourg     & 10.8 & Cleaning dwelling, males                  \\\\\n",
       "\t Netherlands    & 22.6 & Cleaning dwelling, males                  \\\\\n",
       "\t Norway         & 35.7 & Cleaning dwelling, males                  \\\\\n",
       "\t Poland         & 25.9 & Cleaning dwelling, males                  \\\\\n",
       "\t Romania        & 16.4 & Cleaning dwelling, males                  \\\\\n",
       "\t Serbia         & 16.5 & Cleaning dwelling, males                  \\\\\n",
       "\t Turkey         &  7.0 & Cleaning dwelling, males                  \\\\\n",
       "\t United Kingdom & 20.2 & Cleaning dwelling, males                  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 72 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | values &lt;dbl&gt; | bd &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Austria        | 81.1 | Food management except dish washing, females |\n",
       "| Belgium        | 77.0 | Food management except dish washing, females |\n",
       "| Germany        | 76.6 | Food management except dish washing, females |\n",
       "| Estonia        | 85.0 | Food management except dish washing, females |\n",
       "| Greece         | 85.5 | Food management except dish washing, females |\n",
       "| Spain          | 81.3 | Food management except dish washing, females |\n",
       "| Finland        | 84.4 | Food management except dish washing, females |\n",
       "| France         | 79.7 | Food management except dish washing, females |\n",
       "| Hungary        | 85.1 | Food management except dish washing, females |\n",
       "| Italy          | 87.9 | Food management except dish washing, females |\n",
       "| Luxembourg     | 78.4 | Food management except dish washing, females |\n",
       "| Netherlands    | 78.5 | Food management except dish washing, females |\n",
       "| Norway         | 86.2 | Food management except dish washing, females |\n",
       "| Poland         | 90.9 | Food management except dish washing, females |\n",
       "| Romania        | 86.9 | Food management except dish washing, females |\n",
       "| Serbia         | 88.5 | Food management except dish washing, females |\n",
       "| Turkey         | 88.1 | Food management except dish washing, females |\n",
       "| United Kingdom | 80.4 | Food management except dish washing, females |\n",
       "| Austria        | 68.6 | Cleaning dwelling, females                   |\n",
       "| Belgium        | 30.9 | Cleaning dwelling, females                   |\n",
       "| Germany        | 59.8 | Cleaning dwelling, females                   |\n",
       "| Estonia        | 50.3 | Cleaning dwelling, females                   |\n",
       "| Greece         | 62.1 | Cleaning dwelling, females                   |\n",
       "| Spain          | 56.0 | Cleaning dwelling, females                   |\n",
       "| Finland        | 51.8 | Cleaning dwelling, females                   |\n",
       "| France         | 65.6 | Cleaning dwelling, females                   |\n",
       "| Hungary        | 54.1 | Cleaning dwelling, females                   |\n",
       "| Italy          | 79.8 | Cleaning dwelling, females                   |\n",
       "| Luxembourg     | 33.7 | Cleaning dwelling, females                   |\n",
       "| Netherlands    | 51.6 | Cleaning dwelling, females                   |\n",
       "| ⋮ | ⋮ | ⋮ |\n",
       "| Finland        | 60.9 | Food management except dish washing, males |\n",
       "| France         | 43.2 | Food management except dish washing, males |\n",
       "| Hungary        | 43.3 | Food management except dish washing, males |\n",
       "| Italy          | 40.5 | Food management except dish washing, males |\n",
       "| Luxembourg     | 42.3 | Food management except dish washing, males |\n",
       "| Netherlands    | 52.0 | Food management except dish washing, males |\n",
       "| Norway         | 69.5 | Food management except dish washing, males |\n",
       "| Poland         | 53.6 | Food management except dish washing, males |\n",
       "| Romania        | 29.8 | Food management except dish washing, males |\n",
       "| Serbia         | 31.4 | Food management except dish washing, males |\n",
       "| Turkey         | 19.0 | Food management except dish washing, males |\n",
       "| United Kingdom | 56.0 | Food management except dish washing, males |\n",
       "| Austria        | 24.5 | Cleaning dwelling, males                   |\n",
       "| Belgium        | 10.9 | Cleaning dwelling, males                   |\n",
       "| Germany        | 28.9 | Cleaning dwelling, males                   |\n",
       "| Estonia        | 18.3 | Cleaning dwelling, males                   |\n",
       "| Greece         |  9.2 | Cleaning dwelling, males                   |\n",
       "| Spain          | 18.8 | Cleaning dwelling, males                   |\n",
       "| Finland        | 23.9 | Cleaning dwelling, males                   |\n",
       "| France         | 25.5 | Cleaning dwelling, males                   |\n",
       "| Hungary        | 16.4 | Cleaning dwelling, males                   |\n",
       "| Italy          | 16.8 | Cleaning dwelling, males                   |\n",
       "| Luxembourg     | 10.8 | Cleaning dwelling, males                   |\n",
       "| Netherlands    | 22.6 | Cleaning dwelling, males                   |\n",
       "| Norway         | 35.7 | Cleaning dwelling, males                   |\n",
       "| Poland         | 25.9 | Cleaning dwelling, males                   |\n",
       "| Romania        | 16.4 | Cleaning dwelling, males                   |\n",
       "| Serbia         | 16.5 | Cleaning dwelling, males                   |\n",
       "| Turkey         |  7.0 | Cleaning dwelling, males                   |\n",
       "| United Kingdom | 20.2 | Cleaning dwelling, males                   |\n",
       "\n"
      ],
      "text/plain": [
       "       geo            values bd                                          \n",
       "1      Austria        81.1   Food management except dish washing, females\n",
       "2      Belgium        77.0   Food management except dish washing, females\n",
       "3      Germany        76.6   Food management except dish washing, females\n",
       "4      Estonia        85.0   Food management except dish washing, females\n",
       "5      Greece         85.5   Food management except dish washing, females\n",
       "6      Spain          81.3   Food management except dish washing, females\n",
       "7      Finland        84.4   Food management except dish washing, females\n",
       "8      France         79.7   Food management except dish washing, females\n",
       "9      Hungary        85.1   Food management except dish washing, females\n",
       "10     Italy          87.9   Food management except dish washing, females\n",
       "11     Luxembourg     78.4   Food management except dish washing, females\n",
       "12     Netherlands    78.5   Food management except dish washing, females\n",
       "13     Norway         86.2   Food management except dish washing, females\n",
       "14     Poland         90.9   Food management except dish washing, females\n",
       "15     Romania        86.9   Food management except dish washing, females\n",
       "16     Serbia         88.5   Food management except dish washing, females\n",
       "17     Turkey         88.1   Food management except dish washing, females\n",
       "18     United Kingdom 80.4   Food management except dish washing, females\n",
       "19     Austria        68.6   Cleaning dwelling, females                  \n",
       "20     Belgium        30.9   Cleaning dwelling, females                  \n",
       "21     Germany        59.8   Cleaning dwelling, females                  \n",
       "22     Estonia        50.3   Cleaning dwelling, females                  \n",
       "23     Greece         62.1   Cleaning dwelling, females                  \n",
       "24     Spain          56.0   Cleaning dwelling, females                  \n",
       "25     Finland        51.8   Cleaning dwelling, females                  \n",
       "26     France         65.6   Cleaning dwelling, females                  \n",
       "27     Hungary        54.1   Cleaning dwelling, females                  \n",
       "28     Italy          79.8   Cleaning dwelling, females                  \n",
       "29     Luxembourg     33.7   Cleaning dwelling, females                  \n",
       "30     Netherlands    51.6   Cleaning dwelling, females                  \n",
       "<U+22EE> <U+22EE>       <U+22EE> <U+22EE>                                    \n",
       "43     Finland        60.9   Food management except dish washing, males  \n",
       "44     France         43.2   Food management except dish washing, males  \n",
       "45     Hungary        43.3   Food management except dish washing, males  \n",
       "46     Italy          40.5   Food management except dish washing, males  \n",
       "47     Luxembourg     42.3   Food management except dish washing, males  \n",
       "48     Netherlands    52.0   Food management except dish washing, males  \n",
       "49     Norway         69.5   Food management except dish washing, males  \n",
       "50     Poland         53.6   Food management except dish washing, males  \n",
       "51     Romania        29.8   Food management except dish washing, males  \n",
       "52     Serbia         31.4   Food management except dish washing, males  \n",
       "53     Turkey         19.0   Food management except dish washing, males  \n",
       "54     United Kingdom 56.0   Food management except dish washing, males  \n",
       "55     Austria        24.5   Cleaning dwelling, males                    \n",
       "56     Belgium        10.9   Cleaning dwelling, males                    \n",
       "57     Germany        28.9   Cleaning dwelling, males                    \n",
       "58     Estonia        18.3   Cleaning dwelling, males                    \n",
       "59     Greece          9.2   Cleaning dwelling, males                    \n",
       "60     Spain          18.8   Cleaning dwelling, males                    \n",
       "61     Finland        23.9   Cleaning dwelling, males                    \n",
       "62     France         25.5   Cleaning dwelling, males                    \n",
       "63     Hungary        16.4   Cleaning dwelling, males                    \n",
       "64     Italy          16.8   Cleaning dwelling, males                    \n",
       "65     Luxembourg     10.8   Cleaning dwelling, males                    \n",
       "66     Netherlands    22.6   Cleaning dwelling, males                    \n",
       "67     Norway         35.7   Cleaning dwelling, males                    \n",
       "68     Poland         25.9   Cleaning dwelling, males                    \n",
       "69     Romania        16.4   Cleaning dwelling, males                    \n",
       "70     Serbia         16.5   Cleaning dwelling, males                    \n",
       "71     Turkey          7.0   Cleaning dwelling, males                    \n",
       "72     United Kingdom 20.2   Cleaning dwelling, males                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "dt<-dt[,c(\"sex\",\"acl00\",\"geo\",\"values\")]\n",
    "dt[,bd:=paste0(acl00,\", \",tolower(sex))][,c(\"acl00\",\"sex\"):=NULL]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Food management except dish washing, females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(bd=c(\"Cleaning dwelling, males\",\"Cleaning dwelling, males\"),geo=c(\" \",\"  \"),values=c(NA,NA))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Food management except dish washing, females\",bd)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Turkey','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "bd_ord<-sort(unique(dt$bd),decreasing=TRUE)\n",
    "dt$bd<-factor(dt$bd,levels=bd_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
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6WR+j+yosLzJ+njrk3VKJ7vdwd1pvz3h0DSFrBwnbnplJ63xyjeXDvUinxi6eA3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\nb6vfGTEjnSpkdts+Z6t+i6UuE8o4UCQmqjZt01GCAEOVx6AJQyo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iB60dm+pLyf4AGJqYTqgPUgLdLOxIeTp3ztNk5e8eOw64SzUDZkfH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kSw55RnubN28u3l9Ikx7AbVLP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\nMx+3o+JhJT1sACtcLU2RM96fvqVwT12tq3+TpvVq16p6KyXp/Kk/z6f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45OdeqrKyX/n0Y2lG5PkAV106QWPF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\nDAe5YjhocqI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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5_colors<-c(\"#FAA519\",\"#FCC975\",\"#286EB4\",\"#71A8DF\")\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt, aes(fill=bd),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  scale_fill_manual(values = fig5_colors)+\n",
    "  scale_y_continuous(breaks=seq(0,100,10)) +\n",
    "  ggtitle(\"Figure 5: Participation rate per day in cleaning and food management, by gender, % (2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 6: Participation rate per day in laundry and ironing, by gender, % (2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00educ* dataset as in Figure 5. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^iron|^laund\",sex=\"male\",isced97=\"^all\")`. This time we can use again the SDMX REST API to get the values are it is numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 72 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>isced97</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>32.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>23.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>30.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>13.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>27.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>27.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>33.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>19.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>28.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>22.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>26.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>34.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>39.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>23.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>30.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>29.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>18.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>30.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>28.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>24.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>13.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td> 7.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>26.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>17.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td> 8.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>17.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>13.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>24.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>29.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td> 9.7</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td> 7.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td> 2.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td> 2.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td> 1.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td> 3.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td> 6.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>11.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td> 2.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td> 2.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td> 2.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 0.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Laundry</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td> 7.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td> 3.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td> 2.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td> 1.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td> 1.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td> 1.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td> 1.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td> 1.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td> 1.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td> 0.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td> 0.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td> 7.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td> 1.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td> 1.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td> 1.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td> 1.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td> 0.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 0.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Ironing</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td> 3.3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 72 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & isced97 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Austria                                          & 2010 & 32.8\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Belgium                                          & 2010 & 23.5\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 30.1\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Estonia                                          & 2010 & 13.8\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Greece                                           & 2010 & 27.8\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Spain                                            & 2010 & 27.8\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Finland                                          & 2010 & 33.5\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & France                                           & 2010 & 19.3\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Hungary                                          & 2010 & 28.7\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Italy                                            & 2010 & 22.7\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Luxembourg                                       & 2010 & 26.0\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Netherlands                                      & 2010 & 34.8\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Norway                                           & 2010 & 39.5\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Poland                                           & 2010 & 23.2\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Romania                                          & 2010 & 30.2\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Serbia                                           & 2010 & 29.1\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & Turkey                                           & 2010 & 18.7\\\\\n",
       "\t Participation rate (\\%) & Females & Laundry & All ISCED 1997 levels & United Kingdom                                   & 2010 & 30.0\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Austria                                          & 2010 & 28.3\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Belgium                                          & 2010 & 24.2\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 13.4\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Estonia                                          & 2010 &  7.8\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Greece                                           & 2010 & 26.7\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Spain                                            & 2010 & 17.0\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Finland                                          & 2010 &  8.1\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & France                                           & 2010 & 17.5\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Hungary                                          & 2010 & 13.8\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Italy                                            & 2010 & 24.6\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Luxembourg                                       & 2010 & 29.1\\\\\n",
       "\t Participation rate (\\%) & Females & Ironing & All ISCED 1997 levels & Netherlands                                      & 2010 &  9.7\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Finland                                          & 2010 &  7.7\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & France                                           & 2010 &  2.7\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Hungary                                          & 2010 &  2.4\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Italy                                            & 2010 &  1.1\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Luxembourg                                       & 2010 &  3.9\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Netherlands                                      & 2010 &  6.5\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Norway                                           & 2010 & 11.5\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Poland                                           & 2010 &  2.8\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Romania                                          & 2010 &  2.3\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Serbia                                           & 2010 &  2.1\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & Turkey                                           & 2010 &  0.6\\\\\n",
       "\t Participation rate (\\%) & Males & Laundry & All ISCED 1997 levels & United Kingdom                                   & 2010 &  7.0\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Austria                                          & 2010 &  3.6\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Belgium                                          & 2010 &  2.8\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 &  1.9\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Estonia                                          & 2010 &  1.0\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Greece                                           & 2010 &  1.0\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Spain                                            & 2010 &  1.1\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Finland                                          & 2010 &  1.4\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & France                                           & 2010 &  1.6\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Hungary                                          & 2010 &  0.1\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Italy                                            & 2010 &  0.4\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Luxembourg                                       & 2010 &  7.9\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Netherlands                                      & 2010 &  1.4\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Norway                                           & 2010 &  1.5\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Poland                                           & 2010 &  1.7\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Romania                                          & 2010 &  1.2\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Serbia                                           & 2010 &  0.6\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & Turkey                                           & 2010 &  0.5\\\\\n",
       "\t Participation rate (\\%) & Males & Ironing & All ISCED 1997 levels & United Kingdom                                   & 2010 &  3.3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 72 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | isced97 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Austria                                          | 2010 | 32.8 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Belgium                                          | 2010 | 23.5 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 30.1 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Estonia                                          | 2010 | 13.8 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Greece                                           | 2010 | 27.8 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Spain                                            | 2010 | 27.8 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Finland                                          | 2010 | 33.5 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | France                                           | 2010 | 19.3 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Hungary                                          | 2010 | 28.7 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Italy                                            | 2010 | 22.7 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Luxembourg                                       | 2010 | 26.0 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Netherlands                                      | 2010 | 34.8 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Norway                                           | 2010 | 39.5 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Poland                                           | 2010 | 23.2 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Romania                                          | 2010 | 30.2 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Serbia                                           | 2010 | 29.1 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | Turkey                                           | 2010 | 18.7 |\n",
       "| Participation rate (%) | Females | Laundry | All ISCED 1997 levels | United Kingdom                                   | 2010 | 30.0 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Austria                                          | 2010 | 28.3 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Belgium                                          | 2010 | 24.2 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 13.4 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Estonia                                          | 2010 |  7.8 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Greece                                           | 2010 | 26.7 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Spain                                            | 2010 | 17.0 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Finland                                          | 2010 |  8.1 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | France                                           | 2010 | 17.5 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Hungary                                          | 2010 | 13.8 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Italy                                            | 2010 | 24.6 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Luxembourg                                       | 2010 | 29.1 |\n",
       "| Participation rate (%) | Females | Ironing | All ISCED 1997 levels | Netherlands                                      | 2010 |  9.7 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Finland                                          | 2010 |  7.7 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | France                                           | 2010 |  2.7 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Hungary                                          | 2010 |  2.4 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Italy                                            | 2010 |  1.1 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Luxembourg                                       | 2010 |  3.9 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Netherlands                                      | 2010 |  6.5 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Norway                                           | 2010 | 11.5 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Poland                                           | 2010 |  2.8 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Romania                                          | 2010 |  2.3 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Serbia                                           | 2010 |  2.1 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | Turkey                                           | 2010 |  0.6 |\n",
       "| Participation rate (%) | Males | Laundry | All ISCED 1997 levels | United Kingdom                                   | 2010 |  7.0 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Austria                                          | 2010 |  3.6 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Belgium                                          | 2010 |  2.8 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 |  1.9 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Estonia                                          | 2010 |  1.0 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Greece                                           | 2010 |  1.0 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Spain                                            | 2010 |  1.1 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Finland                                          | 2010 |  1.4 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | France                                           | 2010 |  1.6 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Hungary                                          | 2010 |  0.1 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Italy                                            | 2010 |  0.4 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Luxembourg                                       | 2010 |  7.9 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Netherlands                                      | 2010 |  1.4 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Norway                                           | 2010 |  1.5 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Poland                                           | 2010 |  1.7 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Romania                                          | 2010 |  1.2 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Serbia                                           | 2010 |  0.6 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | Turkey                                           | 2010 |  0.5 |\n",
       "| Participation rate (%) | Males | Ironing | All ISCED 1997 levels | United Kingdom                                   | 2010 |  3.3 |\n",
       "\n"
      ],
      "text/plain": [
       "       unit                   sex     acl00   isced97              \n",
       "1      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "2      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "3      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "4      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "5      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "6      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "7      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "8      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "9      Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "10     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "11     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "12     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "13     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "14     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "15     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "16     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "17     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "18     Participation rate (%) Females Laundry All ISCED 1997 levels\n",
       "19     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "20     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "21     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "22     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "23     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "24     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "25     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "26     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "27     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "28     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "29     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "30     Participation rate (%) Females Ironing All ISCED 1997 levels\n",
       "<U+22EE> <U+22EE>               <U+22EE> <U+22EE> <U+22EE>             \n",
       "43     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "44     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "45     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "46     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "47     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "48     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "49     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "50     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "51     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "52     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "53     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "54     Participation rate (%) Males   Laundry All ISCED 1997 levels\n",
       "55     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "56     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "57     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "58     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "59     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "60     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "61     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "62     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "63     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "64     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "65     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "66     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "67     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "68     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "69     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "70     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "71     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "72     Participation rate (%) Males   Ironing All ISCED 1997 levels\n",
       "       geo                                              time   values\n",
       "1      Austria                                          2010   32.8  \n",
       "2      Belgium                                          2010   23.5  \n",
       "3      Germany (until 1990 former territory of the FRG) 2010   30.1  \n",
       "4      Estonia                                          2010   13.8  \n",
       "5      Greece                                           2010   27.8  \n",
       "6      Spain                                            2010   27.8  \n",
       "7      Finland                                          2010   33.5  \n",
       "8      France                                           2010   19.3  \n",
       "9      Hungary                                          2010   28.7  \n",
       "10     Italy                                            2010   22.7  \n",
       "11     Luxembourg                                       2010   26.0  \n",
       "12     Netherlands                                      2010   34.8  \n",
       "13     Norway                                           2010   39.5  \n",
       "14     Poland                                           2010   23.2  \n",
       "15     Romania                                          2010   30.2  \n",
       "16     Serbia                                           2010   29.1  \n",
       "17     Turkey                                           2010   18.7  \n",
       "18     United Kingdom                                   2010   30.0  \n",
       "19     Austria                                          2010   28.3  \n",
       "20     Belgium                                          2010   24.2  \n",
       "21     Germany (until 1990 former territory of the FRG) 2010   13.4  \n",
       "22     Estonia                                          2010    7.8  \n",
       "23     Greece                                           2010   26.7  \n",
       "24     Spain                                            2010   17.0  \n",
       "25     Finland                                          2010    8.1  \n",
       "26     France                                           2010   17.5  \n",
       "27     Hungary                                          2010   13.8  \n",
       "28     Italy                                            2010   24.6  \n",
       "29     Luxembourg                                       2010   29.1  \n",
       "30     Netherlands                                      2010    9.7  \n",
       "<U+22EE> <U+22EE>                                         <U+22EE> <U+22EE>\n",
       "43     Finland                                          2010    7.7  \n",
       "44     France                                           2010    2.7  \n",
       "45     Hungary                                          2010    2.4  \n",
       "46     Italy                                            2010    1.1  \n",
       "47     Luxembourg                                       2010    3.9  \n",
       "48     Netherlands                                      2010    6.5  \n",
       "49     Norway                                           2010   11.5  \n",
       "50     Poland                                           2010    2.8  \n",
       "51     Romania                                          2010    2.3  \n",
       "52     Serbia                                           2010    2.1  \n",
       "53     Turkey                                           2010    0.6  \n",
       "54     United Kingdom                                   2010    7.0  \n",
       "55     Austria                                          2010    3.6  \n",
       "56     Belgium                                          2010    2.8  \n",
       "57     Germany (until 1990 former territory of the FRG) 2010    1.9  \n",
       "58     Estonia                                          2010    1.0  \n",
       "59     Greece                                           2010    1.0  \n",
       "60     Spain                                            2010    1.1  \n",
       "61     Finland                                          2010    1.4  \n",
       "62     France                                           2010    1.6  \n",
       "63     Hungary                                          2010    0.1  \n",
       "64     Italy                                            2010    0.4  \n",
       "65     Luxembourg                                       2010    7.9  \n",
       "66     Netherlands                                      2010    1.4  \n",
       "67     Norway                                           2010    1.5  \n",
       "68     Poland                                           2010    1.7  \n",
       "69     Romania                                          2010    1.2  \n",
       "70     Serbia                                           2010    0.6  \n",
       "71     Turkey                                           2010    0.5  \n",
       "72     United Kingdom                                   2010    3.3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ\",filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^iron|^laund\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we keep only the columns with sex, activities, countries and values. Before plotting the values we need to merge the columns sex and activities and cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 72 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>values</th><th scope=col>bd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td>32.8</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>23.5</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Germany       </td><td>30.1</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>13.8</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Greece        </td><td>27.8</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Spain         </td><td>27.8</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Finland       </td><td>33.5</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>France        </td><td>19.3</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>28.7</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Italy         </td><td>22.7</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>26.0</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>34.8</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Norway        </td><td>39.5</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Poland        </td><td>23.2</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Romania       </td><td>30.2</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>29.1</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>18.7</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>30.0</td><td>Laundry, females</td></tr>\n",
       "\t<tr><td>Austria       </td><td>28.3</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>24.2</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Germany       </td><td>13.4</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Estonia       </td><td> 7.8</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Greece        </td><td>26.7</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Spain         </td><td>17.0</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Finland       </td><td> 8.1</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>France        </td><td>17.5</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>13.8</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Italy         </td><td>24.6</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>29.1</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td> 9.7</td><td>Ironing, females</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Finland       </td><td> 7.7</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>France        </td><td> 2.7</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Hungary       </td><td> 2.4</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Italy         </td><td> 1.1</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td> 3.9</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td> 6.5</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Norway        </td><td>11.5</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Poland        </td><td> 2.8</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Romania       </td><td> 2.3</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Serbia        </td><td> 2.1</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 0.6</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td> 7.0</td><td>Laundry, males</td></tr>\n",
       "\t<tr><td>Austria       </td><td> 3.6</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Belgium       </td><td> 2.8</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Germany       </td><td> 1.9</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Estonia       </td><td> 1.0</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Greece        </td><td> 1.0</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Spain         </td><td> 1.1</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Finland       </td><td> 1.4</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>France        </td><td> 1.6</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Hungary       </td><td> 0.1</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Italy         </td><td> 0.4</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td> 7.9</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td> 1.4</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Norway        </td><td> 1.5</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Poland        </td><td> 1.7</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Romania       </td><td> 1.2</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Serbia        </td><td> 0.6</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 0.5</td><td>Ironing, males</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td> 3.3</td><td>Ironing, males</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 72 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & values & bd\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Austria        & 32.8 & Laundry, females\\\\\n",
       "\t Belgium        & 23.5 & Laundry, females\\\\\n",
       "\t Germany        & 30.1 & Laundry, females\\\\\n",
       "\t Estonia        & 13.8 & Laundry, females\\\\\n",
       "\t Greece         & 27.8 & Laundry, females\\\\\n",
       "\t Spain          & 27.8 & Laundry, females\\\\\n",
       "\t Finland        & 33.5 & Laundry, females\\\\\n",
       "\t France         & 19.3 & Laundry, females\\\\\n",
       "\t Hungary        & 28.7 & Laundry, females\\\\\n",
       "\t Italy          & 22.7 & Laundry, females\\\\\n",
       "\t Luxembourg     & 26.0 & Laundry, females\\\\\n",
       "\t Netherlands    & 34.8 & Laundry, females\\\\\n",
       "\t Norway         & 39.5 & Laundry, females\\\\\n",
       "\t Poland         & 23.2 & Laundry, females\\\\\n",
       "\t Romania        & 30.2 & Laundry, females\\\\\n",
       "\t Serbia         & 29.1 & Laundry, females\\\\\n",
       "\t Turkey         & 18.7 & Laundry, females\\\\\n",
       "\t United Kingdom & 30.0 & Laundry, females\\\\\n",
       "\t Austria        & 28.3 & Ironing, females\\\\\n",
       "\t Belgium        & 24.2 & Ironing, females\\\\\n",
       "\t Germany        & 13.4 & Ironing, females\\\\\n",
       "\t Estonia        &  7.8 & Ironing, females\\\\\n",
       "\t Greece         & 26.7 & Ironing, females\\\\\n",
       "\t Spain          & 17.0 & Ironing, females\\\\\n",
       "\t Finland        &  8.1 & Ironing, females\\\\\n",
       "\t France         & 17.5 & Ironing, females\\\\\n",
       "\t Hungary        & 13.8 & Ironing, females\\\\\n",
       "\t Italy          & 24.6 & Ironing, females\\\\\n",
       "\t Luxembourg     & 29.1 & Ironing, females\\\\\n",
       "\t Netherlands    &  9.7 & Ironing, females\\\\\n",
       "\t ⋮ & ⋮ & ⋮\\\\\n",
       "\t Finland        &  7.7 & Laundry, males\\\\\n",
       "\t France         &  2.7 & Laundry, males\\\\\n",
       "\t Hungary        &  2.4 & Laundry, males\\\\\n",
       "\t Italy          &  1.1 & Laundry, males\\\\\n",
       "\t Luxembourg     &  3.9 & Laundry, males\\\\\n",
       "\t Netherlands    &  6.5 & Laundry, males\\\\\n",
       "\t Norway         & 11.5 & Laundry, males\\\\\n",
       "\t Poland         &  2.8 & Laundry, males\\\\\n",
       "\t Romania        &  2.3 & Laundry, males\\\\\n",
       "\t Serbia         &  2.1 & Laundry, males\\\\\n",
       "\t Turkey         &  0.6 & Laundry, males\\\\\n",
       "\t United Kingdom &  7.0 & Laundry, males\\\\\n",
       "\t Austria        &  3.6 & Ironing, males\\\\\n",
       "\t Belgium        &  2.8 & Ironing, males\\\\\n",
       "\t Germany        &  1.9 & Ironing, males\\\\\n",
       "\t Estonia        &  1.0 & Ironing, males\\\\\n",
       "\t Greece         &  1.0 & Ironing, males\\\\\n",
       "\t Spain          &  1.1 & Ironing, males\\\\\n",
       "\t Finland        &  1.4 & Ironing, males\\\\\n",
       "\t France         &  1.6 & Ironing, males\\\\\n",
       "\t Hungary        &  0.1 & Ironing, males\\\\\n",
       "\t Italy          &  0.4 & Ironing, males\\\\\n",
       "\t Luxembourg     &  7.9 & Ironing, males\\\\\n",
       "\t Netherlands    &  1.4 & Ironing, males\\\\\n",
       "\t Norway         &  1.5 & Ironing, males\\\\\n",
       "\t Poland         &  1.7 & Ironing, males\\\\\n",
       "\t Romania        &  1.2 & Ironing, males\\\\\n",
       "\t Serbia         &  0.6 & Ironing, males\\\\\n",
       "\t Turkey         &  0.5 & Ironing, males\\\\\n",
       "\t United Kingdom &  3.3 & Ironing, males\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 72 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | values &lt;dbl&gt; | bd &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Austria        | 32.8 | Laundry, females |\n",
       "| Belgium        | 23.5 | Laundry, females |\n",
       "| Germany        | 30.1 | Laundry, females |\n",
       "| Estonia        | 13.8 | Laundry, females |\n",
       "| Greece         | 27.8 | Laundry, females |\n",
       "| Spain          | 27.8 | Laundry, females |\n",
       "| Finland        | 33.5 | Laundry, females |\n",
       "| France         | 19.3 | Laundry, females |\n",
       "| Hungary        | 28.7 | Laundry, females |\n",
       "| Italy          | 22.7 | Laundry, females |\n",
       "| Luxembourg     | 26.0 | Laundry, females |\n",
       "| Netherlands    | 34.8 | Laundry, females |\n",
       "| Norway         | 39.5 | Laundry, females |\n",
       "| Poland         | 23.2 | Laundry, females |\n",
       "| Romania        | 30.2 | Laundry, females |\n",
       "| Serbia         | 29.1 | Laundry, females |\n",
       "| Turkey         | 18.7 | Laundry, females |\n",
       "| United Kingdom | 30.0 | Laundry, females |\n",
       "| Austria        | 28.3 | Ironing, females |\n",
       "| Belgium        | 24.2 | Ironing, females |\n",
       "| Germany        | 13.4 | Ironing, females |\n",
       "| Estonia        |  7.8 | Ironing, females |\n",
       "| Greece         | 26.7 | Ironing, females |\n",
       "| Spain          | 17.0 | Ironing, females |\n",
       "| Finland        |  8.1 | Ironing, females |\n",
       "| France         | 17.5 | Ironing, females |\n",
       "| Hungary        | 13.8 | Ironing, females |\n",
       "| Italy          | 24.6 | Ironing, females |\n",
       "| Luxembourg     | 29.1 | Ironing, females |\n",
       "| Netherlands    |  9.7 | Ironing, females |\n",
       "| ⋮ | ⋮ | ⋮ |\n",
       "| Finland        |  7.7 | Laundry, males |\n",
       "| France         |  2.7 | Laundry, males |\n",
       "| Hungary        |  2.4 | Laundry, males |\n",
       "| Italy          |  1.1 | Laundry, males |\n",
       "| Luxembourg     |  3.9 | Laundry, males |\n",
       "| Netherlands    |  6.5 | Laundry, males |\n",
       "| Norway         | 11.5 | Laundry, males |\n",
       "| Poland         |  2.8 | Laundry, males |\n",
       "| Romania        |  2.3 | Laundry, males |\n",
       "| Serbia         |  2.1 | Laundry, males |\n",
       "| Turkey         |  0.6 | Laundry, males |\n",
       "| United Kingdom |  7.0 | Laundry, males |\n",
       "| Austria        |  3.6 | Ironing, males |\n",
       "| Belgium        |  2.8 | Ironing, males |\n",
       "| Germany        |  1.9 | Ironing, males |\n",
       "| Estonia        |  1.0 | Ironing, males |\n",
       "| Greece         |  1.0 | Ironing, males |\n",
       "| Spain          |  1.1 | Ironing, males |\n",
       "| Finland        |  1.4 | Ironing, males |\n",
       "| France         |  1.6 | Ironing, males |\n",
       "| Hungary        |  0.1 | Ironing, males |\n",
       "| Italy          |  0.4 | Ironing, males |\n",
       "| Luxembourg     |  7.9 | Ironing, males |\n",
       "| Netherlands    |  1.4 | Ironing, males |\n",
       "| Norway         |  1.5 | Ironing, males |\n",
       "| Poland         |  1.7 | Ironing, males |\n",
       "| Romania        |  1.2 | Ironing, males |\n",
       "| Serbia         |  0.6 | Ironing, males |\n",
       "| Turkey         |  0.5 | Ironing, males |\n",
       "| United Kingdom |  3.3 | Ironing, males |\n",
       "\n"
      ],
      "text/plain": [
       "       geo            values bd              \n",
       "1      Austria        32.8   Laundry, females\n",
       "2      Belgium        23.5   Laundry, females\n",
       "3      Germany        30.1   Laundry, females\n",
       "4      Estonia        13.8   Laundry, females\n",
       "5      Greece         27.8   Laundry, females\n",
       "6      Spain          27.8   Laundry, females\n",
       "7      Finland        33.5   Laundry, females\n",
       "8      France         19.3   Laundry, females\n",
       "9      Hungary        28.7   Laundry, females\n",
       "10     Italy          22.7   Laundry, females\n",
       "11     Luxembourg     26.0   Laundry, females\n",
       "12     Netherlands    34.8   Laundry, females\n",
       "13     Norway         39.5   Laundry, females\n",
       "14     Poland         23.2   Laundry, females\n",
       "15     Romania        30.2   Laundry, females\n",
       "16     Serbia         29.1   Laundry, females\n",
       "17     Turkey         18.7   Laundry, females\n",
       "18     United Kingdom 30.0   Laundry, females\n",
       "19     Austria        28.3   Ironing, females\n",
       "20     Belgium        24.2   Ironing, females\n",
       "21     Germany        13.4   Ironing, females\n",
       "22     Estonia         7.8   Ironing, females\n",
       "23     Greece         26.7   Ironing, females\n",
       "24     Spain          17.0   Ironing, females\n",
       "25     Finland         8.1   Ironing, females\n",
       "26     France         17.5   Ironing, females\n",
       "27     Hungary        13.8   Ironing, females\n",
       "28     Italy          24.6   Ironing, females\n",
       "29     Luxembourg     29.1   Ironing, females\n",
       "30     Netherlands     9.7   Ironing, females\n",
       "<U+22EE> <U+22EE>       <U+22EE> <U+22EE>        \n",
       "43     Finland         7.7   Laundry, males  \n",
       "44     France          2.7   Laundry, males  \n",
       "45     Hungary         2.4   Laundry, males  \n",
       "46     Italy           1.1   Laundry, males  \n",
       "47     Luxembourg      3.9   Laundry, males  \n",
       "48     Netherlands     6.5   Laundry, males  \n",
       "49     Norway         11.5   Laundry, males  \n",
       "50     Poland          2.8   Laundry, males  \n",
       "51     Romania         2.3   Laundry, males  \n",
       "52     Serbia          2.1   Laundry, males  \n",
       "53     Turkey          0.6   Laundry, males  \n",
       "54     United Kingdom  7.0   Laundry, males  \n",
       "55     Austria         3.6   Ironing, males  \n",
       "56     Belgium         2.8   Ironing, males  \n",
       "57     Germany         1.9   Ironing, males  \n",
       "58     Estonia         1.0   Ironing, males  \n",
       "59     Greece          1.0   Ironing, males  \n",
       "60     Spain           1.1   Ironing, males  \n",
       "61     Finland         1.4   Ironing, males  \n",
       "62     France          1.6   Ironing, males  \n",
       "63     Hungary         0.1   Ironing, males  \n",
       "64     Italy           0.4   Ironing, males  \n",
       "65     Luxembourg      7.9   Ironing, males  \n",
       "66     Netherlands     1.4   Ironing, males  \n",
       "67     Norway          1.5   Ironing, males  \n",
       "68     Poland          1.7   Ironing, males  \n",
       "69     Romania         1.2   Ironing, males  \n",
       "70     Serbia          0.6   Ironing, males  \n",
       "71     Turkey          0.5   Ironing, males  \n",
       "72     United Kingdom  3.3   Ironing, males  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "dt<-dt[,c(\"sex\",\"acl00\",\"geo\",\"values\")]\n",
    "dt[,bd:=paste0(acl00,\", \",tolower(sex))][,c(\"acl00\",\"sex\"):=NULL]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Laundry, females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(bd=c(\"Laundry, males\",\"Laundry, males\"),geo=c(\" \",\"  \"),values=c(NA,NA))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Laundry, females\",bd)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Turkey','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "bd_ord<-sort(unique(dt$bd),decreasing=TRUE)\n",
    "dt$bd<-factor(dt$bd,levels=bd_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
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TuntEaIvNpfVHM2SNI6w3MkKTFulN4Y89cOc4epdR4grzDjvtzoNBRFmw5b8i1L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tZ72pxBn7xb6JgRWnm1+SqwTZe7f64I/O2KIvHm/Nm7dvz8LUoig9GJ3nlJt2Ffq8v6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X9MU8fW3AmeUIAUDqw/1LjwbSnzL0BjaOrZs0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\nWDw52Kte1oZnRWrX3tgm9lm1WyYHsRVTQzi9wsIBsAVuvfXWDf4ssUO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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6_colors<-c(\"#FAA519\",\"#FCC975\",\"#286EB4\",\"#71A8DF\")\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt, aes(fill=bd),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  scale_fill_manual(values = fig6_colors)+\n",
    "  scale_y_continuous(breaks=seq(0,40,5)) +\n",
    "  ggtitle(\"Figure 6: Participation rate per day in laundry and ironing, by gender, % (2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 7: Participation rate per day in shopping and services, by gender, % (2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00educ* dataset as in Figure 5. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^shop\",sex=\"male\",isced97=\"^all\")`. This time we can use again the SDMX REST API to get the values are it is numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>isced97</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>54.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>50.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>54.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>55.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>35.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>51.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>55.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>39.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>50.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>53.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>44.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>54.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>57.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>56.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>31.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>48.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>22.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>50.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>37.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>41.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>45.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>43.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>25.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>33.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>50.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>30.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>34.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>34.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>31.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>41.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>49.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>38.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>30.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>40.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>22.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Shopping and services</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>39.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & isced97 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Austria                                          & 2010 & 54.1\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Belgium                                          & 2010 & 50.1\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 54.2\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Estonia                                          & 2010 & 55.2\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Greece                                           & 2010 & 35.3\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Spain                                            & 2010 & 51.2\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Finland                                          & 2010 & 55.2\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & France                                           & 2010 & 39.4\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Hungary                                          & 2010 & 50.0\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Italy                                            & 2010 & 53.0\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Luxembourg                                       & 2010 & 44.4\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Netherlands                                      & 2010 & 54.9\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Norway                                           & 2010 & 57.2\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Poland                                           & 2010 & 56.5\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Romania                                          & 2010 & 31.7\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Serbia                                           & 2010 & 48.4\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & Turkey                                           & 2010 & 22.5\\\\\n",
       "\t Participation rate (\\%) & Females & Shopping and services & All ISCED 1997 levels & United Kingdom                                   & 2010 & 50.7\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Austria                                          & 2010 & 37.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Belgium                                          & 2010 & 41.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 45.7\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Estonia                                          & 2010 & 43.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Greece                                           & 2010 & 25.9\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Spain                                            & 2010 & 33.6\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Finland                                          & 2010 & 50.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & France                                           & 2010 & 30.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Hungary                                          & 2010 & 34.2\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Italy                                            & 2010 & 34.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Luxembourg                                       & 2010 & 31.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Netherlands                                      & 2010 & 41.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Norway                                           & 2010 & 49.1\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Poland                                           & 2010 & 38.9\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Romania                                          & 2010 & 30.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Serbia                                           & 2010 & 40.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & Turkey                                           & 2010 & 22.9\\\\\n",
       "\t Participation rate (\\%) & Males   & Shopping and services & All ISCED 1997 levels & United Kingdom                                   & 2010 & 39.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | isced97 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Austria                                          | 2010 | 54.1 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Belgium                                          | 2010 | 50.1 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 54.2 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Estonia                                          | 2010 | 55.2 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Greece                                           | 2010 | 35.3 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Spain                                            | 2010 | 51.2 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Finland                                          | 2010 | 55.2 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | France                                           | 2010 | 39.4 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Hungary                                          | 2010 | 50.0 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Italy                                            | 2010 | 53.0 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Luxembourg                                       | 2010 | 44.4 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Netherlands                                      | 2010 | 54.9 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Norway                                           | 2010 | 57.2 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Poland                                           | 2010 | 56.5 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Romania                                          | 2010 | 31.7 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Serbia                                           | 2010 | 48.4 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | Turkey                                           | 2010 | 22.5 |\n",
       "| Participation rate (%) | Females | Shopping and services | All ISCED 1997 levels | United Kingdom                                   | 2010 | 50.7 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Austria                                          | 2010 | 37.0 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Belgium                                          | 2010 | 41.0 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 45.7 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Estonia                                          | 2010 | 43.0 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Greece                                           | 2010 | 25.9 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Spain                                            | 2010 | 33.6 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Finland                                          | 2010 | 50.5 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | France                                           | 2010 | 30.0 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Hungary                                          | 2010 | 34.2 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Italy                                            | 2010 | 34.5 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Luxembourg                                       | 2010 | 31.4 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Netherlands                                      | 2010 | 41.4 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Norway                                           | 2010 | 49.1 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Poland                                           | 2010 | 38.9 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Romania                                          | 2010 | 30.0 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Serbia                                           | 2010 | 40.5 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | Turkey                                           | 2010 | 22.9 |\n",
       "| Participation rate (%) | Males   | Shopping and services | All ISCED 1997 levels | United Kingdom                                   | 2010 | 39.0 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                   sex     acl00                 isced97              \n",
       "1  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "2  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "3  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "4  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "5  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "6  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "7  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "8  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "9  Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "10 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "11 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "12 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "13 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "14 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "15 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "16 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "17 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "18 Participation rate (%) Females Shopping and services All ISCED 1997 levels\n",
       "19 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "20 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "21 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "22 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "23 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "24 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "25 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "26 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "27 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "28 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "29 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "30 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "31 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "32 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "33 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "34 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "35 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "36 Participation rate (%) Males   Shopping and services All ISCED 1997 levels\n",
       "   geo                                              time values\n",
       "1  Austria                                          2010 54.1  \n",
       "2  Belgium                                          2010 50.1  \n",
       "3  Germany (until 1990 former territory of the FRG) 2010 54.2  \n",
       "4  Estonia                                          2010 55.2  \n",
       "5  Greece                                           2010 35.3  \n",
       "6  Spain                                            2010 51.2  \n",
       "7  Finland                                          2010 55.2  \n",
       "8  France                                           2010 39.4  \n",
       "9  Hungary                                          2010 50.0  \n",
       "10 Italy                                            2010 53.0  \n",
       "11 Luxembourg                                       2010 44.4  \n",
       "12 Netherlands                                      2010 54.9  \n",
       "13 Norway                                           2010 57.2  \n",
       "14 Poland                                           2010 56.5  \n",
       "15 Romania                                          2010 31.7  \n",
       "16 Serbia                                           2010 48.4  \n",
       "17 Turkey                                           2010 22.5  \n",
       "18 United Kingdom                                   2010 50.7  \n",
       "19 Austria                                          2010 37.0  \n",
       "20 Belgium                                          2010 41.0  \n",
       "21 Germany (until 1990 former territory of the FRG) 2010 45.7  \n",
       "22 Estonia                                          2010 43.0  \n",
       "23 Greece                                           2010 25.9  \n",
       "24 Spain                                            2010 33.6  \n",
       "25 Finland                                          2010 50.5  \n",
       "26 France                                           2010 30.0  \n",
       "27 Hungary                                          2010 34.2  \n",
       "28 Italy                                            2010 34.5  \n",
       "29 Luxembourg                                       2010 31.4  \n",
       "30 Netherlands                                      2010 41.4  \n",
       "31 Norway                                           2010 49.1  \n",
       "32 Poland                                           2010 38.9  \n",
       "33 Romania                                          2010 30.0  \n",
       "34 Serbia                                           2010 40.5  \n",
       "35 Turkey                                           2010 22.9  \n",
       "36 United Kingdom                                   2010 39.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ\",filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^shop\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we keep only the columns with sex, activities, countries and values. Before plotting the values we need to merge the columns sex and activities and cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>values</th><th scope=col>bd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td>54.1</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>50.1</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Germany       </td><td>54.2</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>55.2</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Greece        </td><td>35.3</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Spain         </td><td>51.2</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Finland       </td><td>55.2</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>France        </td><td>39.4</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>50.0</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Italy         </td><td>53.0</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>44.4</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>54.9</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Norway        </td><td>57.2</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Poland        </td><td>56.5</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Romania       </td><td>31.7</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>48.4</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>22.5</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>50.7</td><td>Shopping and services, females</td></tr>\n",
       "\t<tr><td>Austria       </td><td>37.0</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Belgium       </td><td>41.0</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Germany       </td><td>45.7</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Estonia       </td><td>43.0</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Greece        </td><td>25.9</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Spain         </td><td>33.6</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Finland       </td><td>50.5</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>France        </td><td>30.0</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Hungary       </td><td>34.2</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Italy         </td><td>34.5</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>31.4</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>41.4</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Norway        </td><td>49.1</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Poland        </td><td>38.9</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Romania       </td><td>30.0</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Serbia        </td><td>40.5</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>Turkey        </td><td>22.9</td><td>Shopping and services, males  </td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>39.0</td><td>Shopping and services, males  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & values & bd\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Austria        & 54.1 & Shopping and services, females\\\\\n",
       "\t Belgium        & 50.1 & Shopping and services, females\\\\\n",
       "\t Germany        & 54.2 & Shopping and services, females\\\\\n",
       "\t Estonia        & 55.2 & Shopping and services, females\\\\\n",
       "\t Greece         & 35.3 & Shopping and services, females\\\\\n",
       "\t Spain          & 51.2 & Shopping and services, females\\\\\n",
       "\t Finland        & 55.2 & Shopping and services, females\\\\\n",
       "\t France         & 39.4 & Shopping and services, females\\\\\n",
       "\t Hungary        & 50.0 & Shopping and services, females\\\\\n",
       "\t Italy          & 53.0 & Shopping and services, females\\\\\n",
       "\t Luxembourg     & 44.4 & Shopping and services, females\\\\\n",
       "\t Netherlands    & 54.9 & Shopping and services, females\\\\\n",
       "\t Norway         & 57.2 & Shopping and services, females\\\\\n",
       "\t Poland         & 56.5 & Shopping and services, females\\\\\n",
       "\t Romania        & 31.7 & Shopping and services, females\\\\\n",
       "\t Serbia         & 48.4 & Shopping and services, females\\\\\n",
       "\t Turkey         & 22.5 & Shopping and services, females\\\\\n",
       "\t United Kingdom & 50.7 & Shopping and services, females\\\\\n",
       "\t Austria        & 37.0 & Shopping and services, males  \\\\\n",
       "\t Belgium        & 41.0 & Shopping and services, males  \\\\\n",
       "\t Germany        & 45.7 & Shopping and services, males  \\\\\n",
       "\t Estonia        & 43.0 & Shopping and services, males  \\\\\n",
       "\t Greece         & 25.9 & Shopping and services, males  \\\\\n",
       "\t Spain          & 33.6 & Shopping and services, males  \\\\\n",
       "\t Finland        & 50.5 & Shopping and services, males  \\\\\n",
       "\t France         & 30.0 & Shopping and services, males  \\\\\n",
       "\t Hungary        & 34.2 & Shopping and services, males  \\\\\n",
       "\t Italy          & 34.5 & Shopping and services, males  \\\\\n",
       "\t Luxembourg     & 31.4 & Shopping and services, males  \\\\\n",
       "\t Netherlands    & 41.4 & Shopping and services, males  \\\\\n",
       "\t Norway         & 49.1 & Shopping and services, males  \\\\\n",
       "\t Poland         & 38.9 & Shopping and services, males  \\\\\n",
       "\t Romania        & 30.0 & Shopping and services, males  \\\\\n",
       "\t Serbia         & 40.5 & Shopping and services, males  \\\\\n",
       "\t Turkey         & 22.9 & Shopping and services, males  \\\\\n",
       "\t United Kingdom & 39.0 & Shopping and services, males  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | values &lt;dbl&gt; | bd &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Austria        | 54.1 | Shopping and services, females |\n",
       "| Belgium        | 50.1 | Shopping and services, females |\n",
       "| Germany        | 54.2 | Shopping and services, females |\n",
       "| Estonia        | 55.2 | Shopping and services, females |\n",
       "| Greece         | 35.3 | Shopping and services, females |\n",
       "| Spain          | 51.2 | Shopping and services, females |\n",
       "| Finland        | 55.2 | Shopping and services, females |\n",
       "| France         | 39.4 | Shopping and services, females |\n",
       "| Hungary        | 50.0 | Shopping and services, females |\n",
       "| Italy          | 53.0 | Shopping and services, females |\n",
       "| Luxembourg     | 44.4 | Shopping and services, females |\n",
       "| Netherlands    | 54.9 | Shopping and services, females |\n",
       "| Norway         | 57.2 | Shopping and services, females |\n",
       "| Poland         | 56.5 | Shopping and services, females |\n",
       "| Romania        | 31.7 | Shopping and services, females |\n",
       "| Serbia         | 48.4 | Shopping and services, females |\n",
       "| Turkey         | 22.5 | Shopping and services, females |\n",
       "| United Kingdom | 50.7 | Shopping and services, females |\n",
       "| Austria        | 37.0 | Shopping and services, males   |\n",
       "| Belgium        | 41.0 | Shopping and services, males   |\n",
       "| Germany        | 45.7 | Shopping and services, males   |\n",
       "| Estonia        | 43.0 | Shopping and services, males   |\n",
       "| Greece         | 25.9 | Shopping and services, males   |\n",
       "| Spain          | 33.6 | Shopping and services, males   |\n",
       "| Finland        | 50.5 | Shopping and services, males   |\n",
       "| France         | 30.0 | Shopping and services, males   |\n",
       "| Hungary        | 34.2 | Shopping and services, males   |\n",
       "| Italy          | 34.5 | Shopping and services, males   |\n",
       "| Luxembourg     | 31.4 | Shopping and services, males   |\n",
       "| Netherlands    | 41.4 | Shopping and services, males   |\n",
       "| Norway         | 49.1 | Shopping and services, males   |\n",
       "| Poland         | 38.9 | Shopping and services, males   |\n",
       "| Romania        | 30.0 | Shopping and services, males   |\n",
       "| Serbia         | 40.5 | Shopping and services, males   |\n",
       "| Turkey         | 22.9 | Shopping and services, males   |\n",
       "| United Kingdom | 39.0 | Shopping and services, males   |\n",
       "\n"
      ],
      "text/plain": [
       "   geo            values bd                            \n",
       "1  Austria        54.1   Shopping and services, females\n",
       "2  Belgium        50.1   Shopping and services, females\n",
       "3  Germany        54.2   Shopping and services, females\n",
       "4  Estonia        55.2   Shopping and services, females\n",
       "5  Greece         35.3   Shopping and services, females\n",
       "6  Spain          51.2   Shopping and services, females\n",
       "7  Finland        55.2   Shopping and services, females\n",
       "8  France         39.4   Shopping and services, females\n",
       "9  Hungary        50.0   Shopping and services, females\n",
       "10 Italy          53.0   Shopping and services, females\n",
       "11 Luxembourg     44.4   Shopping and services, females\n",
       "12 Netherlands    54.9   Shopping and services, females\n",
       "13 Norway         57.2   Shopping and services, females\n",
       "14 Poland         56.5   Shopping and services, females\n",
       "15 Romania        31.7   Shopping and services, females\n",
       "16 Serbia         48.4   Shopping and services, females\n",
       "17 Turkey         22.5   Shopping and services, females\n",
       "18 United Kingdom 50.7   Shopping and services, females\n",
       "19 Austria        37.0   Shopping and services, males  \n",
       "20 Belgium        41.0   Shopping and services, males  \n",
       "21 Germany        45.7   Shopping and services, males  \n",
       "22 Estonia        43.0   Shopping and services, males  \n",
       "23 Greece         25.9   Shopping and services, males  \n",
       "24 Spain          33.6   Shopping and services, males  \n",
       "25 Finland        50.5   Shopping and services, males  \n",
       "26 France         30.0   Shopping and services, males  \n",
       "27 Hungary        34.2   Shopping and services, males  \n",
       "28 Italy          34.5   Shopping and services, males  \n",
       "29 Luxembourg     31.4   Shopping and services, males  \n",
       "30 Netherlands    41.4   Shopping and services, males  \n",
       "31 Norway         49.1   Shopping and services, males  \n",
       "32 Poland         38.9   Shopping and services, males  \n",
       "33 Romania        30.0   Shopping and services, males  \n",
       "34 Serbia         40.5   Shopping and services, males  \n",
       "35 Turkey         22.9   Shopping and services, males  \n",
       "36 United Kingdom 39.0   Shopping and services, males  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "dt<-dt[,c(\"sex\",\"acl00\",\"geo\",\"values\")]\n",
    "dt[,bd:=paste0(acl00,\", \",tolower(sex))][,c(\"acl00\",\"sex\"):=NULL]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Shopping and services, females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(bd=c(\"Shopping and services, males\",\"Shopping and services, males\"),geo=c(\" \",\"  \"),values=c(NA,NA))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Shopping and services, females\",bd)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Turkey','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "bd_ord<-sort(unique(dt$bd),decreasing=TRUE)\n",
    "dt$bd<-factor(dt$bd,levels=bd_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
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kn6YzsHK34LhBZNuw1e+M/2a/cev4/4lJJRQIxYnHW/giVp/mxRptaq/x6obyOU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hv8HhO8ZP4fzz4tb+c19kBQf62GqI9oSnx2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\n9EgNGGoxUsUULRVW3PWrfNmQE1HJldtrGrMWEznMmUKKp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zr8kkt+164gN6K8AGyhd8YNu/ax6cnNLeP+6U9/sqLo6y8+W1BUmkyficXi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\nfQghpMpnPH7d0LFvhxBi8bxhjz3avkF2xnIDAACEEELwZwkAhFSq9O/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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig7_colors<-c(\"#FAA519\",\"#286EB4\") #\"#FCC975\",\"#71A8DF\",\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt, aes(fill=bd),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  scale_fill_manual(values = fig7_colors)+\n",
    "  scale_y_continuous(breaks=seq(0,70,10)) +\n",
    "  ggtitle(\"Figure 7: Participation rate per day in shopping and services, by gender, % (2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 8: Participation rate per day in childcare, by gender, % (2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00educ* dataset as in Figure 5. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^child|^teach\",sex=\"male\",isced97=\"^all\")`. This time we can use again the SDMX REST API to get the values are it is numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 72 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>isced97</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>23.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>22.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>16.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>23.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>14.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>26.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>18.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>26.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>19.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>21.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>21.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>23.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>26.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>26.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>15.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>18.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>31.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>26.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>23.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>14.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>13.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>15.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>10.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>13.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>14.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>14.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>21.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>14.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>14.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>14.7</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>12.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>14.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td> 8.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td> 8.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td>12.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>16.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>21.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>13.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td> 5.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td> 7.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 6.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Childcare, except teaching, reading and talking</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td>13.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>15.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td> 8.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td> 7.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td> 7.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>10.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td>11.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td> 8.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td> 9.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>15.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>10.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td> 8.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td> 9.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>11.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>16.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td> 7.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td> 8.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td>12.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males</td><td>Teaching, reading and talking with child       </td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td> 9.3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 72 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & isced97 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Austria                                          & 2010 & 23.0\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Belgium                                          & 2010 & 22.7\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 16.3\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Estonia                                          & 2010 & 23.2\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Greece                                           & 2010 & 14.1\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Spain                                            & 2010 & 26.0\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Finland                                          & 2010 & 18.7\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & France                                           & 2010 & 26.5\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Hungary                                          & 2010 & 19.3\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Italy                                            & 2010 & 21.1\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Luxembourg                                       & 2010 & 21.1\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Netherlands                                      & 2010 & 23.3\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Norway                                           & 2010 & 26.4\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Poland                                           & 2010 & 26.5\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Romania                                          & 2010 & 15.4\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Serbia                                           & 2010 & 18.4\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Turkey                                           & 2010 & 31.7\\\\\n",
       "\t Participation rate (\\%) & Females & Childcare, except teaching, reading and talking & All ISCED 1997 levels & United Kingdom                                   & 2010 & 26.2\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Austria                                          & 2010 & 23.6\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Belgium                                          & 2010 & 14.2\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 13.0\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Estonia                                          & 2010 & 15.4\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Greece                                           & 2010 & 10.9\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Spain                                            & 2010 & 13.9\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Finland                                          & 2010 & 14.7\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & France                                           & 2010 & 14.7\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Hungary                                          & 2010 & 21.7\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Italy                                            & 2010 & 14.2\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Luxembourg                                       & 2010 & 14.7\\\\\n",
       "\t Participation rate (\\%) & Females & Teaching, reading and talking with child        & All ISCED 1997 levels & Netherlands                                      & 2010 & 14.7\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Finland                                          & 2010 & 12.0\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & France                                           & 2010 & 14.0\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Hungary                                          & 2010 &  8.6\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Italy                                            & 2010 &  8.6\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Luxembourg                                       & 2010 & 12.1\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Netherlands                                      & 2010 & 16.0\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Norway                                           & 2010 & 21.2\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Poland                                           & 2010 & 13.4\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Romania                                          & 2010 &  5.9\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Serbia                                           & 2010 &  7.9\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & Turkey                                           & 2010 &  6.0\\\\\n",
       "\t Participation rate (\\%) & Males & Childcare, except teaching, reading and talking & All ISCED 1997 levels & United Kingdom                                   & 2010 & 13.4\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Austria                                          & 2010 & 15.2\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Belgium                                          & 2010 &  8.8\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 &  7.2\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Estonia                                          & 2010 &  7.8\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Greece                                           & 2010 & 10.0\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Spain                                            & 2010 & 11.0\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Finland                                          & 2010 &  8.4\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & France                                           & 2010 &  9.1\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Hungary                                          & 2010 & 15.4\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Italy                                            & 2010 & 10.0\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Luxembourg                                       & 2010 &  8.4\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Netherlands                                      & 2010 &  9.0\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Norway                                           & 2010 & 11.5\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Poland                                           & 2010 & 16.4\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Romania                                          & 2010 &  7.0\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Serbia                                           & 2010 &  8.5\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & Turkey                                           & 2010 & 12.5\\\\\n",
       "\t Participation rate (\\%) & Males & Teaching, reading and talking with child        & All ISCED 1997 levels & United Kingdom                                   & 2010 &  9.3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 72 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | isced97 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Austria                                          | 2010 | 23.0 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Belgium                                          | 2010 | 22.7 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 16.3 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Estonia                                          | 2010 | 23.2 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Greece                                           | 2010 | 14.1 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Spain                                            | 2010 | 26.0 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Finland                                          | 2010 | 18.7 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | France                                           | 2010 | 26.5 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Hungary                                          | 2010 | 19.3 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Italy                                            | 2010 | 21.1 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Luxembourg                                       | 2010 | 21.1 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Netherlands                                      | 2010 | 23.3 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Norway                                           | 2010 | 26.4 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Poland                                           | 2010 | 26.5 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Romania                                          | 2010 | 15.4 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Serbia                                           | 2010 | 18.4 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Turkey                                           | 2010 | 31.7 |\n",
       "| Participation rate (%) | Females | Childcare, except teaching, reading and talking | All ISCED 1997 levels | United Kingdom                                   | 2010 | 26.2 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Austria                                          | 2010 | 23.6 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Belgium                                          | 2010 | 14.2 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 13.0 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Estonia                                          | 2010 | 15.4 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Greece                                           | 2010 | 10.9 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Spain                                            | 2010 | 13.9 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Finland                                          | 2010 | 14.7 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | France                                           | 2010 | 14.7 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Hungary                                          | 2010 | 21.7 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Italy                                            | 2010 | 14.2 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Luxembourg                                       | 2010 | 14.7 |\n",
       "| Participation rate (%) | Females | Teaching, reading and talking with child        | All ISCED 1997 levels | Netherlands                                      | 2010 | 14.7 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Finland                                          | 2010 | 12.0 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | France                                           | 2010 | 14.0 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Hungary                                          | 2010 |  8.6 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Italy                                            | 2010 |  8.6 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Luxembourg                                       | 2010 | 12.1 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Netherlands                                      | 2010 | 16.0 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Norway                                           | 2010 | 21.2 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Poland                                           | 2010 | 13.4 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Romania                                          | 2010 |  5.9 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Serbia                                           | 2010 |  7.9 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | Turkey                                           | 2010 |  6.0 |\n",
       "| Participation rate (%) | Males | Childcare, except teaching, reading and talking | All ISCED 1997 levels | United Kingdom                                   | 2010 | 13.4 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Austria                                          | 2010 | 15.2 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Belgium                                          | 2010 |  8.8 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 |  7.2 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Estonia                                          | 2010 |  7.8 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Greece                                           | 2010 | 10.0 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Spain                                            | 2010 | 11.0 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Finland                                          | 2010 |  8.4 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | France                                           | 2010 |  9.1 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Hungary                                          | 2010 | 15.4 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Italy                                            | 2010 | 10.0 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Luxembourg                                       | 2010 |  8.4 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Netherlands                                      | 2010 |  9.0 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Norway                                           | 2010 | 11.5 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Poland                                           | 2010 | 16.4 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Romania                                          | 2010 |  7.0 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Serbia                                           | 2010 |  8.5 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | Turkey                                           | 2010 | 12.5 |\n",
       "| Participation rate (%) | Males | Teaching, reading and talking with child        | All ISCED 1997 levels | United Kingdom                                   | 2010 |  9.3 |\n",
       "\n"
      ],
      "text/plain": [
       "       unit                   sex    \n",
       "1      Participation rate (%) Females\n",
       "2      Participation rate (%) Females\n",
       "3      Participation rate (%) Females\n",
       "4      Participation rate (%) Females\n",
       "5      Participation rate (%) Females\n",
       "6      Participation rate (%) Females\n",
       "7      Participation rate (%) Females\n",
       "8      Participation rate (%) Females\n",
       "9      Participation rate (%) Females\n",
       "10     Participation rate (%) Females\n",
       "11     Participation rate (%) Females\n",
       "12     Participation rate (%) Females\n",
       "13     Participation rate (%) Females\n",
       "14     Participation rate (%) Females\n",
       "15     Participation rate (%) Females\n",
       "16     Participation rate (%) Females\n",
       "17     Participation rate (%) Females\n",
       "18     Participation rate (%) Females\n",
       "19     Participation rate (%) Females\n",
       "20     Participation rate (%) Females\n",
       "21     Participation rate (%) Females\n",
       "22     Participation rate (%) Females\n",
       "23     Participation rate (%) Females\n",
       "24     Participation rate (%) Females\n",
       "25     Participation rate (%) Females\n",
       "26     Participation rate (%) Females\n",
       "27     Participation rate (%) Females\n",
       "28     Participation rate (%) Females\n",
       "29     Participation rate (%) Females\n",
       "30     Participation rate (%) Females\n",
       "<U+22EE> <U+22EE>               <U+22EE>\n",
       "43     Participation rate (%) Males  \n",
       "44     Participation rate (%) Males  \n",
       "45     Participation rate (%) Males  \n",
       "46     Participation rate (%) Males  \n",
       "47     Participation rate (%) Males  \n",
       "48     Participation rate (%) Males  \n",
       "49     Participation rate (%) Males  \n",
       "50     Participation rate (%) Males  \n",
       "51     Participation rate (%) Males  \n",
       "52     Participation rate (%) Males  \n",
       "53     Participation rate (%) Males  \n",
       "54     Participation rate (%) Males  \n",
       "55     Participation rate (%) Males  \n",
       "56     Participation rate (%) Males  \n",
       "57     Participation rate (%) Males  \n",
       "58     Participation rate (%) Males  \n",
       "59     Participation rate (%) Males  \n",
       "60     Participation rate (%) Males  \n",
       "61     Participation rate (%) Males  \n",
       "62     Participation rate (%) Males  \n",
       "63     Participation rate (%) Males  \n",
       "64     Participation rate (%) Males  \n",
       "65     Participation rate (%) Males  \n",
       "66     Participation rate (%) Males  \n",
       "67     Participation rate (%) Males  \n",
       "68     Participation rate (%) Males  \n",
       "69     Participation rate (%) Males  \n",
       "70     Participation rate (%) Males  \n",
       "71     Participation rate (%) Males  \n",
       "72     Participation rate (%) Males  \n",
       "       acl00                                           isced97              \n",
       "1      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "2      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "3      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "4      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "5      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "6      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "7      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "8      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "9      Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "10     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "11     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "12     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "13     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "14     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "15     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "16     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "17     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "18     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "19     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "20     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "21     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "22     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "23     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "24     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "25     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "26     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "27     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "28     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "29     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "30     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "<U+22EE> <U+22EE>                                        <U+22EE>             \n",
       "43     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "44     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "45     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "46     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "47     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "48     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "49     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "50     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "51     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "52     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "53     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "54     Childcare, except teaching, reading and talking All ISCED 1997 levels\n",
       "55     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "56     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "57     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "58     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "59     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "60     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "61     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "62     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "63     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "64     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "65     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "66     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "67     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "68     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "69     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "70     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "71     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "72     Teaching, reading and talking with child        All ISCED 1997 levels\n",
       "       geo                                              time   values\n",
       "1      Austria                                          2010   23.0  \n",
       "2      Belgium                                          2010   22.7  \n",
       "3      Germany (until 1990 former territory of the FRG) 2010   16.3  \n",
       "4      Estonia                                          2010   23.2  \n",
       "5      Greece                                           2010   14.1  \n",
       "6      Spain                                            2010   26.0  \n",
       "7      Finland                                          2010   18.7  \n",
       "8      France                                           2010   26.5  \n",
       "9      Hungary                                          2010   19.3  \n",
       "10     Italy                                            2010   21.1  \n",
       "11     Luxembourg                                       2010   21.1  \n",
       "12     Netherlands                                      2010   23.3  \n",
       "13     Norway                                           2010   26.4  \n",
       "14     Poland                                           2010   26.5  \n",
       "15     Romania                                          2010   15.4  \n",
       "16     Serbia                                           2010   18.4  \n",
       "17     Turkey                                           2010   31.7  \n",
       "18     United Kingdom                                   2010   26.2  \n",
       "19     Austria                                          2010   23.6  \n",
       "20     Belgium                                          2010   14.2  \n",
       "21     Germany (until 1990 former territory of the FRG) 2010   13.0  \n",
       "22     Estonia                                          2010   15.4  \n",
       "23     Greece                                           2010   10.9  \n",
       "24     Spain                                            2010   13.9  \n",
       "25     Finland                                          2010   14.7  \n",
       "26     France                                           2010   14.7  \n",
       "27     Hungary                                          2010   21.7  \n",
       "28     Italy                                            2010   14.2  \n",
       "29     Luxembourg                                       2010   14.7  \n",
       "30     Netherlands                                      2010   14.7  \n",
       "<U+22EE> <U+22EE>                                         <U+22EE> <U+22EE>\n",
       "43     Finland                                          2010   12.0  \n",
       "44     France                                           2010   14.0  \n",
       "45     Hungary                                          2010    8.6  \n",
       "46     Italy                                            2010    8.6  \n",
       "47     Luxembourg                                       2010   12.1  \n",
       "48     Netherlands                                      2010   16.0  \n",
       "49     Norway                                           2010   21.2  \n",
       "50     Poland                                           2010   13.4  \n",
       "51     Romania                                          2010    5.9  \n",
       "52     Serbia                                           2010    7.9  \n",
       "53     Turkey                                           2010    6.0  \n",
       "54     United Kingdom                                   2010   13.4  \n",
       "55     Austria                                          2010   15.2  \n",
       "56     Belgium                                          2010    8.8  \n",
       "57     Germany (until 1990 former territory of the FRG) 2010    7.2  \n",
       "58     Estonia                                          2010    7.8  \n",
       "59     Greece                                           2010   10.0  \n",
       "60     Spain                                            2010   11.0  \n",
       "61     Finland                                          2010    8.4  \n",
       "62     France                                           2010    9.1  \n",
       "63     Hungary                                          2010   15.4  \n",
       "64     Italy                                            2010   10.0  \n",
       "65     Luxembourg                                       2010    8.4  \n",
       "66     Netherlands                                      2010    9.0  \n",
       "67     Norway                                           2010   11.5  \n",
       "68     Poland                                           2010   16.4  \n",
       "69     Romania                                          2010    7.0  \n",
       "70     Serbia                                           2010    8.5  \n",
       "71     Turkey                                           2010   12.5  \n",
       "72     United Kingdom                                   2010    9.3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ\",filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^child|^teach\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we keep only the columns with sex, activities, countries and values. Before plotting the values we need to merge the columns sex and activities and cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 72 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>values</th><th scope=col>bd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td>23.0</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>22.7</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Germany       </td><td>16.3</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>23.2</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Greece        </td><td>14.1</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Spain         </td><td>26.0</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Finland       </td><td>18.7</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>France        </td><td>26.5</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>19.3</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Italy         </td><td>21.1</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>21.1</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>23.3</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Norway        </td><td>26.4</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Poland        </td><td>26.5</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Romania       </td><td>15.4</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>18.4</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>31.7</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>26.2</td><td>Childcare, except teaching, reading and talking, females</td></tr>\n",
       "\t<tr><td>Austria       </td><td>23.6</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Belgium       </td><td>14.2</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Germany       </td><td>13.0</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Estonia       </td><td>15.4</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Greece        </td><td>10.9</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Spain         </td><td>13.9</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Finland       </td><td>14.7</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>France        </td><td>14.7</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Hungary       </td><td>21.7</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Italy         </td><td>14.2</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>14.7</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>14.7</td><td>Teaching, reading and talking with child, females       </td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Finland       </td><td>12.0</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>France        </td><td>14.0</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Hungary       </td><td> 8.6</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Italy         </td><td> 8.6</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>12.1</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>16.0</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Norway        </td><td>21.2</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Poland        </td><td>13.4</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Romania       </td><td> 5.9</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Serbia        </td><td> 7.9</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 6.0</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>13.4</td><td>Childcare, except teaching, reading and talking, males</td></tr>\n",
       "\t<tr><td>Austria       </td><td>15.2</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Belgium       </td><td> 8.8</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Germany       </td><td> 7.2</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Estonia       </td><td> 7.8</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Greece        </td><td>10.0</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Spain         </td><td>11.0</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Finland       </td><td> 8.4</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>France        </td><td> 9.1</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Hungary       </td><td>15.4</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Italy         </td><td>10.0</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td> 8.4</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Netherlands   </td><td> 9.0</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Norway        </td><td>11.5</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Poland        </td><td>16.4</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Romania       </td><td> 7.0</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Serbia        </td><td> 8.5</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>Turkey        </td><td>12.5</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "\t<tr><td>United Kingdom</td><td> 9.3</td><td>Teaching, reading and talking with child, males       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 72 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & values & bd\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Austria        & 23.0 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Belgium        & 22.7 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Germany        & 16.3 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Estonia        & 23.2 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Greece         & 14.1 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Spain          & 26.0 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Finland        & 18.7 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t France         & 26.5 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Hungary        & 19.3 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Italy          & 21.1 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Luxembourg     & 21.1 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Netherlands    & 23.3 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Norway         & 26.4 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Poland         & 26.5 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Romania        & 15.4 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Serbia         & 18.4 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Turkey         & 31.7 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t United Kingdom & 26.2 & Childcare, except teaching, reading and talking, females\\\\\n",
       "\t Austria        & 23.6 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Belgium        & 14.2 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Germany        & 13.0 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Estonia        & 15.4 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Greece         & 10.9 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Spain          & 13.9 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Finland        & 14.7 & Teaching, reading and talking with child, females       \\\\\n",
       "\t France         & 14.7 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Hungary        & 21.7 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Italy          & 14.2 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Luxembourg     & 14.7 & Teaching, reading and talking with child, females       \\\\\n",
       "\t Netherlands    & 14.7 & Teaching, reading and talking with child, females       \\\\\n",
       "\t ⋮ & ⋮ & ⋮\\\\\n",
       "\t Finland        & 12.0 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t France         & 14.0 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Hungary        &  8.6 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Italy          &  8.6 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Luxembourg     & 12.1 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Netherlands    & 16.0 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Norway         & 21.2 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Poland         & 13.4 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Romania        &  5.9 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Serbia         &  7.9 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Turkey         &  6.0 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t United Kingdom & 13.4 & Childcare, except teaching, reading and talking, males\\\\\n",
       "\t Austria        & 15.2 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Belgium        &  8.8 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Germany        &  7.2 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Estonia        &  7.8 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Greece         & 10.0 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Spain          & 11.0 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Finland        &  8.4 & Teaching, reading and talking with child, males       \\\\\n",
       "\t France         &  9.1 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Hungary        & 15.4 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Italy          & 10.0 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Luxembourg     &  8.4 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Netherlands    &  9.0 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Norway         & 11.5 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Poland         & 16.4 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Romania        &  7.0 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Serbia         &  8.5 & Teaching, reading and talking with child, males       \\\\\n",
       "\t Turkey         & 12.5 & Teaching, reading and talking with child, males       \\\\\n",
       "\t United Kingdom &  9.3 & Teaching, reading and talking with child, males       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 72 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | values &lt;dbl&gt; | bd &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Austria        | 23.0 | Childcare, except teaching, reading and talking, females |\n",
       "| Belgium        | 22.7 | Childcare, except teaching, reading and talking, females |\n",
       "| Germany        | 16.3 | Childcare, except teaching, reading and talking, females |\n",
       "| Estonia        | 23.2 | Childcare, except teaching, reading and talking, females |\n",
       "| Greece         | 14.1 | Childcare, except teaching, reading and talking, females |\n",
       "| Spain          | 26.0 | Childcare, except teaching, reading and talking, females |\n",
       "| Finland        | 18.7 | Childcare, except teaching, reading and talking, females |\n",
       "| France         | 26.5 | Childcare, except teaching, reading and talking, females |\n",
       "| Hungary        | 19.3 | Childcare, except teaching, reading and talking, females |\n",
       "| Italy          | 21.1 | Childcare, except teaching, reading and talking, females |\n",
       "| Luxembourg     | 21.1 | Childcare, except teaching, reading and talking, females |\n",
       "| Netherlands    | 23.3 | Childcare, except teaching, reading and talking, females |\n",
       "| Norway         | 26.4 | Childcare, except teaching, reading and talking, females |\n",
       "| Poland         | 26.5 | Childcare, except teaching, reading and talking, females |\n",
       "| Romania        | 15.4 | Childcare, except teaching, reading and talking, females |\n",
       "| Serbia         | 18.4 | Childcare, except teaching, reading and talking, females |\n",
       "| Turkey         | 31.7 | Childcare, except teaching, reading and talking, females |\n",
       "| United Kingdom | 26.2 | Childcare, except teaching, reading and talking, females |\n",
       "| Austria        | 23.6 | Teaching, reading and talking with child, females        |\n",
       "| Belgium        | 14.2 | Teaching, reading and talking with child, females        |\n",
       "| Germany        | 13.0 | Teaching, reading and talking with child, females        |\n",
       "| Estonia        | 15.4 | Teaching, reading and talking with child, females        |\n",
       "| Greece         | 10.9 | Teaching, reading and talking with child, females        |\n",
       "| Spain          | 13.9 | Teaching, reading and talking with child, females        |\n",
       "| Finland        | 14.7 | Teaching, reading and talking with child, females        |\n",
       "| France         | 14.7 | Teaching, reading and talking with child, females        |\n",
       "| Hungary        | 21.7 | Teaching, reading and talking with child, females        |\n",
       "| Italy          | 14.2 | Teaching, reading and talking with child, females        |\n",
       "| Luxembourg     | 14.7 | Teaching, reading and talking with child, females        |\n",
       "| Netherlands    | 14.7 | Teaching, reading and talking with child, females        |\n",
       "| ⋮ | ⋮ | ⋮ |\n",
       "| Finland        | 12.0 | Childcare, except teaching, reading and talking, males |\n",
       "| France         | 14.0 | Childcare, except teaching, reading and talking, males |\n",
       "| Hungary        |  8.6 | Childcare, except teaching, reading and talking, males |\n",
       "| Italy          |  8.6 | Childcare, except teaching, reading and talking, males |\n",
       "| Luxembourg     | 12.1 | Childcare, except teaching, reading and talking, males |\n",
       "| Netherlands    | 16.0 | Childcare, except teaching, reading and talking, males |\n",
       "| Norway         | 21.2 | Childcare, except teaching, reading and talking, males |\n",
       "| Poland         | 13.4 | Childcare, except teaching, reading and talking, males |\n",
       "| Romania        |  5.9 | Childcare, except teaching, reading and talking, males |\n",
       "| Serbia         |  7.9 | Childcare, except teaching, reading and talking, males |\n",
       "| Turkey         |  6.0 | Childcare, except teaching, reading and talking, males |\n",
       "| United Kingdom | 13.4 | Childcare, except teaching, reading and talking, males |\n",
       "| Austria        | 15.2 | Teaching, reading and talking with child, males        |\n",
       "| Belgium        |  8.8 | Teaching, reading and talking with child, males        |\n",
       "| Germany        |  7.2 | Teaching, reading and talking with child, males        |\n",
       "| Estonia        |  7.8 | Teaching, reading and talking with child, males        |\n",
       "| Greece         | 10.0 | Teaching, reading and talking with child, males        |\n",
       "| Spain          | 11.0 | Teaching, reading and talking with child, males        |\n",
       "| Finland        |  8.4 | Teaching, reading and talking with child, males        |\n",
       "| France         |  9.1 | Teaching, reading and talking with child, males        |\n",
       "| Hungary        | 15.4 | Teaching, reading and talking with child, males        |\n",
       "| Italy          | 10.0 | Teaching, reading and talking with child, males        |\n",
       "| Luxembourg     |  8.4 | Teaching, reading and talking with child, males        |\n",
       "| Netherlands    |  9.0 | Teaching, reading and talking with child, males        |\n",
       "| Norway         | 11.5 | Teaching, reading and talking with child, males        |\n",
       "| Poland         | 16.4 | Teaching, reading and talking with child, males        |\n",
       "| Romania        |  7.0 | Teaching, reading and talking with child, males        |\n",
       "| Serbia         |  8.5 | Teaching, reading and talking with child, males        |\n",
       "| Turkey         | 12.5 | Teaching, reading and talking with child, males        |\n",
       "| United Kingdom |  9.3 | Teaching, reading and talking with child, males        |\n",
       "\n"
      ],
      "text/plain": [
       "       geo            values\n",
       "1      Austria        23.0  \n",
       "2      Belgium        22.7  \n",
       "3      Germany        16.3  \n",
       "4      Estonia        23.2  \n",
       "5      Greece         14.1  \n",
       "6      Spain          26.0  \n",
       "7      Finland        18.7  \n",
       "8      France         26.5  \n",
       "9      Hungary        19.3  \n",
       "10     Italy          21.1  \n",
       "11     Luxembourg     21.1  \n",
       "12     Netherlands    23.3  \n",
       "13     Norway         26.4  \n",
       "14     Poland         26.5  \n",
       "15     Romania        15.4  \n",
       "16     Serbia         18.4  \n",
       "17     Turkey         31.7  \n",
       "18     United Kingdom 26.2  \n",
       "19     Austria        23.6  \n",
       "20     Belgium        14.2  \n",
       "21     Germany        13.0  \n",
       "22     Estonia        15.4  \n",
       "23     Greece         10.9  \n",
       "24     Spain          13.9  \n",
       "25     Finland        14.7  \n",
       "26     France         14.7  \n",
       "27     Hungary        21.7  \n",
       "28     Italy          14.2  \n",
       "29     Luxembourg     14.7  \n",
       "30     Netherlands    14.7  \n",
       "<U+22EE> <U+22EE>       <U+22EE>\n",
       "43     Finland        12.0  \n",
       "44     France         14.0  \n",
       "45     Hungary         8.6  \n",
       "46     Italy           8.6  \n",
       "47     Luxembourg     12.1  \n",
       "48     Netherlands    16.0  \n",
       "49     Norway         21.2  \n",
       "50     Poland         13.4  \n",
       "51     Romania         5.9  \n",
       "52     Serbia          7.9  \n",
       "53     Turkey          6.0  \n",
       "54     United Kingdom 13.4  \n",
       "55     Austria        15.2  \n",
       "56     Belgium         8.8  \n",
       "57     Germany         7.2  \n",
       "58     Estonia         7.8  \n",
       "59     Greece         10.0  \n",
       "60     Spain          11.0  \n",
       "61     Finland         8.4  \n",
       "62     France          9.1  \n",
       "63     Hungary        15.4  \n",
       "64     Italy          10.0  \n",
       "65     Luxembourg      8.4  \n",
       "66     Netherlands     9.0  \n",
       "67     Norway         11.5  \n",
       "68     Poland         16.4  \n",
       "69     Romania         7.0  \n",
       "70     Serbia          8.5  \n",
       "71     Turkey         12.5  \n",
       "72     United Kingdom  9.3  \n",
       "       bd                                                      \n",
       "1      Childcare, except teaching, reading and talking, females\n",
       "2      Childcare, except teaching, reading and talking, females\n",
       "3      Childcare, except teaching, reading and talking, females\n",
       "4      Childcare, except teaching, reading and talking, females\n",
       "5      Childcare, except teaching, reading and talking, females\n",
       "6      Childcare, except teaching, reading and talking, females\n",
       "7      Childcare, except teaching, reading and talking, females\n",
       "8      Childcare, except teaching, reading and talking, females\n",
       "9      Childcare, except teaching, reading and talking, females\n",
       "10     Childcare, except teaching, reading and talking, females\n",
       "11     Childcare, except teaching, reading and talking, females\n",
       "12     Childcare, except teaching, reading and talking, females\n",
       "13     Childcare, except teaching, reading and talking, females\n",
       "14     Childcare, except teaching, reading and talking, females\n",
       "15     Childcare, except teaching, reading and talking, females\n",
       "16     Childcare, except teaching, reading and talking, females\n",
       "17     Childcare, except teaching, reading and talking, females\n",
       "18     Childcare, except teaching, reading and talking, females\n",
       "19     Teaching, reading and talking with child, females       \n",
       "20     Teaching, reading and talking with child, females       \n",
       "21     Teaching, reading and talking with child, females       \n",
       "22     Teaching, reading and talking with child, females       \n",
       "23     Teaching, reading and talking with child, females       \n",
       "24     Teaching, reading and talking with child, females       \n",
       "25     Teaching, reading and talking with child, females       \n",
       "26     Teaching, reading and talking with child, females       \n",
       "27     Teaching, reading and talking with child, females       \n",
       "28     Teaching, reading and talking with child, females       \n",
       "29     Teaching, reading and talking with child, females       \n",
       "30     Teaching, reading and talking with child, females       \n",
       "<U+22EE> <U+22EE>                                                \n",
       "43     Childcare, except teaching, reading and talking, males  \n",
       "44     Childcare, except teaching, reading and talking, males  \n",
       "45     Childcare, except teaching, reading and talking, males  \n",
       "46     Childcare, except teaching, reading and talking, males  \n",
       "47     Childcare, except teaching, reading and talking, males  \n",
       "48     Childcare, except teaching, reading and talking, males  \n",
       "49     Childcare, except teaching, reading and talking, males  \n",
       "50     Childcare, except teaching, reading and talking, males  \n",
       "51     Childcare, except teaching, reading and talking, males  \n",
       "52     Childcare, except teaching, reading and talking, males  \n",
       "53     Childcare, except teaching, reading and talking, males  \n",
       "54     Childcare, except teaching, reading and talking, males  \n",
       "55     Teaching, reading and talking with child, males         \n",
       "56     Teaching, reading and talking with child, males         \n",
       "57     Teaching, reading and talking with child, males         \n",
       "58     Teaching, reading and talking with child, males         \n",
       "59     Teaching, reading and talking with child, males         \n",
       "60     Teaching, reading and talking with child, males         \n",
       "61     Teaching, reading and talking with child, males         \n",
       "62     Teaching, reading and talking with child, males         \n",
       "63     Teaching, reading and talking with child, males         \n",
       "64     Teaching, reading and talking with child, males         \n",
       "65     Teaching, reading and talking with child, males         \n",
       "66     Teaching, reading and talking with child, males         \n",
       "67     Teaching, reading and talking with child, males         \n",
       "68     Teaching, reading and talking with child, males         \n",
       "69     Teaching, reading and talking with child, males         \n",
       "70     Teaching, reading and talking with child, males         \n",
       "71     Teaching, reading and talking with child, males         \n",
       "72     Teaching, reading and talking with child, males         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "dt<-dt[,c(\"sex\",\"acl00\",\"geo\",\"values\")]\n",
    "dt[,bd:=paste0(acl00,\", \",tolower(sex))][,c(\"acl00\",\"sex\"):=NULL]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Childcare, except teaching, reading and talking, females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(bd=c(\"Childcare, except teaching, reading and talking, males\",\"Childcare, except teaching, reading and talking, males\"),geo=c(\" \",\"  \"),values=c(NA,NA))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Childcare, except teaching, reading and talking, females\",bd)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia','Turkey')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "bd_ord<-sort(unique(dt$bd))[c(2,1,4,3)]\n",
    "dt$bd<-factor(dt$bd,levels=bd_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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5C3x/a//AZmXq9xp3ibJk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ZaOQNpDLMh///49ADAlCXTHgpAqpJeSAMBg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\nPp+flZWVEHnv35S8sld5TFjjhIso6gCvMyqAYwVIy4kF+e/fvwcApiSB7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WSw7Fo3t+TnZKalpaVn5JReMNyixdC9a0fj1Fv54XVG9T4H\n/zVt2ysA0NGzXXfIx6XCwUZh9ss/pq5KFJAA0GLhwXUe1iqKEiGE0I/DutAaTksm9GOSHtUy\n3Nfh/teu33sSIyBl/HQtbFv26TegXxdXBj7iVduwAf0FEmrA/rMT6xqoOxYtMmPowOQiUtek\nzeFjK9iYmFGhMYMG5IglrZYeWuNmpe5YtAguYIC0jTAnatn0VW8LRKUPEgTDzMpaX5LPTcsW\nf3tnburSb/f6SZinV6xCXlzow6cxMTEfk9PyC/ILRWBkZMw2r+Po7NyiXUfXxhZl+pNFSQlp\nerb1LPG/QalE/FdDRyyXUJTd8O07RjVWdzi1GCbp1Usiynn5+MnbxJTmA0c5llqP8eGZbXsv\nheZ8KWwlCHqL7qOWeA1m4f7c8kmL3Dhp7SMAYLAcD5z2tihnTu3deWN2xecAwLTjF/qY6ZV5\n9+mGyWse8wCg4cC/fSf8pOSQNRwp+LhuxqJnGQIAIOisNl37dvu5qaWlBceSY6gjSuPxeDxe\n3PP7AYFhWSKSIOi9ZvlM794EACiJ8PO7F7dvXLwS8kb6Ufajtm8djpf9H4DXGRULnDXqQGIe\nALRbfHC5e+VJd+799VN9ngCAccPpp317Kz0+hBBC1YB1oTWe5k/oxyQ9qpUokv/hTXR8cnp+\nfn6hUGJgaGRsyrF3dqlrWvY5HFXZn8MHvS0Uzz15seuXqdlIBQb17y+mqF82n/hTc5dwqZmk\ns1JmnLjQCy8jKoQLGCAtQx2cMfJGcoH0BZPduN/gfp3aNatjbc6kEQBAkYK0zykvHwVfvRKY\nkF+cyLfxXLzvD3e1hYyQCu0YO+zfbIGeaa8LJ2aoO5Za7MKFCwDAtu/Wo6WZumNB3xAXpDx/\n+T4ts8C8XqPGdnbmRjjA9wMil09c+zIdABwn797Sr4HsTpR4ytChqUISAKafuND7u7v6nPit\nY+aFAoBh3Sln9vdVbsSa7sKicaffZAFAffeRi6cPalDOgDVZmHrj6JYjQe8Igvjtr8NT2lmW\nvPX+f3v+2HWboig60/r4+QM4Q10h8DqjDPOGDYoXiAmCfvjSZUtG5Q+tElH60CGTRBSlo9f4\nyoXtKogQIYRQNWFdaE2m8RP6dSrvglDNQ9BZdi5t7HDBUWXqxGG9Tch9mVqISXpVMmPQeEKy\nVR1cfVHVmujrRBVNzsbwAAAgAElEQVSIxDhvTbWi+CIAcJk5DTP0SBtkvdlbkqHntBnuvXSE\nxbfDfARdj1PPrtsQu879+pzasPTKf+kAkBLic2ts654WOH8Iab4mejr/AgjzHwNgkr7qhg0b\npu4QkGw6BnXbuNVVdxS1VUhCnrTRp1O5ZayCzBupX9YjFZAyOuhbuAGEAoAw9wkAJumrLif+\nsDRDz24yZNei3ytIr9P1rfrP3EqmTDj+KvOfLUs9Tu53ZBWPQzbuOnN26NNd/6WTQm5AWuE4\na3wEVgC8zijDpyISAOi6DeXJ0AMAjWFhq0d/WygmhUlKDg0hpeO9i3wQGRUbG/spLSs/P18g\nphkZGRmbWTk4OTd1dXNzsVF3gAgphrWL+wwX9+lYF1ojMVjNPNm6/2YLUm7fhlEaOFaASXpU\n0yUFrlx6Nh4AdI3djuydqe5wtIjbJNdDK4MjdwdQvuNxlpjKdDHRPcfjf5I5qoSUqY+dcdSr\njKcxOX3d8cZLdYokFAD87KiZWwohVEbUiQhpg8XpvHvFyAq2X6Uzrcat2p0+eUJoeiFFSa6e\njus5r6mqwkRIbeKLxABAkfnqDkRbkBRg5SqqLT4UiQGAIJjuxuVWBnPvhUobNB2T3mYyJprT\nmfWkDVKYqoQYtcizg/eljSHLhspxGSH6LBx9fOwuUsjbe/HDrnFfNxpwm/7LrmlXACAqMgN+\nwyR9RXBkTI2MdWjpIpKS8OU/pVBCAQAQDGXFhJDyZb0N9t1/KjIurczxgrxsbkrS26jI6xdP\nmtu5jpk+t4sjLgWKNATWhdZYmj2hH5P0qKYT8LJyc3MBgC58o+5YtItFy/nD7P+78PbKsqMN\nVk/orFt+LgEpUOfRzue2RT7wezVuQXt1x6JdWs0aRJt+OPrQSUGHPyvInCHFwgUMkFa5k1Cc\neuy8bGKl1xmCxpryV5fQ+YEAkBZ5DQCT9HLZvXu3Yj9w1qxZiv1AVB5h7pN72UUAQGPWUXcs\nmqMwg5uSVdS4ScPSB3Peh/sevvzuY2J2IZjVse3Qpc+YwZ30cMdiVLPxhBIAoDEsdMr/qT6+\n/VnaMKgzXOZPmqAVZ+4lokzFh6hNrsXnAQBNh93fQl+e/rom3TjMPTwhmXL7Aoz7q+S4nnl3\ngCsAwP/0A+lP7YQjY2pkq0dPF5GkkPu8QNTSoPK8u5j/+pNQAgAMfXvlR4eQUkQHbF9xLFhU\n2S7JGfHPdi6e/HLC+nkDnFQTGEJIO2n2hH5M0qOazrxtA/BPAABSkPCaL3Zh4Y9WZYgRG715\nCxYFB+wYHxE8dnR/ZzvbetZmWHCjVHU6Le0bMOFG6OaLXQ8ObWmh7nC0CKtO3/Ujnyzzu79w\nu6PP/N8wT68auIAB0irxhdIqQPqYRsby9De2G8cg/hFRlKjglZJD0xy3b99W7Adikl41irJi\n9yzfQVIUAOibdVN3OJog7eXdfcfOP43nMVjNL51dV3I849nJaWsvCyXFQ64ZybHXT8WGhL/0\n3TrbtILkJyoFJwOphQGdEEgoiioqrwNF5lzhFSd6bfq1kNmHFPGkDRrDTOERapXEIhIAaAzL\nSnuWMNOh8YSkqOBl6YN0BkfaEGYKFRieRsKRMTXqZmcc8SIdAI6cjfadLPvyUlrsxUMURQGA\nceNeSg8OISVIDTuw9Fgw9SVDb1TXoW2zJhwOh2PJMWKIUrlcLpf7PioiJjkPAChKdO/YUiOr\ng5PcOGqNGiGlKOLn6+gbYjZEvTR+Qj/e1aGazsxlrptJxMNsAQCcCPq0ZWAjdUekRehMm74D\nOwTvCCpIfr5v83MAIGh0ecps/P39lR6cpiIYEzetyfhzxelV02J7j548pq81Pn6rStPha+cX\nee+8fHjs65DBI0b179xSD+/ClAwXMEBahQQKAGhMa5Z8FasEoVdHl5YoIIGSKDk0hBTv7Nmz\ncvWTFH1OTHgZ+V+mqPh37jz2ZyWGpR244Udnbrn6ffETReZu2BxQkqEvkRt/d5FP80NLPVUU\nXy2Hk4HUoi5TJ0MkpMSZyULShkn/vkP+p3OFX37bv7aXnTwWFbyQNujMcje2R/Iw0aGliUhS\nkJhDUmw5npgoMu+jQDpV8ZsqZFLIlTaYprgqeCVwZEyNnEa3hhdBAJB4fd2ZZrtHtq/oAsJ7\nemGN/wdp23WUoyriQ0ihJOL09btuSTP0TKOfxs+d1aedrawLPfXhSaDvjuNx+UKKkgRu3zSo\n7Tac8YlqF2FhXiHdgM2kyXiPIv+7fe7Kv08Tkz6JGSZNW7t59h7o1sRE5TEirZjQj7kfVOMR\nzPlbF6bO8Y7ni975bXjc0be9JRZcqkjE8eXrrnwz1Z2SkLhZulIFBAQAgH3nbq/PXHsSeCzi\nnxNsS5v6NpYMOW50V69erezwNJj0mwdjpx7N39988dZv16ozvgwzK2tra2sTg3J3vpRavHix\nKkLURLiAAdIqzQ0YD3OFElGGiAJ5ruqUhJ9SJAEABguXypTX6NGj1R0CKiZvkv5bLCvPBT9j\nFU61kIL4v7Zfl7k8afrzPXGFYgCg6bCHTJ/R2ob5+uG1k9eeAwDv0Y77OR082JXc8yCkLu5m\nuq8KhBRFXXyfO89Jxt63MScipA26XsOuJjI2pAcA3v3/pA1d0w5KilNLeJrqXuTxKUp44Fn6\noraV19NnvDokkFAAwDT+ZhoWn3tT2jB2kGuRIa2GI2PqY+Iwoxvn/l0en6KE5zfOiOs9ZuSA\nnk2sWGW6FfLeB109d/LGE7E0kWDZ1csR0zmo9kkN254gIAFAR892zd5NLuXeHBK27X7z3tvg\nj6mrEgWkWPB+28PUdR44Bw7VAikv/w0ICo18+jKdL27+1+H17cs+fgpzXnuv8I78mPPlAPfh\nXf9H/7vW5rcZf035VVZKH/0YnNBfBibpUS2gx2nrvWfVrg0+YXGp3l5zBk2a2NuzrbmejOnz\nSIFy3p9c74/r66ra0aNHS7+kKEk2Lymbl6SueLRHmW8eAChKlMFNyuDil69cuIAB0h59Wpk/\nDPlMSQR+iXnjGxpV2j87+qB0jM/4p9+UH52GGDZsmLpDQFVn2qTjyvVz9HFz9Or59M/eNCEJ\nADS68aCZ83q0bVry1rMTr6UN+1FrRv9qBwBOLm04/Blb7yZTlOTClQSPCT+pJebaBScDqYVL\n9zpwNA8Anuzyp/ZNLHOZoMRZh19mSNvGtsPLuYhITl9JlLY4Hjj7rVq6DLe96PsaAB5t3fTm\nsLejUUXze8T891u9w6Vtm969v75BCf23h0qbbZvLmHiBysCRMfWhTdk4N8prC1dIUhQZGXj8\n6T8nTSzrWHE4VlZW+lDI46WmpqZ+TsuWfJkhR2dy5myYgokcVBs9u/RR2nCdu7T8DH0xpknz\n5bNbT/V5AgAfLjwDj94V90dIvSgy78yWlecfvq+gj0SUvn726ufZZbdYoigy4vruBUW07bM0\ns5hblXBCfxmYpEe1QGBgIAC4dBmcnXMmKo17ce/GS/uYJuZmZmbmpmZs3QrTOVjhWmUP9tyR\nrm6kz3EePrKfUwMbS1NDHDRFCCkQLmCAtIrj1CnssPU5pOTm2oMDD/5R8fKwpPDztk1hAEAQ\n9CFezVQVI0IK06uX/Puw0i3rNbRr/FMLJzucplV9j/75JG20nLl5bDebr29Q4vPJBQBAEMTE\nXg1KDv88fjTc3QwAaeHPAJP0csDJQGpR99dxjGPLRRSVnxyw5nyr1cNblX73+bGVXGHxim+O\nv8teXzrh5qYnecUbn/fvZSOzD5JTHc/5TQ5PjysUiwvjlk9fNmH+rD5tGsnsmfzizu5tB6P5\nIgCgMzkz+zeUHs/7/PbGie2X4nMBgGnYaqCFvqpir8VwZEyN9Dluf2+Zu27NnjdZRQBAUZIs\nXnIWL/lNlIzOTLb9jJUr3a3LltojVCvc4RUCAEHQp7WTKxnG+Xk6g4gQURQ/9Q4AJulRDUaJ\nDv8163p0VsW9nh9YKc3Q65o6de/aur4pLf5tbFTEs2S+CADe39513LP1+KY4uVDVNHtCPybp\nUS1w4MCBMkcoSpiVzs1K56olHi1xLSkfAHRN2hw8sEKefeaQQsybN0/dIWgpLy8vdYegjXAB\nA6RVmEZtNs30nOl7rzAtZNYi+pLF0104stcp/fz6/pFde17kCQHAYfCa3t8tp4lQzTdjxgx1\nh6ClwnKLAIAgmPM71y19XJB9N11EAgDT2MOR9XUcgGnsbs6gZYgkwtyHAMNVHC1CcmKwms1s\nb7njEQ8AnvmtWvBxYN9Oro4OtlQuNzLozOHA4hJ5mo7pRBez709PCD+9+OATadvQZpAnW/Z6\n+EhONAZn+bIhU1eeF1KUMO/tgbVzztR1bNusMYfD4XA4LBDw0nhpvLT415Gvk7KlpxAE0X3m\n2iZ6dADgcw+Pnn6d+lJz/MucmTjcIA8cGVMvIztP78MugefOB968l5IvktmHwbLu1KvP8BG/\nWTFxhQNUW30qIgGArtvQkiHXYhA0hoWtHv1toZgU4jAOqtHi/NeUZOj1LBwH9P+1lbMdp4F5\n6T5kUZLP/5IBQM+0w98HFtb/slwNKfi8f8nCoPhcAAjcfGD8qSWqjV3T4IT+MjBJjxCSLVUo\nAYD2S2djhl6VunTpou4QtFTPnj3VHQJCSHPk5eXJPM5uP2ltIWPt4ds57/5dNu1RczfP9i3s\nra2srKys9InCVC6X+/nzf/f/CY1KkfZ3HTh3xZjmKgxcG5HCIjoTszVIc0jv4XX0G5W5h896\nGSJtmDh3L3NKPaZOhkhIijDNU0VJgSuXno0HAF1jtyN7Z6o7HI3V6c81N8fNiS0QAcC7cP9t\n4f7f97Hrt8SKWZxUoMSCzMzMT3HR4f+7fivig/QgQdObsgYnoyiAWYuRvkski30uZYslAJCX\n8ubflDfldSZout2nrPf6MnNIIuGXZOjte8+bo6ErlyLNQ2NY9h0z67dRkz7GxsTExH5Oz8nP\nzxeBjqGhIduijoODk6OTLUtDi/yQ9jDWoaWLSErCl/+UQgkFAEAwlBUTQtVGkdneZ4p3/rJ0\nHbpjxWgjWfmO9GdHCkgKAJrOnVy/1IYydL06071XPR65MFssKcp5cDmVPxhLKaoBJ/SXgUl6\nVAtghatamDFoPCHZqg7+yUEIKQte3pGmGjVqVKV9KJL/IuyfF2H/lNeBRmcXRN9asuhWo8EL\nZ+L4tYJQ4qwHwWGvXkW9jonLLijg8wtFJHXt2jUAEOZFXAnOc/f0qG+EA0zqJMz5xGTXU3cU\ntZg+jRBIKEoiLnP87fXi2T+2/eqXeUtYnC3DvEIVCXhZubm5AEAXlpukRNVHZ9qs37Ny9dz1\nr3PK7hIqxW7SY/3Yr2vdJ91cPuvQ29IdCILWbdqmzhxcWV0x6riNPnjQ9ej+o3ci3pFfku7f\nq+vsPm6al5utUZnjLGv7vsMnjOrqouQwNQc+OtUQBE3f1snV1slV3YEgpBS2evR0EUkKuc8L\nRC0NKn8sEvNffxJKAIChb6/86BCqovT/9vKEJAAwWI6bl4+SmaEHgFfni7erb93IsMxbdL2f\n5ra2WPOYBwDB/yQPxm3CkOJgkh7VAljhqhZdTHTP8fifBKS6A9EiWIWDtA1e3hGqgITMiY3N\nAQAiW6juWDTEm/uX9x88G58j+/skiz6cOXT63NFjnr9PnT3MAxcSUjFSkPE07H5ISMjDl/FX\nrl5Vdzi1mK2+TlaekCz6mCwkbUqW26VEfh9zpc0Btsal+1OSwniBGABoDAvVRqo5zNs2AP8E\nACAFCa/5YhcWDrMoi65Ziw1H9t08c8w/6BGv4Oty0zSGafehY8YM7VpBDauOfp1BM5aN9myo\nkki1hZ6Fs9fyrRN4caEPn8bExHxMTssvyC8UgZGRMdu8jqOzc4t2HV0bl7226JsP3LF3hG09\nS/xL+0Pw0QkhpALd7IwjXqQDwJGz0b6TW1TaP/biIeniKMaN5V+/GiFVSwiIkzYaj5xloVPO\nVg6U+PynfGmTkHWP0mSEIzzmAUDG4zeASXr5iETFd+wMBtZClAufHhFCsnUe7XxuW+QDv1fj\nFrRXdyzaAqtwVCY7u2RzRAabbaDeYBBCCKnAM78Vq8+/qLSbhMz5188nOi5177IhOpg9UD6K\n5L9+EhYSEhL2OEq6tCCqpu51DJ7lCSlK4ns72fu3BtKDGS/2c4XSDendnL9NIee8O1kkoQBA\n1+hn1UerGcxc5rqZRDzMFgDAiaBPWwY2UndEmozGtOgzfmGfccIPb95w0zMLSEadujY2Deqb\n6MneAZogCI5t03btO/Qb2NOqnD6omvQ5TXr0b9Kjv7z96br17XDBFIQQqpGcRreGF0EAkHh9\n3Zlmu0e2t66gM+/phTX+xRvKuI5yrKAnQuoVklC8I2GfTuX+pAWZN1KFxcWKMosW9S3cAEIB\nQJj7BKCv4qPURIMHD5Y2Dl8O4DDKmR6h9TBJjxCSrU6npX0DJtwI3Xyx68GhLbGwRhWwCkdl\nxo4dK20wDVpcOrsOADZv3lzlT1u8eLFiwkIIaQrp8umo5ki6vaskQ0/QjTp29bRv8hPj1Zn9\n979uwq3DcmpmY/AquQAAuI9PLjvbdMtIHGlSGkoc//JRSEjI/bDIdFy3SaGcJ7SCpf8CQMyR\npRfMl/duY1/4KWKzd7D03brdh5bunJdwf+WqIGnbvF0b1UaqQQjm/K0LU+d4x/NF7/w2PO7o\n295ST90xaTqCaevU3LbCLta/zN/XVtfExMRAD5+qEEI/7MOz4PsRz6PffszOzSskaWwTkwY/\nubRp7+np2kjdoSGkRCYOM7px7t/l8SlKeH7jjLjeY0YO6Nnku+23C3nvg66eO3njiZiiAEDf\nsquXo4k64kVILh+KxABAEEx3Y2Z5fbj3QqUNmo5JbzPd7zvQmcVzDElhqhJiRNoLn1UQQuUg\nGBM3rcn4c8XpVdNie4+ePKavNeaMlQyrcNQoPDxc3SEghBBSClKQsPLAv9I2277Twj+9mlvr\nA0Acz790Nwar2Ya9px6e27Dp7FMAiL24OnbgaQd9vPlRMO67ZyEhoaH3w5OyZOwqTRC0eo6Y\nKq4WE+fp7mYPwjMFFJl3etNiP4KgvuwVTdD0pgwtXuu7kHdz85brL94lS3eSJgj60N8bqStm\nDaDHaeu9Z9WuDT5hcaneXnMGTZrY27OtOdZtqxWTbWPDVncQCFWJn5+ftDHg95EGuAGPygnS\nX23b+PejuMzSB7PSUz/GxYbevHLSvuOCv+a6mMrI3yCkEWhTNs6N8trCFZIURUYGHn/6z0kT\nyzpWHI6VlZU+FPJ4qampqZ/TsiVf7jDpTM6cDVOwQhbVZDyhBABoDIsKVst7fPuztGFQZ7ie\nrB2UCFrxlV8iyvz+XYSqDEedUC1ECe+HPZano3nrn51ZuN1FFQUEBACAfedur89cexJ4LOKf\nE2xLm/o2lgw5nhBXr16t7PA0E1bhIIQQQoqWcmdPhkgCALrsNtu955e7BR0AEDpuI1bN/TR1\n530uRfIPXE/aNqziQk0kr9zk2NDQ4OCQ0LcpeTI7cBq3+OWXTr94dGxkgTc/1UIQerM3zX4/\ne5t0ffuSDD0AOAxZ0ezLw1FRdsSzt59K3mrUY6knG/MNVRcYGAgALl0GZ+eciUrjXty78dI+\npom5mZmZuakZW7fCHBuuyYRqu/ycHDEl734lbBMTzDlX6vz589JG92EjMEmvYkXZz+bOWPe5\nqNxlftLfhi2f9mHFgR2umKdHGkqf4/b3lrnr1ux5k1UEABQlyeIlZ/GS30TJ6Mxk289YudLd\numypPUI1igGdEEgoipIxTVyKInOu8PjStk2/FjL7kCKetEFjmCk8QqTNMEmPajZK/Do8KPhB\nRBIx1HuhS/ExSYGPj488Z7fbecrZFifPV9HRo0dLv6QoSTYvKZuXpK54tARW4aiGg4ODtKGj\nX7xUkZeXl/rC0V7jxo2r2olNxnuv6FxHscEghDTVw6uJ0obHolkVZei/8Jg6Zud9HwBIuRMB\nmKSvnqKsxPCQ0JDQkP/iZC8JaFLfycPjl04eHvY2xiqOTYOx6njs8DU+uOdI8KsEaZETTcfQ\nvf/kBaObfd+ZIHRa95ry17R2Kg9Toxw4cKDMEYoSZqVzs9K5MvsjpAGSnwWdvHYvLu59Wm65\nQ97f8/O/aoRZ52qjyLyVq7dI2+vWrVNvMJqF2r9oS+kMPdPAtEHDRsZE3seExMx8ofQgKUje\nvGCX35GFFVRkIlSrGdl5eh92CTx3PvDmvZR8kcw+DJZ1p159ho/4zYqJg5aopqvL1MkQCSlx\nZrKQtJH1i83/dK5QUjzj8Nf2ljI/RFRQvIMenVnuxvYIVQEm6VHNxXtxc+ueE2+4fACwdB34\no6cTBN1YjnFYhGoUrMJRje8n+vTs2VMtkWi5rKysqp2YV35lA0K1Aj8nLeVzhkjusjN7Rycc\n0K6ykJwiACBouhOcTeXpz2R7cJjbeEJSmBMGMEzJ0WkmsjA9Iiw0JCT00asPZDm/czqTs9bH\nu5mthYpj0xKsOi3mrd81I4ubmJpBN7SsZ2PJJL65iOiw7Nw8jOs2sm/n9otTPUN1xYkQqqXi\nrm9bcDiEkvtOpgQDB2kUQ/zixQt1x6CBcuKO/o9bXEmpw6o/avaCwe52Je8mPA7YuvNUQr4I\nAArT7+98NmFBa7yNQRqLxrDsO2bWb6MmfYyNiYmJ/Zyek5+fLwIdQ0NDtkUdBwcnRydblqwl\nwRGqgdzNdF8VCCmKuvg+d56TjGGBmBMR0gZdr2FXE9kLpfDu/ydt6Jp2UFKcSDthkh7VUM/O\nb1l3Jry8Qb0Sbdu2zsvKTP2UmCUoTtgQBL1zv2GtmjVt2tTZnIVT+apu3rx56g5BG2EVDkIV\n0GGZmRnqAIAZ7hKNaidKnHn5yIEboc8y836g5gyw7Kx6UoUSAKDrNpD/O7Rm0HlCkhR+VmZc\nGogiC6Ie3Q8OCQmPiOaTMm7jDa2bdOzY8dal4wBA0IwwQ69suqbWP5nKrvMwrDd66UIVh6PJ\ncE0mpFWEOeHLjnyToafT5R17KTNhCKEaJe70A2mDzuSsObi9mTGz9LsN2w/YdtB+1vi/PgtJ\nAHh+6hm0/lUNUSKkQgRN39bJ1dbJVd2BIFQtLt3rwNE8AHiyy5/aN7HMvQglzjr8MkPaNrYd\nXs6diuT0leIl+jge9kqLFGkjHONGNVHcdZ/VfmElL2k6xk2bmcjsuWLFKgCgJILYyBC/o8de\npPApivwg4MxrJ2MtR/RDunTpou4QEEIabvfu3RW+T+Wmp37+nJL0MSroTkShhKIk+kMXbOwh\na9IrQjUfRRbsnDvr36T8Kpyri2Vn1WBAJ4RiSiJKpwDkzAxwRSQAEDR9pQamOShR3PNHoSEh\noeFPM2WtdMKytHPv2LGjR8dWTawBQJqkR0iT4JpMSKvEHDwhkFAAoM9pOnHaqFY/2XFM8C8m\n0gT/e58rbTTov7hMhl6KYei8aEij+WfeAwCfexcAk/QIIVQL1P11HOPYchFF5ScHrDnfavXw\nVqXffX5sJVdY/Bjr+LujzE9IuLnpSV7xpif9e9koNVqkbTBJj2ocQWb4siPFGXqCzuo9ZurA\nXp04+hXNyyZoeo7teqxt43Hee/6ZR58/BO1cbWG1enhTlcSLkCJhFY4K+Pn5SRsDfh9pgJWp\natWgQYPKejRsCgAwYOTwtxeO+V66n7B36fSCLQcH2bNVEB5CivXp9sbSGXoGi80xM5LzGsTA\nsrNqaG/EvJUlkIizgjIFPc30Ku0vzHvIE5IAwDBorvzoNMGMsSOSc4TfH9cza9ihY0cPj46u\nDjb4C0YIIY1x60UWADCN2+zdv9wcNxlEGiSuUCxtePaqX14fm1+7wZn3ACAWfFRNVAghhKqJ\nwWo2s73ljkc8AHjmt2rBx4F9O7k6OthSudzIoDOHA4tL5Gk6phNdzL4/PSH89OKDT6RtQ5tB\nnmzZ6+GjCnxOSRYp4qbRxkYDZ0hgkh7VODfW7JNOyiboBlO99/dxkDcTQ9BYvy/1zZo1/mZS\n/n9nVof9erqjaeXjsAjVKFiFowLnz5+XNroPG4FJ+tpCz8J+7MKdhnmTjz9PP718dZtTWxvo\n4oYmqJb538U4acOx87CpYwY0scBNoFWku6fVLf8EALiwK7jn6sr/zr4+dUraMG+Ff5TlUiZD\nzzSp18G9Y0cPjzbO9TF1o1QZGcWrMpqam1f5q6bIvEVL1krbPj4+iogLIaThovgiAHCZOQ0z\n9EjDpIkk0kYLQ0Z5fUomcVISgSpiQgghpAid/lxzc9yc2AIRALwL998W7v99H7t+S6yYxfc2\nlFiQmZn5KS46/H/Xb0V8kB4kaHpT1gxXWcyaZMXsmQr5nGvXrinkc2oUTNKjmkWY9+T0xzxp\nu830LfJn6IsRzAnrZwaN3yKhhAfWXO64Y5TiQ0QIaTqKzFu5eou0vW7dOvUGg0qh9V0y/+SI\n5WLB+22XPu4Y1Vjd8SD0Y8JyhQBg6jJq8/zyNjlDStFw0O+MgC0iikp/tnfTJfaiwW4VTNDi\nRp5dG5Qsbf860k5FIWoKgs7qOe6Pqf3b4RQ41ZgwYYK0cfhyAIchI1VGSQqPnzhXpvN3xLGx\nsUqJD31BCovoTCy4QZqjSEIBwM+OuLQV0jQkRUkbhuXfytB0cKIt0hD9+vVT7AdqZPIMaQw6\n02b9npWr565/nVMkswO7SY/1Y7+udZ90c/msQ29LdyAIWrdpmzpzcIsfpGCYpEc1y+e75yUU\nBQBMozZLfy13dakK6Jm6j7c1PhqfkxN/PjB9cB8LLKZXDN67yAeRUbGxsZ/SsvLz8wVimpGR\nkbGZlYOTc1NXNzcXDVxpBGkx8YsXL9QdA5KBwWrmydb9N1uQcvs2jJqh7nAQ+jG5YgkAdJr9\nG6YvVYzJdl/Srd66O0kA8PDkpklPPGeM7d/U8dsEPEVmcD+GBl48ef2hdHzW1HH8IGuWWgKu\nvSiSf/Po+jsqMC4AACAASURBVId3XDp37tzZ85dGeB+udpTA37+4RqT8JD1SMEqc9SA47NWr\nqNcxcdkFBXx+oYikpMPWwryIK8F57p4e9Y3KrdFEqOZroq8TVSASU+qOAyGEEEJIbrpmLTYc\n2XfzzDH/oEe8AlHJcRrDtPvQMWOGdmXRyh2t0dGvM2jGstGeDVUSKdIumKRHNcub/3Gljfr9\nRutUdQzbbXijo5teAMDNy4l9ptkrKjatlfU22Hf/qci4tDLHC/KyuSlJb6Mir188aW7nOmb6\n3C6OpmqJUHtgFQ5CTfR0/gUQ5j8GwCQ9qmUa6NLfFoobsvD2Ww3azPTplzT92ptsAMh8E7xh\nWTBB17M0LF7RdMkfMxMTU/KFZEl/XXbztWv7qyfWWqihuV5CxtcVX7OTXvuffB1wal/Dpj93\n6dK5k0cbUyauh4y0xZv7l/cfPBv/7R4QJciiD2cOnT539Jjn71NnD/PANSdQLdXHzjjqVcbT\nmJy+7jgZCyGEEEK1Bo1p0Wf8wj7jhB/evOGmZxaQjDp1bWwa1DfRk72lJkEQHNum7dp36Dew\np1U5fZA8Zi5eaoLbJJUDRwlRzRKeWbzeiGtn6yp/CNvRHeAFAKQ/eQyYpK+e6IDtK44Fi6hK\nJslnxD/buXjyywnr5w1wUk1g2gCrcBD6XnyRGAAoMl/dgSD0wzpxWG8Tcl+mFnY1welWqkbQ\nWJM2+Zrt23L89ivpEYoU8HKK342OSyrd2dShy7LlXg3xCVxuvkfPfox6FBwcEno/Il1QPNeB\nosiPr8KPvgo/vse4RYdfOnfu7O76EwNTkkijPfNbsfp85asxScicf/18ouNS9y4bUuWJ6Qip\nUatZg2jTD0cfOino8KcegT9ihBCqlaq5vWPMvXNn70VTX0aMCQKfnlDtQTBtnZrbVtjF+pf5\n+9rqmpiYGOhhClUBWrVrL3ODNgSYpEc1TcqXGqaWhhWkHgk9vYrmazP0itcvFeZFAIxRWHDa\nJzXswNJjwSX3W0Z1Hdo2a8LhcDiWHCOGKJXL5XK576MiYpLzAICiRPeOLTWyOjjJjaPWqDUE\nVuEg9D1h7pN72UUAQGPWUXcsCP0wt0muh1YGR+4OoHzH42Vb9Qg6e9CsDR06h/tfu37vSYyA\nlDEB0cK2ZZ9+A/p1ccVc8o8h6I2auY9v5j7OqyDqcVhwcHB4RDT/yzcsEef+F3rjv9Abu9k2\n7p06d+7SWb3BIqQkSbd3lWToCbpRx66e9k1+Yrw6s/8+t6SPDsupmY3Bq+QCAOA+PrnsbNMt\nIx1lfxxCNRirTt/1I58s87u/cLujz/zfME+PEEK1UYsWLap2YlFm7NFdO24+Sy45wqrbcvq8\nuQqKC6Eagcm2sWGrOwikHTBJj2qWLFHxuqOm5S9/QdBNLly4UMGHEDom0gYp5FbQDVVMIk5f\nv+uWNEPPNPpp/NxZfdrZynr4pj48CfTdcTwuX0hRksDtmwa13WaKJSHVg1U4CH2vKCt2z/Id\n0r2i9c26qTschH6YRcv5w+z/u/D2yrKjDVZP6KyLI9rqYO3iPsPFfTrJ//AmOj45PT8/v1Ao\nMTA0Mjbl2Du71DXFZXurhaAbNOvQo1mHHl78tMf3Q0KC7z2O/iT5Mt1TmJN879rpe9dOS19S\nlLBQQumXv+0fQrUIKUhYeeBfaZtt32nhn17NrfUBII7nX7obg9Vsw95TD89t2HT2KQDEXlwd\nO/C0gz4Oy6Dap+nwtfOLvHdePjz2dcjgEaP6d26phzPHEUJI41HkkxtH9xwLzBIXD+ATND3P\nYdOn/94Z7+oRQqhq8GkQ1SxsHVq6iASADJGkHrOK6+SQIl5xi8A1NKouNWx7goAEAB092zV7\nN7mwmeV0JGzb/ea9t8EfU1clCkix4P22h6nrPKq+WwHCKhykPc6ePStXP0nR58SEl5H/ZX6Z\nyOU89mclhoWQshAjNnrzFiwKDtgxPiJ47Oj+zna29azNcExb9Qg6y86ljZ2LuuPQXHSWZYce\nQzr0GFKYFh8aEhwcHPI6MatMH7IoadTIKe08funk6dnepQHetaNaLeXOngyRBAB02W22e8+3\nqGDDRULHbcSquZ+m7rzPpUj+getJ24ZVvNYmQjVOQEAAAICxU4/m72++eOu3a9UZX4aZlbW1\ntbWJQXnjBsUWL16sihARQggpWkFS5J6dvmFvv97Vmzr8MnfuDNd6BmqMCiGEajtM0qOaxZGl\nE5ZDAsCjTEELgyputi3MfiJt0Jl1FRaZ9nl26aO04Tp3afkZ+mJMk+bLZ7ee6vMEAD5ceAYe\nvZUdnqbCKhykVeRN0n+LZeW54GfcVgPVSnSmTd+BHYJ3BBUkP9+3+TkAEDS6PCUH/v7+lXdC\nqObRt7TrMcSux5CJ6fEvgkNCgkPCEjMFJe+K+bwHQZceBF3Ss2j0yy+enTw7NWtkrsZoEaqy\nh1cTpQ2PRbMqytB/4TF1zM77PgCQcicCMEmPapujR4+WOUJRogxuUgY3SS3xaLD1i/8s5yGf\nLGn98ccflX7Otm3bFBWSllg1f265qxVSX7/82bNnV/w5vr6+igsKIbWhJPz/nd1/8GKIQFK8\nRBaNYdZ3wqzxfdrgjHOEEKomzOigmsXNihWWUwQAT8/Gw6Iqbo2TfP2ZtME0aqewyLTPHV4h\nABAEfVo7uZJhnJ+nM4gIEUXxU+8AYJK+irAKB6GKmTbpuHL9HFxIDdVSEceXr7vysvQRSkKS\n5fVGqkIK8tIyC/SMjNlGLLy4KImFXYshdi2GjJ/54dWj4OB7oWFPMwRff/uC9I+3rxy/feW4\nacOmnTt1Gj+khxpDRagKQnKKAICg6U5wNpWnP5PtwWFu4wlJYU4YwDAlR4cQqq0+xsVV2idO\njj7oRyUnJsjTLSFBrm4I1WrpMfd27jjw4jO/5Ej9Nn3mzZnwk0klBV0IIYTkgUl6VLPYD2oE\n3lkAkBZ5OEO8y7wK+2xT4vOhxauCW/7sqtjwtMqnIhIA6LoNLRlyrT9KY1jY6tHfFopJIU6f\nrzqswkFapVevXnL3pVvWa2jX+KcWTnY4UxvVUjnvT673f6XuKNBXwpyE6xfOBYb+l55TPOTE\nMOK0aNW29+Df29iy1RubxiLots3dbZu7j5+Z/+rR/eB7wWGRb0oqcgAgKyHqyskoTNKjWidV\nKAEAum4DI7lvU6wZdJ6QJIWflRkXQkrh5eWl7hAQQggpnUSYHnBk98lb/0mo4tt1Bqv+iJlz\nh3jYqzcwhBDSJJikRzWLRevJLPosPkmRgoR1p6N3jP/hzUJ5D7dF5Aml7e4D6is6QC1irENL\nF5GUhF951y8KpWOsRBX3KUCAVThIy8yYMUPdISCkOg/23KEoCgD0Oc7DR/ZzamBjaWqIc06U\nRJjz4ca1oIinUZ8zMgk9E45VnTadevbu0sbgS/6Mn3x/ztxtPOE3CxmI8niRoYFP799sP3TB\n0tEe+L+jPATdsLl7r+buvbz4vMchwcEhIRExn0qG/xCqdQzohFBMSUTpFICclw6uiAQAgqav\n1MAQUoaePXuqOwQN5+Hhoe4QtFTXrl3VHQJCNUXCk+s7fE+8zykeYycIwrnziDnTh9bRo6s3\nMIQQ0jCYpEc1C123/pKe9VcGJgLAB/+Vfi77RrX9gY2Hi7L+W739obRtaDOgnwUOeVSdrR49\nXUSSQu7zAlFLg8rz7mL+609CCQAw9HFCZdVhFQ5CCGmqa0n5AKBr0ubggRVsXBFCmRLun1yx\n/Uq2WFL8Oic/I/VTzMuIyxfarN66xJHNFPOjl/6xvUyGvgRFSR5d8FlGmGwa1Ux1QWsrHRbH\nvdcw917D+Lz3oSEhwcHB0UnZ6g4KoR/W3oh5K0sgEWcFZQp6mulV2l+Y91B6CWIYNFd+dAih\nWmbhwoXqDkFLzZ07V90hIKR+4oIEv907L4d/3UpDz8J50tx5PVpYqzEqhFBttHnzZmnDVI4F\ng7UWfjWoxmk+YUUDPToAUJTowsa5fvfeynliIfe/DfM2SRdpB4Bhy4crK0Tt0M3OWNo4cjZa\nnv6xFw9JCwSNG8u/fjUqS1rhJ63CkRNW4SCEUK0gnYbVfulszNArVeaLk3O3Xv6aoS+Fnxq5\nfOaaLDEV7LP1Q6EYAAiC1syz15iJMxYv/mPiqKEdHMxKOkdfWBmSXaS6uLUei9O459CJ3ntO\nHtm+Vt2xIPTDuntaSRsXdgXL0//1qVPShnkrrEhGCCGEUA1Bvbrr5zV+XkmGniAYbgOnHz60\nCTP0CKEqcPqCgcNg5cNKelTj0JhWa5b9Pm31GaGEosiC89v/jIwYOH74oBYNy90clCJzH966\ncuBIQNaXAdkmfRYPsDFQVciayWl0a3gRBACJ19edabZ7ZPuK7sZ4Ty+s8f8gbbuOclRFfBoK\nq3BUaf3iP8v5K/i1tvKPP/6o9HO2bdumqJAQQhrMjEHjCclWdVjqDkSTUWTumg0BJaum0/Ws\nmjW3r1/PvICX8uHNyw/pAmHuq2W7z6Q+ywAAum79+evW/uJo/vX84aMir+9ee+guAFAU6bc/\nutOSVur4d2g1y8Yt1R0CQj+s4aDfGQFbRBSV/mzvpkvsRYPdKpiOxY08uzYoWdr+daSdikJE\nqKqys4sXOCEIBpuNwywIIaSZBGmvD+/cefslt+SIcaP2M+fNdvtSxIUQQkgZMEmPaiLzlsN3\nzM+dte2GdIz1fZj/yvCABi5tXZs1dXH+ydLUxMjIkBAV5ubm8j69j4qKigh7lMIXlZxu0eL3\nLVPd1Re+hjBxmNGNc/8uj09RwvMbZ8T1HjNyQM8mVmVTC4W890FXz5288UQs3WfXsquXo4k6\n4tUQ3T2tbvknAMCFXcE9V1deWINVONXxMS6u0j5xcvRBCCF5dDHRPcfjfxLIXmIdKQTv0c4P\nArG0bd6i14pFk+2MirfsocjcG4c3Hwp8lfzveemRdgtWfZOhBwCgtek7Z3bEc9/n6QCQGXUL\nAJP0CKHKMdnuS7rVW3cnCQAentw06YnnjLH9mzp+m4CnyAzux9DAiyevPyQpCgBMHccPssaZ\nW6imGzt2rLTBNGhx6ew6KLVyaRUsXrxYMWEhhBBSFEoY7n9k36mgXLK4+I1GN/p1tNfkQe5M\nLH5FCCElwyQ9qqHqdZqy37juJp+jH/JFAEBRVELUk4SoJ/6Vndis19Tl0/ro4D2EAtCmbJwb\n5bWFKyQpiowMPP70n5MmlnWsOBwrKyt9KOTxUlNTUz+nZX+tV2Ny5myYgrtoVAdW4SBNNWLE\nCMV+4NmzZxX7gQgpW+fRzue2RT7wezVuQXt1x6Kxoi4Wb5Oko9/EZ9U0i1LbnhF0477T1me9\nHH0pKQ8ACIKY6Goh80Pcprn7zrgKAKK8JyQFuDsBqvmqtzgQzhxSjDYzffolTb/2JhsAMt8E\nb1gWTND1LA2LB7uX/DEzMTElX/j129ZlN1+7tr96YkWoesLDw9UdAkIIIcXI/fB4z87dD+Nz\nSo5YNes+d+6UppzKV/dECCFUfZikRzWXdas+2462PH/kWODdiDyy8h26Deq6DB01eZBHYxXE\npiX0OW5/b5m7bs2eN1lFAEBRkixechYv+U2UjM5Mtv2MlSvdsRakerAKRwU8PDzUHYI2Kigo\nUHcICKlZnU5L+wZMuBG6+WLXg0Nbyk4Po2r6Xypf2qjf26t0hv4L4rcZzS4tewAAQDCtmLIn\nFuqbdwK4CgAUhclLVDvg4kA1AUFjTdrka7Zvy/Hbr6RHKFLA+zLiHR2XVLqzqUOXZcu9GurR\nVRwkQgghhJAUReYFndp72P+BsKT4Std68LTZo7o1w1nKCCGkMpikRzUaXc9m5Mzlw8al/O+f\noCfPX0W/iS/4sut8CR2WhUvLlu06dOnl0RQL6BXOyM7T+7BL4LnzgTfvpeSLZPZhsKw79eoz\nfMRvVkwcZlIArMJRtoULF6o7BISQViIYEzetyfhzxelV02J7j548pq81C2/FFSypqPjvo2uP\nujI7GDbsBPAAAChJUXkfQtP5ugY+ltEjhORH0NmDZm3o0Dnc/9r1e09iBLImmlvYtuzTb0C/\nLq4MvLygWsLBwUHa0NGvJ214eXmpLxyEEEKK8de0yVG8wpKXli5d5swc1dCQkZOdXbUPNDHB\n/U8RQuiHERRVeYEyQjUERfI/JaXk5ubl5uaKCF22MZttYlqvnjXm5lWAkhR+jI2JiYn9nJ6T\nn58vAh1DQ0O2RR0HBydHJ1sWDf8PFIkic/xLVeFUQFqF48BmqiAqhKrj5MmTFXegJILLV25I\n20OGDKn0A0t2x0SotggICAAAiTjT/8y1HLGEIGhsS5v6Npby5GlWr16t7PA0Q79+/aQN73NX\nnGXNgSCFKQOHTJe2r127JvNDKDKr/8BxFfdBqCbw8fFR7AfiREYFokj+hzfR8cnp+fn5hUKJ\ngaGRsSnH3tmlrikuHosQQggh9St5dFIUfHRCCKEqwPIdVJsQdFb9Rk3UHYWWImj6tk6utk6u\n6g5EK2AVDtI8lebUKTKrJEmPCXikkY4ePVr6JUVJsnlJ2byk8vqj6rBgyF7KnkbXV3EkCCkP\n5tRrMoLOsnNpY+ei7jgQQgghhBBCCNVUmKRHCKEaytrFfYaL+3SswkEIIYQQQgghpHJpIoll\nOfPeqkDAe6XHaaaoT0MIIYQQQqi2wyQ9QkixqLdvYu0dHdUdhubAKhyEENIM8+bNU3cICCGE\nEEI/YN7C3Tt9ZpW3Ps0PeRl0bOuBqyevBFT/oxBCCFXfzp071R0CQgghTNIjhL4jEfIzs7IE\nlJ6lpZku/QfWUpcI02+d2LL/+hvchQghhBAqo0uXLuoOASGEkCoUFfJlbVclG4vFUmYsCFVL\nXvzdeQthR/Xy9GJ+wvG/va9FJCswMIQQQtVka2ur7hAQQghhkh4hVMq7h9fPXbvzIjpBSFEA\nQBD0Ok4/Dxw0tEc7u9LdxPy0l89eJqfn5Ofn5+XlC4qERUWCrLSUhI9JeUJSTbEjhBBCCCGE\nkNokPQs6eyM07v17bhZf/rNwfjOq4XLj785bROzYMrNqefrExwGbt59M4osVHhhCCCGEEEK1\nHSbpEUIAABQlvLr9z6PBH789SKZEh++JDo8cufKv39sAAEXmXtyx7nzoWxEld20Iqg5KeD/s\nsTwdzVv/7MxiKDschBBCCCGE0Pdi/X0WHQ+j8CkJaaLc93fmLYIfzdNT4qwr+3xO3IkqOcIw\nbKiE6BBCCCGEEKqtMEmPEAIAiDq1rEyGvrTHZ9Zuq3foj45WJxfPvPw2p+KPIogfWCEffUWJ\nX4cHBT+ISCKGei8s3oKekhT4+PjIc3a7naecbdnKjA8hhJASkcIiOlNX3VFojvt37xjryEgk\nUJKCkvadO3dknlu6D0IIyUOQFfwXZuiRJprRzW7f3XgAyH1/Z/5iYvsWLwtZf16/lxUbvGXz\n3tfpgpIjth0GL5o3WlmBIoQQQgghVAthkh4hBGJ+9JrL70pemjZxbevQsI4VO4/3OfFjdGRU\nEgDc37WuO9OpJENPEHRjc0tLCwtjfR2SlEgomoGxkbEx28bOuXVrV/X8M2oz3oubW/eceMPl\nA4Cl68AfPZ0g6DJTEQghhGomSpz1IDjs1auo1zFx2QUFfH6hiKSkKx4L8yKuBOe5e3rUN8L1\nUaruxL49lfbx9fVVQSQIIW0Qs99P+CVD79xtzIhfWzdqVI9Fx7nLqNbrNWc7jb5gT1AcAOTE\n3Z6/iNixZYZ5hc+eFFV0z2/XnothJcvv0ZmcoTMXjuzsoIqIEUIIIYQQqj0wSY8QgqQbh79s\nQk/8OvGv6f3alR5QSnp4Zo73eVKQuHxDkvRIE4+BU8aNcOLoqSVazfPs/JZ1Z8LJyipv2rZt\nnZeVmfopMUtASo8QBL1zv2GtmjVt2tTZnEVXfqQIIYQU4M39y/sPno3PEcp8lyz6cObQ6XNH\nj3n+PnX2MA9M8SCEUM1363WWtNFs7KYNQ1zUGwxCCkX0mPk3jbbQ9+ZbAMiJC5q3CHZs8TLX\nkX2Dwuc+9/X+Ozz+6/J7Vs17LFw4xZ7NVFG8CCGEEEII1R6YpEcIwYu7n6UNs6azZvZvV+bd\n+m4jF3cM3nifK12/0dRx3N8LB2PKQFHirvus9gsreUnTMW7azERmzxUrVgEAJRHERob4HT32\nIoVPUeQHAWdeu2YqihUhhFC1PfNbsfr8i0q7Scicf/18ouNS9y4bUs4wOEIIoZoiukAMAHSG\n5dKBzuqOBSGFI7rP8KHTF++48QakefrFsGPz93l66lng4b8PB+aREulrGt2o18T5U/u2wRsZ\nhBBCCCGEZMIkPUIIwnKKpI2Wk9vL7NB8TCe4f17a7jinBz5jK4ogM3zZkeIMPUFn9R4zdWCv\nThz9imriCZqeY7sea9t4nPeef+bR5w9BO1dbWK0e3lQl8SKEEKqWpNu7SjL0BN2oY1dP+yY/\nMV6d2X+fW9JHh+XUzMbgVXIBAHAfn1x2tumWkY7qCbcW8vPzU3cICCFtVEhRAKBr2tUQ1z9B\nmonoMnULjbZk27VoAMh5FzRvMezc7GX2JU8vyos/+vfmwGefS05gN3ZfsHh2S2uWeuJFCCGE\nEEKoNsAkPUIIPguLp7p3tJS9gr2uWSeA4iT9L+a4yr3C3FizTyChAICgG0z13t/HgS3niQSN\n9ftS36xZ428m5f93ZnXYr6c7muL/C0L/Z+8+46OoEz8A/zaNEAgBpKkgigoKVg4rosjZPVE5\nRdETG39F5EQ9Fc921sOKItazYwEVu55nOTvHnWIvoGIDpUkLYEjb7P/FYkQEEpLNbALP8+HF\n7OxvJt/MbiZDvjszUK/Fi7+76PZXktMFnfc4+6wh27RrHEKYOueJ5Ydl5219xS33Txx3xYix\n74YQPn/04s8PfaBLYwft1ZKfn5/uCMC6aOPcrC+KykJVt6+CBq33oCszM86/5smPQwiFX74w\n7NzYjVee0iIr9s2E8VeNenBG5U3ZMnL2OGLo0CP3yIn5zAoAAKxORroDAOlXeT26tjkrP4c7\nM7tt5XTzTPuN1Chd/PYD3y5OTvcYfHX1G/plYjnHX35qRiyWSJTefsljqc8HQErNeOnmeWUV\nIYRGBT2uv/KMZEO/crGsXQb8bVivdiGERLzo9memRxYSgBo4YMMmIYSSRW+VqulZq/U64Yrh\n/bZNThd+8a/Tzr3t4RvOGXbVmMqGPq/dtmdfe9eZA3pr6AEAoEpOygF+scr/SMeyf5n0f+0U\nmfnywxWJRAghJ7/HX/fpUIM15Lboedwmze7+urDw64efm/vHA1s5mZ76a8yYMasfkKgorv7g\nEMLAgQNrmwmiNfGpacmJXucMbZVV9Sfeep10zKg3rwkhzHjpndB/k7oNB0At9Dh1v3D6uHjJ\nDzf/d84Zu7RJdxyoQz2Pu+yvmRePePS9EELhF88/+MWy+bFYRvcDB5154oH5bvoAAADVo6QH\nSI8p/152B+IOff+UVdO/Y+xyxMZ3j/gwhPD8Y9MOPLlzqrJByo0fPz61g5X0NDivF5aEEGIZ\njY7v2qI643MKerXJGTmnNF5a+FYI/es4HQA116zTUWf1efPaV35447oLf3fddbt3bJruRFCH\ndjnm4gsyLr384UmVc3IKNh901jn7bdt2NUsBAAArcNlqgPSYML8kOdF9z3Y1XknBFj2TE3Pf\n/l8KMgFQZ2aXVoQQMhttVP0zzNplZ4YQ4qUz6zAWAKnQ67SRf9qtQ7x05nXDjrv05nFfzS+u\nehlosHY8+qKLjtqx8uHmfxiooQcAgDXlTHqA9JhRuuy+fds1zV71qFhu7uouYp+d2yk5Ubr4\nnRCOSVk4SLVmzZqlOwKkWZPMWGl5oqJsbiKEarb0s8riIYRYxqrvXg9AtK666qpVPpfYsHHG\n90srSie98NCkFx7KK2i1/vrrt1mv2erPjRg+fHiqM0IUehx5wcUZIy5+YGII4dMHL7wq+4rh\n/bZOdygAAGhIlPQA6bGgrCI50WLVdyaOZTZ/5JFHVrOSWFbz5ES8dFYKs0HKPfDAA+mOAGm2\nU37OvxYUV5QveGF+8X4tV/cBrKTSxRPnlMZDCNlNtqn7dABUy4QJE6o5sqhw7leFc7+q0zSQ\nVt37//WyzKsuvG9CCGHCvedfHa44R08PAADV5nL3AOlR8HM3P+/ntr4G4mVzlk3F7M8B6rW9\ney+7DOwjN75WnfGf3n9/cmK97fero0gAALWx7R+HX3F8r1gsFkJ4697zr3nik3QnAgCABsOZ\n9ADpsUVe1luF8RDCf+cXb9tkNVe8X53ShW8nJzJzNkhZMgDqQMd+R2Y/eXVZIjH3vVtGjC84\n54+7rObe9LMmjb30hR+S0/sc1SmiiABUZciQIemOANF56aWXqh7UdLvfd/rw5a8WhRDevOe8\n8qX/16P1Kq8YtPfee6cwHgAANGhKeoD02KVt3luFJSGEd8d+Hc7ZtmYr+eGZ95ITOfk7piwZ\nAHUgp6DnuXu1v+yl6SGEiWNGnPh271MGHrzVFr8u4BPxebO+feO5R8c8MzGeSIQQWmxxXL92\neWkJDMBv7befq5uwDhk9evSaLjJx3B0TV/2skh4AACop6YFf/GXQ8VVeM706Y+67777UBFqr\nde63cbhyQQjhx0l3ziu/cb2sVZ9QuSqJ8offWHYr+tY7d09tPABSrsep1/SdPvjpKQtDCPOn\nvHbFea/FMnNbN11205Nzzzx12rQZS0rjleMbFWxz6aUHpycrAAAAAFBnlPTALwoXLEjJGKqj\n1e8G5WUOLYon4sXfXfbAZzcc121N1zBn4sh3Fpcmp/c+pEOqAwKQYrGMvBNHjG5569X3vvhx\nck4iXjyncNmzn02dvvzgFl36nHfBkI65mRGHBGBNPfPMMyGE/E69endrXs1FPnjhn9NL41mN\nN91/r651GQ0AAIB6SkkPkB6ZjTqcu1+Hi56bFkL45omLHux269E7tKn+4iUL3r/4+mXXEWy6\n4SF9Jct9bwAAIABJREFUWzWuk5QApFQss6Df0Ct23XPCE08/8+rbk4vjid+OabXJdgf2PaRv\nn+7Za36NFQCid8cdd4QQOvbtUv2S/rvH7r9r1k/ZeVvtv9ff6zIa1MqDDz6Y7ggAALDWUtID\n4dBDD013hHXUNsdfuNG/B08rjicSZY/8fVg47ZKj9+xcnQWXznp/xPAR35csuyRy/wuOqMuY\nAKRYu249T+nWc3C86Jspn339w9wlS5YsLa1o0jS/WYs2nbt226BFbroDAlC3SisSIYTykm/S\nHQRWJz8/P90RAABgraWkB8Lxxx+f7gjrqIyctpecd+TJFz9UWpFIxH96+PqzJr1z6HFH9Nu2\nY8GqFknEF0381+O33/XkgvJl9zDe7MDhh2zYJKrIAKRMLDOvU7cendb4bicApNnkyZN/O7Nk\n/jeTJ8erXjhRvmDGZ4/OXZp8kOJkAAAANBCxRML/CQHS6fvX7xg68tmKn/fGsVhso247dN96\nq25dN2/donl+ftNY2dJFixbN+f6rTz755J23/jujqKxy2VbbHvmPS4/Kcj1kAACISt++fVOy\nntzmfR4Zc3pKVgUAAEDDoqQHSL9Z7z834pq7v1lSVvXQ5Wy9/0kXnHxg4wwVPUADMLWwdLOC\nnBosOOeTl9tstVfK8wBQY6kq6XsOv2N4z7YpWRUAAAANi5IeoF6IF//w8F33PPfyO4vjVe+W\nm2zQ7fCjB/XrtWkEwQBIiUMPG3TYkL8c3WfL6i9SUTb3yTtuHPPCh08+9VTdBQNgTQ0ZMmT5\nh99//30IITu/Tdtqfxir6XobbN3r0GP2ccsTAACAdZSSHqAeKV8y49//fOHtDz7+bMrXP/18\n1/lKWXmtum233Y679tm/11YucQ/QsCRPu9xox4PPPn1gx6bZVY7/ftJzI0fdM7WwNITw9NNP\n13k+AGoquYfv2Pfa0YM6pzsLAAAADUNWugMA8Iusphvs2//4ffuHRLzo++kzFi1avGjRorJY\no4JmBQXNW7Rv3043D9CgTXv7qdNPmHTUn/9yeK/NVjUmXjzj4VtuGPfalCiDAQAAAACRcSY9\nAADUuXeeueOme55b8PNVUjbtedhZw47eMDdzhWFTJzx2/eiHpheVJR9m53U4csiww3d3aiZA\n/fXII4+EEAo677Xvdi3TnQUAAICGQUkPAABRKFnw+d2jrn/+vRnJh9lNOh5z+lmH7NQx+bBs\n8bf3jx755H+/T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9v+uM6i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\nAAAGoZMeAAAAAAAAAAAAAACD/D+uk7uBh2l96QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8_colors<-c(\"#FAA519\",\"#FCC975\",\"#286EB4\",\"#71A8DF\")\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt, aes(fill=bd),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  scale_fill_manual(values = fig8_colors)+\n",
    "  scale_y_continuous(breaks=seq(0,35,5)) +\n",
    "  ggtitle(\"Figure 8: Participation rate per day in childcare, by gender, % (2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 9: Participation rate per day in construction, by gender, % (2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00educ* dataset as in Figure 5. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^const\",sex=\"male\",isced97=\"^all\")`. This time we can use again the SDMX REST API to get the values are it is numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>isced97</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td> 2.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td> 5.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td> 4.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td> 2.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td> 1.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td> 1.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td> 6.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td> 4.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td> 1.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td> 0.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td> 2.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td> 5.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td> 5.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td> 0.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td> 0.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td> 1.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 0.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td> 4.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>13.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>13.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>13.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>14.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>12.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td> 6.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>20.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>17.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>10.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td> 5.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td> 9.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>14.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>20.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td> 8.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td>12.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>12.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 3.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Construction and repairs</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td> 9.9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & isced97 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Austria                                          & 2010 &  2.9\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Belgium                                          & 2010 &  5.0\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 &  4.8\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Estonia                                          & 2010 &  2.7\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Greece                                           & 2010 &  1.9\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Spain                                            & 2010 &  1.4\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Finland                                          & 2010 &  6.5\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & France                                           & 2010 &  4.2\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Hungary                                          & 2010 &  1.5\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Italy                                            & 2010 &  0.6\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Luxembourg                                       & 2010 &  2.2\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Netherlands                                      & 2010 &  5.2\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Norway                                           & 2010 &  5.8\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Poland                                           & 2010 &  0.7\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Romania                                          & 2010 &  0.9\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Serbia                                           & 2010 &  1.3\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & Turkey                                           & 2010 &  0.6\\\\\n",
       "\t Participation rate (\\%) & Females & Construction and repairs & All ISCED 1997 levels & United Kingdom                                   & 2010 &  4.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Austria                                          & 2010 & 13.2\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Belgium                                          & 2010 & 13.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 13.2\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Estonia                                          & 2010 & 14.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Greece                                           & 2010 & 12.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Spain                                            & 2010 &  6.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Finland                                          & 2010 & 20.9\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & France                                           & 2010 & 17.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Hungary                                          & 2010 & 10.7\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Italy                                            & 2010 &  5.1\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Luxembourg                                       & 2010 &  9.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Netherlands                                      & 2010 & 14.1\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Norway                                           & 2010 & 20.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Poland                                           & 2010 &  8.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Romania                                          & 2010 & 12.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Serbia                                           & 2010 & 12.3\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & Turkey                                           & 2010 &  3.3\\\\\n",
       "\t Participation rate (\\%) & Males   & Construction and repairs & All ISCED 1997 levels & United Kingdom                                   & 2010 &  9.9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | isced97 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Austria                                          | 2010 |  2.9 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Belgium                                          | 2010 |  5.0 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 |  4.8 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Estonia                                          | 2010 |  2.7 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Greece                                           | 2010 |  1.9 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Spain                                            | 2010 |  1.4 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Finland                                          | 2010 |  6.5 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | France                                           | 2010 |  4.2 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Hungary                                          | 2010 |  1.5 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Italy                                            | 2010 |  0.6 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Luxembourg                                       | 2010 |  2.2 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Netherlands                                      | 2010 |  5.2 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Norway                                           | 2010 |  5.8 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Poland                                           | 2010 |  0.7 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Romania                                          | 2010 |  0.9 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Serbia                                           | 2010 |  1.3 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | Turkey                                           | 2010 |  0.6 |\n",
       "| Participation rate (%) | Females | Construction and repairs | All ISCED 1997 levels | United Kingdom                                   | 2010 |  4.0 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Austria                                          | 2010 | 13.2 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Belgium                                          | 2010 | 13.8 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 13.2 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Estonia                                          | 2010 | 14.8 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Greece                                           | 2010 | 12.8 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Spain                                            | 2010 |  6.0 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Finland                                          | 2010 | 20.9 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | France                                           | 2010 | 17.0 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Hungary                                          | 2010 | 10.7 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Italy                                            | 2010 |  5.1 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Luxembourg                                       | 2010 |  9.4 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Netherlands                                      | 2010 | 14.1 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Norway                                           | 2010 | 20.8 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Poland                                           | 2010 |  8.5 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Romania                                          | 2010 | 12.5 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Serbia                                           | 2010 | 12.3 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | Turkey                                           | 2010 |  3.3 |\n",
       "| Participation rate (%) | Males   | Construction and repairs | All ISCED 1997 levels | United Kingdom                                   | 2010 |  9.9 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                   sex     acl00                   \n",
       "1  Participation rate (%) Females Construction and repairs\n",
       "2  Participation rate (%) Females Construction and repairs\n",
       "3  Participation rate (%) Females Construction and repairs\n",
       "4  Participation rate (%) Females Construction and repairs\n",
       "5  Participation rate (%) Females Construction and repairs\n",
       "6  Participation rate (%) Females Construction and repairs\n",
       "7  Participation rate (%) Females Construction and repairs\n",
       "8  Participation rate (%) Females Construction and repairs\n",
       "9  Participation rate (%) Females Construction and repairs\n",
       "10 Participation rate (%) Females Construction and repairs\n",
       "11 Participation rate (%) Females Construction and repairs\n",
       "12 Participation rate (%) Females Construction and repairs\n",
       "13 Participation rate (%) Females Construction and repairs\n",
       "14 Participation rate (%) Females Construction and repairs\n",
       "15 Participation rate (%) Females Construction and repairs\n",
       "16 Participation rate (%) Females Construction and repairs\n",
       "17 Participation rate (%) Females Construction and repairs\n",
       "18 Participation rate (%) Females Construction and repairs\n",
       "19 Participation rate (%) Males   Construction and repairs\n",
       "20 Participation rate (%) Males   Construction and repairs\n",
       "21 Participation rate (%) Males   Construction and repairs\n",
       "22 Participation rate (%) Males   Construction and repairs\n",
       "23 Participation rate (%) Males   Construction and repairs\n",
       "24 Participation rate (%) Males   Construction and repairs\n",
       "25 Participation rate (%) Males   Construction and repairs\n",
       "26 Participation rate (%) Males   Construction and repairs\n",
       "27 Participation rate (%) Males   Construction and repairs\n",
       "28 Participation rate (%) Males   Construction and repairs\n",
       "29 Participation rate (%) Males   Construction and repairs\n",
       "30 Participation rate (%) Males   Construction and repairs\n",
       "31 Participation rate (%) Males   Construction and repairs\n",
       "32 Participation rate (%) Males   Construction and repairs\n",
       "33 Participation rate (%) Males   Construction and repairs\n",
       "34 Participation rate (%) Males   Construction and repairs\n",
       "35 Participation rate (%) Males   Construction and repairs\n",
       "36 Participation rate (%) Males   Construction and repairs\n",
       "   isced97               geo                                              time\n",
       "1  All ISCED 1997 levels Austria                                          2010\n",
       "2  All ISCED 1997 levels Belgium                                          2010\n",
       "3  All ISCED 1997 levels Germany (until 1990 former territory of the FRG) 2010\n",
       "4  All ISCED 1997 levels Estonia                                          2010\n",
       "5  All ISCED 1997 levels Greece                                           2010\n",
       "6  All ISCED 1997 levels Spain                                            2010\n",
       "7  All ISCED 1997 levels Finland                                          2010\n",
       "8  All ISCED 1997 levels France                                           2010\n",
       "9  All ISCED 1997 levels Hungary                                          2010\n",
       "10 All ISCED 1997 levels Italy                                            2010\n",
       "11 All ISCED 1997 levels Luxembourg                                       2010\n",
       "12 All ISCED 1997 levels Netherlands                                      2010\n",
       "13 All ISCED 1997 levels Norway                                           2010\n",
       "14 All ISCED 1997 levels Poland                                           2010\n",
       "15 All ISCED 1997 levels Romania                                          2010\n",
       "16 All ISCED 1997 levels Serbia                                           2010\n",
       "17 All ISCED 1997 levels Turkey                                           2010\n",
       "18 All ISCED 1997 levels United Kingdom                                   2010\n",
       "19 All ISCED 1997 levels Austria                                          2010\n",
       "20 All ISCED 1997 levels Belgium                                          2010\n",
       "21 All ISCED 1997 levels Germany (until 1990 former territory of the FRG) 2010\n",
       "22 All ISCED 1997 levels Estonia                                          2010\n",
       "23 All ISCED 1997 levels Greece                                           2010\n",
       "24 All ISCED 1997 levels Spain                                            2010\n",
       "25 All ISCED 1997 levels Finland                                          2010\n",
       "26 All ISCED 1997 levels France                                           2010\n",
       "27 All ISCED 1997 levels Hungary                                          2010\n",
       "28 All ISCED 1997 levels Italy                                            2010\n",
       "29 All ISCED 1997 levels Luxembourg                                       2010\n",
       "30 All ISCED 1997 levels Netherlands                                      2010\n",
       "31 All ISCED 1997 levels Norway                                           2010\n",
       "32 All ISCED 1997 levels Poland                                           2010\n",
       "33 All ISCED 1997 levels Romania                                          2010\n",
       "34 All ISCED 1997 levels Serbia                                           2010\n",
       "35 All ISCED 1997 levels Turkey                                           2010\n",
       "36 All ISCED 1997 levels United Kingdom                                   2010\n",
       "   values\n",
       "1   2.9  \n",
       "2   5.0  \n",
       "3   4.8  \n",
       "4   2.7  \n",
       "5   1.9  \n",
       "6   1.4  \n",
       "7   6.5  \n",
       "8   4.2  \n",
       "9   1.5  \n",
       "10  0.6  \n",
       "11  2.2  \n",
       "12  5.2  \n",
       "13  5.8  \n",
       "14  0.7  \n",
       "15  0.9  \n",
       "16  1.3  \n",
       "17  0.6  \n",
       "18  4.0  \n",
       "19 13.2  \n",
       "20 13.8  \n",
       "21 13.2  \n",
       "22 14.8  \n",
       "23 12.8  \n",
       "24  6.0  \n",
       "25 20.9  \n",
       "26 17.0  \n",
       "27 10.7  \n",
       "28  5.1  \n",
       "29  9.4  \n",
       "30 14.1  \n",
       "31 20.8  \n",
       "32  8.5  \n",
       "33 12.5  \n",
       "34 12.3  \n",
       "35  3.3  \n",
       "36  9.9  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ\",filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^const\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we keep only the columns with sex, activities, countries and values. Before plotting the values we need to merge the columns sex and activities and cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>values</th><th scope=col>bd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td> 2.9</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Belgium       </td><td> 5.0</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Germany       </td><td> 4.8</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Estonia       </td><td> 2.7</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Greece        </td><td> 1.9</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Spain         </td><td> 1.4</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Finland       </td><td> 6.5</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>France        </td><td> 4.2</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Hungary       </td><td> 1.5</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Italy         </td><td> 0.6</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td> 2.2</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td> 5.2</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Norway        </td><td> 5.8</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Poland        </td><td> 0.7</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Romania       </td><td> 0.9</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Serbia        </td><td> 1.3</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 0.6</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td> 4.0</td><td>Construction and repairs, females</td></tr>\n",
       "\t<tr><td>Austria       </td><td>13.2</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Belgium       </td><td>13.8</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Germany       </td><td>13.2</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Estonia       </td><td>14.8</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Greece        </td><td>12.8</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Spain         </td><td> 6.0</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Finland       </td><td>20.9</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>France        </td><td>17.0</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Hungary       </td><td>10.7</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Italy         </td><td> 5.1</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td> 9.4</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>14.1</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Norway        </td><td>20.8</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Poland        </td><td> 8.5</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Romania       </td><td>12.5</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Serbia        </td><td>12.3</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 3.3</td><td>Construction and repairs, males  </td></tr>\n",
       "\t<tr><td>United Kingdom</td><td> 9.9</td><td>Construction and repairs, males  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & values & bd\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Austria        &  2.9 & Construction and repairs, females\\\\\n",
       "\t Belgium        &  5.0 & Construction and repairs, females\\\\\n",
       "\t Germany        &  4.8 & Construction and repairs, females\\\\\n",
       "\t Estonia        &  2.7 & Construction and repairs, females\\\\\n",
       "\t Greece         &  1.9 & Construction and repairs, females\\\\\n",
       "\t Spain          &  1.4 & Construction and repairs, females\\\\\n",
       "\t Finland        &  6.5 & Construction and repairs, females\\\\\n",
       "\t France         &  4.2 & Construction and repairs, females\\\\\n",
       "\t Hungary        &  1.5 & Construction and repairs, females\\\\\n",
       "\t Italy          &  0.6 & Construction and repairs, females\\\\\n",
       "\t Luxembourg     &  2.2 & Construction and repairs, females\\\\\n",
       "\t Netherlands    &  5.2 & Construction and repairs, females\\\\\n",
       "\t Norway         &  5.8 & Construction and repairs, females\\\\\n",
       "\t Poland         &  0.7 & Construction and repairs, females\\\\\n",
       "\t Romania        &  0.9 & Construction and repairs, females\\\\\n",
       "\t Serbia         &  1.3 & Construction and repairs, females\\\\\n",
       "\t Turkey         &  0.6 & Construction and repairs, females\\\\\n",
       "\t United Kingdom &  4.0 & Construction and repairs, females\\\\\n",
       "\t Austria        & 13.2 & Construction and repairs, males  \\\\\n",
       "\t Belgium        & 13.8 & Construction and repairs, males  \\\\\n",
       "\t Germany        & 13.2 & Construction and repairs, males  \\\\\n",
       "\t Estonia        & 14.8 & Construction and repairs, males  \\\\\n",
       "\t Greece         & 12.8 & Construction and repairs, males  \\\\\n",
       "\t Spain          &  6.0 & Construction and repairs, males  \\\\\n",
       "\t Finland        & 20.9 & Construction and repairs, males  \\\\\n",
       "\t France         & 17.0 & Construction and repairs, males  \\\\\n",
       "\t Hungary        & 10.7 & Construction and repairs, males  \\\\\n",
       "\t Italy          &  5.1 & Construction and repairs, males  \\\\\n",
       "\t Luxembourg     &  9.4 & Construction and repairs, males  \\\\\n",
       "\t Netherlands    & 14.1 & Construction and repairs, males  \\\\\n",
       "\t Norway         & 20.8 & Construction and repairs, males  \\\\\n",
       "\t Poland         &  8.5 & Construction and repairs, males  \\\\\n",
       "\t Romania        & 12.5 & Construction and repairs, males  \\\\\n",
       "\t Serbia         & 12.3 & Construction and repairs, males  \\\\\n",
       "\t Turkey         &  3.3 & Construction and repairs, males  \\\\\n",
       "\t United Kingdom &  9.9 & Construction and repairs, males  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | values &lt;dbl&gt; | bd &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Austria        |  2.9 | Construction and repairs, females |\n",
       "| Belgium        |  5.0 | Construction and repairs, females |\n",
       "| Germany        |  4.8 | Construction and repairs, females |\n",
       "| Estonia        |  2.7 | Construction and repairs, females |\n",
       "| Greece         |  1.9 | Construction and repairs, females |\n",
       "| Spain          |  1.4 | Construction and repairs, females |\n",
       "| Finland        |  6.5 | Construction and repairs, females |\n",
       "| France         |  4.2 | Construction and repairs, females |\n",
       "| Hungary        |  1.5 | Construction and repairs, females |\n",
       "| Italy          |  0.6 | Construction and repairs, females |\n",
       "| Luxembourg     |  2.2 | Construction and repairs, females |\n",
       "| Netherlands    |  5.2 | Construction and repairs, females |\n",
       "| Norway         |  5.8 | Construction and repairs, females |\n",
       "| Poland         |  0.7 | Construction and repairs, females |\n",
       "| Romania        |  0.9 | Construction and repairs, females |\n",
       "| Serbia         |  1.3 | Construction and repairs, females |\n",
       "| Turkey         |  0.6 | Construction and repairs, females |\n",
       "| United Kingdom |  4.0 | Construction and repairs, females |\n",
       "| Austria        | 13.2 | Construction and repairs, males   |\n",
       "| Belgium        | 13.8 | Construction and repairs, males   |\n",
       "| Germany        | 13.2 | Construction and repairs, males   |\n",
       "| Estonia        | 14.8 | Construction and repairs, males   |\n",
       "| Greece         | 12.8 | Construction and repairs, males   |\n",
       "| Spain          |  6.0 | Construction and repairs, males   |\n",
       "| Finland        | 20.9 | Construction and repairs, males   |\n",
       "| France         | 17.0 | Construction and repairs, males   |\n",
       "| Hungary        | 10.7 | Construction and repairs, males   |\n",
       "| Italy          |  5.1 | Construction and repairs, males   |\n",
       "| Luxembourg     |  9.4 | Construction and repairs, males   |\n",
       "| Netherlands    | 14.1 | Construction and repairs, males   |\n",
       "| Norway         | 20.8 | Construction and repairs, males   |\n",
       "| Poland         |  8.5 | Construction and repairs, males   |\n",
       "| Romania        | 12.5 | Construction and repairs, males   |\n",
       "| Serbia         | 12.3 | Construction and repairs, males   |\n",
       "| Turkey         |  3.3 | Construction and repairs, males   |\n",
       "| United Kingdom |  9.9 | Construction and repairs, males   |\n",
       "\n"
      ],
      "text/plain": [
       "   geo            values bd                               \n",
       "1  Austria         2.9   Construction and repairs, females\n",
       "2  Belgium         5.0   Construction and repairs, females\n",
       "3  Germany         4.8   Construction and repairs, females\n",
       "4  Estonia         2.7   Construction and repairs, females\n",
       "5  Greece          1.9   Construction and repairs, females\n",
       "6  Spain           1.4   Construction and repairs, females\n",
       "7  Finland         6.5   Construction and repairs, females\n",
       "8  France          4.2   Construction and repairs, females\n",
       "9  Hungary         1.5   Construction and repairs, females\n",
       "10 Italy           0.6   Construction and repairs, females\n",
       "11 Luxembourg      2.2   Construction and repairs, females\n",
       "12 Netherlands     5.2   Construction and repairs, females\n",
       "13 Norway          5.8   Construction and repairs, females\n",
       "14 Poland          0.7   Construction and repairs, females\n",
       "15 Romania         0.9   Construction and repairs, females\n",
       "16 Serbia          1.3   Construction and repairs, females\n",
       "17 Turkey          0.6   Construction and repairs, females\n",
       "18 United Kingdom  4.0   Construction and repairs, females\n",
       "19 Austria        13.2   Construction and repairs, males  \n",
       "20 Belgium        13.8   Construction and repairs, males  \n",
       "21 Germany        13.2   Construction and repairs, males  \n",
       "22 Estonia        14.8   Construction and repairs, males  \n",
       "23 Greece         12.8   Construction and repairs, males  \n",
       "24 Spain           6.0   Construction and repairs, males  \n",
       "25 Finland        20.9   Construction and repairs, males  \n",
       "26 France         17.0   Construction and repairs, males  \n",
       "27 Hungary        10.7   Construction and repairs, males  \n",
       "28 Italy           5.1   Construction and repairs, males  \n",
       "29 Luxembourg      9.4   Construction and repairs, males  \n",
       "30 Netherlands    14.1   Construction and repairs, males  \n",
       "31 Norway         20.8   Construction and repairs, males  \n",
       "32 Poland          8.5   Construction and repairs, males  \n",
       "33 Romania        12.5   Construction and repairs, males  \n",
       "34 Serbia         12.3   Construction and repairs, males  \n",
       "35 Turkey          3.3   Construction and repairs, males  \n",
       "36 United Kingdom  9.9   Construction and repairs, males  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "dt<-dt[,c(\"sex\",\"acl00\",\"geo\",\"values\")]\n",
    "dt[,bd:=paste0(acl00,\", \",tolower(sex))][,c(\"acl00\",\"sex\"):=NULL]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Construction and repairs, females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(bd=c(\"Construction and repairs, males\",\"Construction and repairs, males\"),geo=c(\" \",\"  \"),values=c(NA,NA))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Construction and repairs, females\",bd)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Turkey','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "bd_ord<-sort(unique(dt$bd),decreasing=TRUE)\n",
    "dt$bd<-factor(dt$bd,levels=bd_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
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D/h2LLRdgoT+TacfV/VsXN34TAOLsH9eY1Jh+ZSr29oWJZiyLKi2OVj2pE+/2nn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BQW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\nQ6/Zk4d1cLDNPrVg0bmXAHDp0iWgyHz+q7thZ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96wR2lldxhMbj/vH1QVYJNXVFR5QXvuV9M3lGhtOHRD+Py2/6x/O3r2+qqTnYW5ubm5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\nN/90oSxDX+Zt6q2lOzoeXNFLRfE1fUFBQQ342xx822OSvs4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6O6giwQ7FPQAAIABJREFUPjXlOK0zrLgNAAmHV5wxXTXQ3a7k9aOftoUr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WNo5fC+CwZGQoKUnR6TMXy3X+hjg2NlYp8ak7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\noTs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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig9_colors<-c(\"#FAA519\",\"#286EB4\") #\"#FCC975\",\"#71A8DF\",\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt, aes(fill=bd),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  scale_fill_manual(values = fig9_colors)+\n",
    "  scale_y_continuous(limits=c(0,25),breaks=seq(0,25,5)) +\n",
    "  ggtitle(\"Figure 9: Participation rate per day in construction, by gender, % (2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 10: Participation rate per day in gardening and pet care, by gender, % (2008 to 2015)\n",
    "\n",
    "The data is again in the *tus_00educ* dataset as in Figure 5. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^garden\",sex=\"male\",isced97=\"^all\")`. This time we can use again the SDMX REST API to get the values are it is numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>isced97</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>23.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td> 7.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>16.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td>10.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td> 9.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td> 3.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td>10.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td> 8.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>11.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td> 5.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td> 6.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td> 8.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>14.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td>10.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td> 8.2</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>10.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 5.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Females</td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td> 8.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Austria                                         </td><td>2010</td><td>14.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Belgium                                         </td><td>2010</td><td>13.6</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>13.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Estonia                                         </td><td>2010</td><td> 7.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Greece                                          </td><td>2010</td><td>13.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Spain                                           </td><td>2010</td><td> 6.5</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Finland                                         </td><td>2010</td><td> 7.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>France                                          </td><td>2010</td><td>12.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Hungary                                         </td><td>2010</td><td>15.7</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Italy                                           </td><td>2010</td><td>10.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Luxembourg                                      </td><td>2010</td><td> 8.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Netherlands                                     </td><td>2010</td><td>10.0</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Norway                                          </td><td>2010</td><td>12.1</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Poland                                          </td><td>2010</td><td> 9.3</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Romania                                         </td><td>2010</td><td> 9.9</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Serbia                                          </td><td>2010</td><td>10.8</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>Turkey                                          </td><td>2010</td><td> 4.4</td></tr>\n",
       "\t<tr><td>Participation rate (%)</td><td>Males  </td><td>Gardening; other pet care</td><td>All ISCED 1997 levels</td><td>United Kingdom                                  </td><td>2010</td><td> 9.2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & acl00 & isced97 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Austria                                          & 2010 & 23.7\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Belgium                                          & 2010 &  7.0\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 16.2\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Estonia                                          & 2010 & 10.9\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Greece                                           & 2010 &  9.2\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Spain                                            & 2010 &  3.2\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Finland                                          & 2010 & 10.4\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & France                                           & 2010 &  8.8\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Hungary                                          & 2010 & 11.6\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Italy                                            & 2010 &  5.2\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Luxembourg                                       & 2010 &  6.8\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Netherlands                                      & 2010 &  8.8\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Norway                                           & 2010 & 14.9\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Poland                                           & 2010 & 10.3\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Romania                                          & 2010 &  8.2\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Serbia                                           & 2010 & 10.7\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & Turkey                                           & 2010 &  5.3\\\\\n",
       "\t Participation rate (\\%) & Females & Gardening; other pet care & All ISCED 1997 levels & United Kingdom                                   & 2010 &  8.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Austria                                          & 2010 & 14.1\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Belgium                                          & 2010 & 13.6\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Germany (until 1990 former territory of the FRG) & 2010 & 13.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Estonia                                          & 2010 &  7.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Greece                                           & 2010 & 13.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Spain                                            & 2010 &  6.5\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Finland                                          & 2010 &  7.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & France                                           & 2010 & 12.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Hungary                                          & 2010 & 15.7\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Italy                                            & 2010 & 10.3\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Luxembourg                                       & 2010 &  8.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Netherlands                                      & 2010 & 10.0\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Norway                                           & 2010 & 12.1\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Poland                                           & 2010 &  9.3\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Romania                                          & 2010 &  9.9\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Serbia                                           & 2010 & 10.8\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & Turkey                                           & 2010 &  4.4\\\\\n",
       "\t Participation rate (\\%) & Males   & Gardening; other pet care & All ISCED 1997 levels & United Kingdom                                   & 2010 &  9.2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | isced97 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Austria                                          | 2010 | 23.7 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Belgium                                          | 2010 |  7.0 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 16.2 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Estonia                                          | 2010 | 10.9 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Greece                                           | 2010 |  9.2 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Spain                                            | 2010 |  3.2 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Finland                                          | 2010 | 10.4 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | France                                           | 2010 |  8.8 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Hungary                                          | 2010 | 11.6 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Italy                                            | 2010 |  5.2 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Luxembourg                                       | 2010 |  6.8 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Netherlands                                      | 2010 |  8.8 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Norway                                           | 2010 | 14.9 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Poland                                           | 2010 | 10.3 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Romania                                          | 2010 |  8.2 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Serbia                                           | 2010 | 10.7 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | Turkey                                           | 2010 |  5.3 |\n",
       "| Participation rate (%) | Females | Gardening; other pet care | All ISCED 1997 levels | United Kingdom                                   | 2010 |  8.8 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Austria                                          | 2010 | 14.1 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Belgium                                          | 2010 | 13.6 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Germany (until 1990 former territory of the FRG) | 2010 | 13.5 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Estonia                                          | 2010 |  7.0 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Greece                                           | 2010 | 13.8 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Spain                                            | 2010 |  6.5 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Finland                                          | 2010 |  7.0 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | France                                           | 2010 | 12.4 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Hungary                                          | 2010 | 15.7 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Italy                                            | 2010 | 10.3 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Luxembourg                                       | 2010 |  8.4 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Netherlands                                      | 2010 | 10.0 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Norway                                           | 2010 | 12.1 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Poland                                           | 2010 |  9.3 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Romania                                          | 2010 |  9.9 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Serbia                                           | 2010 | 10.8 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | Turkey                                           | 2010 |  4.4 |\n",
       "| Participation rate (%) | Males   | Gardening; other pet care | All ISCED 1997 levels | United Kingdom                                   | 2010 |  9.2 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                   sex     acl00                    \n",
       "1  Participation rate (%) Females Gardening; other pet care\n",
       "2  Participation rate (%) Females Gardening; other pet care\n",
       "3  Participation rate (%) Females Gardening; other pet care\n",
       "4  Participation rate (%) Females Gardening; other pet care\n",
       "5  Participation rate (%) Females Gardening; other pet care\n",
       "6  Participation rate (%) Females Gardening; other pet care\n",
       "7  Participation rate (%) Females Gardening; other pet care\n",
       "8  Participation rate (%) Females Gardening; other pet care\n",
       "9  Participation rate (%) Females Gardening; other pet care\n",
       "10 Participation rate (%) Females Gardening; other pet care\n",
       "11 Participation rate (%) Females Gardening; other pet care\n",
       "12 Participation rate (%) Females Gardening; other pet care\n",
       "13 Participation rate (%) Females Gardening; other pet care\n",
       "14 Participation rate (%) Females Gardening; other pet care\n",
       "15 Participation rate (%) Females Gardening; other pet care\n",
       "16 Participation rate (%) Females Gardening; other pet care\n",
       "17 Participation rate (%) Females Gardening; other pet care\n",
       "18 Participation rate (%) Females Gardening; other pet care\n",
       "19 Participation rate (%) Males   Gardening; other pet care\n",
       "20 Participation rate (%) Males   Gardening; other pet care\n",
       "21 Participation rate (%) Males   Gardening; other pet care\n",
       "22 Participation rate (%) Males   Gardening; other pet care\n",
       "23 Participation rate (%) Males   Gardening; other pet care\n",
       "24 Participation rate (%) Males   Gardening; other pet care\n",
       "25 Participation rate (%) Males   Gardening; other pet care\n",
       "26 Participation rate (%) Males   Gardening; other pet care\n",
       "27 Participation rate (%) Males   Gardening; other pet care\n",
       "28 Participation rate (%) Males   Gardening; other pet care\n",
       "29 Participation rate (%) Males   Gardening; other pet care\n",
       "30 Participation rate (%) Males   Gardening; other pet care\n",
       "31 Participation rate (%) Males   Gardening; other pet care\n",
       "32 Participation rate (%) Males   Gardening; other pet care\n",
       "33 Participation rate (%) Males   Gardening; other pet care\n",
       "34 Participation rate (%) Males   Gardening; other pet care\n",
       "35 Participation rate (%) Males   Gardening; other pet care\n",
       "36 Participation rate (%) Males   Gardening; other pet care\n",
       "   isced97               geo                                              time\n",
       "1  All ISCED 1997 levels Austria                                          2010\n",
       "2  All ISCED 1997 levels Belgium                                          2010\n",
       "3  All ISCED 1997 levels Germany (until 1990 former territory of the FRG) 2010\n",
       "4  All ISCED 1997 levels Estonia                                          2010\n",
       "5  All ISCED 1997 levels Greece                                           2010\n",
       "6  All ISCED 1997 levels Spain                                            2010\n",
       "7  All ISCED 1997 levels Finland                                          2010\n",
       "8  All ISCED 1997 levels France                                           2010\n",
       "9  All ISCED 1997 levels Hungary                                          2010\n",
       "10 All ISCED 1997 levels Italy                                            2010\n",
       "11 All ISCED 1997 levels Luxembourg                                       2010\n",
       "12 All ISCED 1997 levels Netherlands                                      2010\n",
       "13 All ISCED 1997 levels Norway                                           2010\n",
       "14 All ISCED 1997 levels Poland                                           2010\n",
       "15 All ISCED 1997 levels Romania                                          2010\n",
       "16 All ISCED 1997 levels Serbia                                           2010\n",
       "17 All ISCED 1997 levels Turkey                                           2010\n",
       "18 All ISCED 1997 levels United Kingdom                                   2010\n",
       "19 All ISCED 1997 levels Austria                                          2010\n",
       "20 All ISCED 1997 levels Belgium                                          2010\n",
       "21 All ISCED 1997 levels Germany (until 1990 former territory of the FRG) 2010\n",
       "22 All ISCED 1997 levels Estonia                                          2010\n",
       "23 All ISCED 1997 levels Greece                                           2010\n",
       "24 All ISCED 1997 levels Spain                                            2010\n",
       "25 All ISCED 1997 levels Finland                                          2010\n",
       "26 All ISCED 1997 levels France                                           2010\n",
       "27 All ISCED 1997 levels Hungary                                          2010\n",
       "28 All ISCED 1997 levels Italy                                            2010\n",
       "29 All ISCED 1997 levels Luxembourg                                       2010\n",
       "30 All ISCED 1997 levels Netherlands                                      2010\n",
       "31 All ISCED 1997 levels Norway                                           2010\n",
       "32 All ISCED 1997 levels Poland                                           2010\n",
       "33 All ISCED 1997 levels Romania                                          2010\n",
       "34 All ISCED 1997 levels Serbia                                           2010\n",
       "35 All ISCED 1997 levels Turkey                                           2010\n",
       "36 All ISCED 1997 levels United Kingdom                                   2010\n",
       "   values\n",
       "1  23.7  \n",
       "2   7.0  \n",
       "3  16.2  \n",
       "4  10.9  \n",
       "5   9.2  \n",
       "6   3.2  \n",
       "7  10.4  \n",
       "8   8.8  \n",
       "9  11.6  \n",
       "10  5.2  \n",
       "11  6.8  \n",
       "12  8.8  \n",
       "13 14.9  \n",
       "14 10.3  \n",
       "15  8.2  \n",
       "16 10.7  \n",
       "17  5.3  \n",
       "18  8.8  \n",
       "19 14.1  \n",
       "20 13.6  \n",
       "21 13.5  \n",
       "22  7.0  \n",
       "23 13.8  \n",
       "24  6.5  \n",
       "25  7.0  \n",
       "26 12.4  \n",
       "27 15.7  \n",
       "28 10.3  \n",
       "29  8.4  \n",
       "30 10.0  \n",
       "31 12.1  \n",
       "32  9.3  \n",
       "33  9.9  \n",
       "34 10.8  \n",
       "35  4.4  \n",
       "36  9.2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ\",filters=list(unit=\"Participation rate\",age=\"total\",acl00=\"^garden\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we keep only the columns with sex, activities, countries and values. Before plotting the values we need to merge the columns sex and activities and cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>values</th><th scope=col>bd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td>23.7</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Belgium       </td><td> 7.0</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Germany       </td><td>16.2</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>10.9</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Greece        </td><td> 9.2</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Spain         </td><td> 3.2</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Finland       </td><td>10.4</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>France        </td><td> 8.8</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>11.6</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Italy         </td><td> 5.2</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td> 6.8</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td> 8.8</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Norway        </td><td>14.9</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Poland        </td><td>10.3</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Romania       </td><td> 8.2</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>10.7</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 5.3</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td> 8.8</td><td>Gardening; other pet care, females</td></tr>\n",
       "\t<tr><td>Austria       </td><td>14.1</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Belgium       </td><td>13.6</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Germany       </td><td>13.5</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Estonia       </td><td> 7.0</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Greece        </td><td>13.8</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Spain         </td><td> 6.5</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Finland       </td><td> 7.0</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>France        </td><td>12.4</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Hungary       </td><td>15.7</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Italy         </td><td>10.3</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td> 8.4</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>10.0</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Norway        </td><td>12.1</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Poland        </td><td> 9.3</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Romania       </td><td> 9.9</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Serbia        </td><td>10.8</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>Turkey        </td><td> 4.4</td><td>Gardening; other pet care, males  </td></tr>\n",
       "\t<tr><td>United Kingdom</td><td> 9.2</td><td>Gardening; other pet care, males  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & values & bd\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Austria        & 23.7 & Gardening; other pet care, females\\\\\n",
       "\t Belgium        &  7.0 & Gardening; other pet care, females\\\\\n",
       "\t Germany        & 16.2 & Gardening; other pet care, females\\\\\n",
       "\t Estonia        & 10.9 & Gardening; other pet care, females\\\\\n",
       "\t Greece         &  9.2 & Gardening; other pet care, females\\\\\n",
       "\t Spain          &  3.2 & Gardening; other pet care, females\\\\\n",
       "\t Finland        & 10.4 & Gardening; other pet care, females\\\\\n",
       "\t France         &  8.8 & Gardening; other pet care, females\\\\\n",
       "\t Hungary        & 11.6 & Gardening; other pet care, females\\\\\n",
       "\t Italy          &  5.2 & Gardening; other pet care, females\\\\\n",
       "\t Luxembourg     &  6.8 & Gardening; other pet care, females\\\\\n",
       "\t Netherlands    &  8.8 & Gardening; other pet care, females\\\\\n",
       "\t Norway         & 14.9 & Gardening; other pet care, females\\\\\n",
       "\t Poland         & 10.3 & Gardening; other pet care, females\\\\\n",
       "\t Romania        &  8.2 & Gardening; other pet care, females\\\\\n",
       "\t Serbia         & 10.7 & Gardening; other pet care, females\\\\\n",
       "\t Turkey         &  5.3 & Gardening; other pet care, females\\\\\n",
       "\t United Kingdom &  8.8 & Gardening; other pet care, females\\\\\n",
       "\t Austria        & 14.1 & Gardening; other pet care, males  \\\\\n",
       "\t Belgium        & 13.6 & Gardening; other pet care, males  \\\\\n",
       "\t Germany        & 13.5 & Gardening; other pet care, males  \\\\\n",
       "\t Estonia        &  7.0 & Gardening; other pet care, males  \\\\\n",
       "\t Greece         & 13.8 & Gardening; other pet care, males  \\\\\n",
       "\t Spain          &  6.5 & Gardening; other pet care, males  \\\\\n",
       "\t Finland        &  7.0 & Gardening; other pet care, males  \\\\\n",
       "\t France         & 12.4 & Gardening; other pet care, males  \\\\\n",
       "\t Hungary        & 15.7 & Gardening; other pet care, males  \\\\\n",
       "\t Italy          & 10.3 & Gardening; other pet care, males  \\\\\n",
       "\t Luxembourg     &  8.4 & Gardening; other pet care, males  \\\\\n",
       "\t Netherlands    & 10.0 & Gardening; other pet care, males  \\\\\n",
       "\t Norway         & 12.1 & Gardening; other pet care, males  \\\\\n",
       "\t Poland         &  9.3 & Gardening; other pet care, males  \\\\\n",
       "\t Romania        &  9.9 & Gardening; other pet care, males  \\\\\n",
       "\t Serbia         & 10.8 & Gardening; other pet care, males  \\\\\n",
       "\t Turkey         &  4.4 & Gardening; other pet care, males  \\\\\n",
       "\t United Kingdom &  9.2 & Gardening; other pet care, males  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | values &lt;dbl&gt; | bd &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| Austria        | 23.7 | Gardening; other pet care, females |\n",
       "| Belgium        |  7.0 | Gardening; other pet care, females |\n",
       "| Germany        | 16.2 | Gardening; other pet care, females |\n",
       "| Estonia        | 10.9 | Gardening; other pet care, females |\n",
       "| Greece         |  9.2 | Gardening; other pet care, females |\n",
       "| Spain          |  3.2 | Gardening; other pet care, females |\n",
       "| Finland        | 10.4 | Gardening; other pet care, females |\n",
       "| France         |  8.8 | Gardening; other pet care, females |\n",
       "| Hungary        | 11.6 | Gardening; other pet care, females |\n",
       "| Italy          |  5.2 | Gardening; other pet care, females |\n",
       "| Luxembourg     |  6.8 | Gardening; other pet care, females |\n",
       "| Netherlands    |  8.8 | Gardening; other pet care, females |\n",
       "| Norway         | 14.9 | Gardening; other pet care, females |\n",
       "| Poland         | 10.3 | Gardening; other pet care, females |\n",
       "| Romania        |  8.2 | Gardening; other pet care, females |\n",
       "| Serbia         | 10.7 | Gardening; other pet care, females |\n",
       "| Turkey         |  5.3 | Gardening; other pet care, females |\n",
       "| United Kingdom |  8.8 | Gardening; other pet care, females |\n",
       "| Austria        | 14.1 | Gardening; other pet care, males   |\n",
       "| Belgium        | 13.6 | Gardening; other pet care, males   |\n",
       "| Germany        | 13.5 | Gardening; other pet care, males   |\n",
       "| Estonia        |  7.0 | Gardening; other pet care, males   |\n",
       "| Greece         | 13.8 | Gardening; other pet care, males   |\n",
       "| Spain          |  6.5 | Gardening; other pet care, males   |\n",
       "| Finland        |  7.0 | Gardening; other pet care, males   |\n",
       "| France         | 12.4 | Gardening; other pet care, males   |\n",
       "| Hungary        | 15.7 | Gardening; other pet care, males   |\n",
       "| Italy          | 10.3 | Gardening; other pet care, males   |\n",
       "| Luxembourg     |  8.4 | Gardening; other pet care, males   |\n",
       "| Netherlands    | 10.0 | Gardening; other pet care, males   |\n",
       "| Norway         | 12.1 | Gardening; other pet care, males   |\n",
       "| Poland         |  9.3 | Gardening; other pet care, males   |\n",
       "| Romania        |  9.9 | Gardening; other pet care, males   |\n",
       "| Serbia         | 10.8 | Gardening; other pet care, males   |\n",
       "| Turkey         |  4.4 | Gardening; other pet care, males   |\n",
       "| United Kingdom |  9.2 | Gardening; other pet care, males   |\n",
       "\n"
      ],
      "text/plain": [
       "   geo            values bd                                \n",
       "1  Austria        23.7   Gardening; other pet care, females\n",
       "2  Belgium         7.0   Gardening; other pet care, females\n",
       "3  Germany        16.2   Gardening; other pet care, females\n",
       "4  Estonia        10.9   Gardening; other pet care, females\n",
       "5  Greece          9.2   Gardening; other pet care, females\n",
       "6  Spain           3.2   Gardening; other pet care, females\n",
       "7  Finland        10.4   Gardening; other pet care, females\n",
       "8  France          8.8   Gardening; other pet care, females\n",
       "9  Hungary        11.6   Gardening; other pet care, females\n",
       "10 Italy           5.2   Gardening; other pet care, females\n",
       "11 Luxembourg      6.8   Gardening; other pet care, females\n",
       "12 Netherlands     8.8   Gardening; other pet care, females\n",
       "13 Norway         14.9   Gardening; other pet care, females\n",
       "14 Poland         10.3   Gardening; other pet care, females\n",
       "15 Romania         8.2   Gardening; other pet care, females\n",
       "16 Serbia         10.7   Gardening; other pet care, females\n",
       "17 Turkey          5.3   Gardening; other pet care, females\n",
       "18 United Kingdom  8.8   Gardening; other pet care, females\n",
       "19 Austria        14.1   Gardening; other pet care, males  \n",
       "20 Belgium        13.6   Gardening; other pet care, males  \n",
       "21 Germany        13.5   Gardening; other pet care, males  \n",
       "22 Estonia         7.0   Gardening; other pet care, males  \n",
       "23 Greece         13.8   Gardening; other pet care, males  \n",
       "24 Spain           6.5   Gardening; other pet care, males  \n",
       "25 Finland         7.0   Gardening; other pet care, males  \n",
       "26 France         12.4   Gardening; other pet care, males  \n",
       "27 Hungary        15.7   Gardening; other pet care, males  \n",
       "28 Italy          10.3   Gardening; other pet care, males  \n",
       "29 Luxembourg      8.4   Gardening; other pet care, males  \n",
       "30 Netherlands    10.0   Gardening; other pet care, males  \n",
       "31 Norway         12.1   Gardening; other pet care, males  \n",
       "32 Poland          9.3   Gardening; other pet care, males  \n",
       "33 Romania         9.9   Gardening; other pet care, males  \n",
       "34 Serbia         10.8   Gardening; other pet care, males  \n",
       "35 Turkey          4.4   Gardening; other pet care, males  \n",
       "36 United Kingdom  9.2   Gardening; other pet care, males  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "dt<-dt[,c(\"sex\",\"acl00\",\"geo\",\"values\")]\n",
    "dt[,bd:=paste0(acl00,\", \",tolower(sex))][,c(\"acl00\",\"sex\"):=NULL]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Gardening; other pet care, females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(bd=c(\"Gardening; other pet care, males\",\"Gardening; other pet care, males\"),geo=c(\" \",\"  \"),values=c(NA,NA))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Gardening; other pet care, females\",bd)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Turkey','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "bd_ord<-sort(unique(dt$bd),decreasing=TRUE)\n",
    "dt$bd<-factor(dt$bd,levels=bd_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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tGzEXbevA1e6s0S95ZBeHXzI7bkMU442Szr593GS8vVFMDMRzKEVb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2EK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\ngM/3w6jhxdZbVKXPwYMH1RMQAAAAAACA3qjRsyqtSiai9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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig10_colors<-c(\"#FAA519\",\"#286EB4\") #\"#FCC975\",\"#71A8DF\",\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt, aes(fill=bd),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  scale_fill_manual(values = fig10_colors)+\n",
    "  scale_y_continuous(limits=c(0,25),breaks=seq(0,25,5)) +\n",
    "  ggtitle(\"Figure 10: Participation rate per day in gardening and pet care, by gender, % (2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 11a: Participation time per day in the most common secondary activities watching TV and listening to radio, (hh mm; 2008 to 2015)\n",
    "\n",
    "The data is in a different dataset then in the previous figures, this the data is in the *tus_00educ2*. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^tv|^radio\",sex=\"total\",isced97=\"^all\")`. This time again we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility, because we need the time values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>isced97</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Austria                                         </td><td>2010</td><td>1:01</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Belgium                                         </td><td>2010</td><td>1:18</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>1:11</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Estonia                                         </td><td>2010</td><td>1:27</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Greece                                          </td><td>2010</td><td>1:28</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Spain                                           </td><td>2010</td><td>1:17</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Finland                                         </td><td>2010</td><td>0:58</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>France                                          </td><td>2010</td><td>1:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Hungary                                         </td><td>2010</td><td>1:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Italy                                           </td><td>2010</td><td>1:39</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Luxembourg                                      </td><td>2010</td><td>1:06</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Netherlands                                     </td><td>2010</td><td>0:54</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Norway                                          </td><td>2010</td><td>0:57</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Poland                                          </td><td>2010</td><td>0:55</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Romania                                         </td><td>2010</td><td>1:12</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Serbia                                          </td><td>2010</td><td>1:03</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>TV and video   </td><td>United Kingdom                                  </td><td>2010</td><td>1:10</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Austria                                         </td><td>2010</td><td>1:37</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Belgium                                         </td><td>2010</td><td>2:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>2:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Estonia                                         </td><td>2010</td><td>1:54</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Greece                                          </td><td>2010</td><td>1:24</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Spain                                           </td><td>2010</td><td>1:37</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Finland                                         </td><td>2010</td><td>1:48</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>France                                          </td><td>2010</td><td>2:10</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Hungary                                         </td><td>2010</td><td>2:05</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Italy                                           </td><td>2010</td><td>1:34</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Luxembourg                                      </td><td>2010</td><td>1:31</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Netherlands                                     </td><td>2010</td><td>1:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Norway                                          </td><td>2010</td><td>1:35</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Poland                                          </td><td>2010</td><td>1:25</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Romania                                         </td><td>2010</td><td>2:29</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Serbia                                          </td><td>2010</td><td>1:28</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Radio and music</td><td>United Kingdom                                  </td><td>2010</td><td>1:44</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & isced97 & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Austria                                          & 2010 & 1:01\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Belgium                                          & 2010 & 1:18\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Germany (until 1990 former territory of the FRG) & 2010 & 1:11\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Estonia                                          & 2010 & 1:27\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Greece                                           & 2010 & 1:28\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Spain                                            & 2010 & 1:17\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Finland                                          & 2010 & 0:58\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & France                                           & 2010 & 1:50\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Hungary                                          & 2010 & 1:46\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Italy                                            & 2010 & 1:39\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Luxembourg                                       & 2010 & 1:06\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Netherlands                                      & 2010 & 0:54\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Norway                                           & 2010 & 0:57\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Poland                                           & 2010 & 0:55\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Romania                                          & 2010 & 1:12\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Serbia                                           & 2010 & 1:03\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & TV and video    & United Kingdom                                   & 2010 & 1:10\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Austria                                          & 2010 & 1:37\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Belgium                                          & 2010 & 2:00\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Germany (until 1990 former territory of the FRG) & 2010 & 2:00\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Estonia                                          & 2010 & 1:54\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Greece                                           & 2010 & 1:24\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Spain                                            & 2010 & 1:37\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Finland                                          & 2010 & 1:48\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & France                                           & 2010 & 2:10\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Hungary                                          & 2010 & 2:05\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Italy                                            & 2010 & 1:34\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Luxembourg                                       & 2010 & 1:31\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Netherlands                                      & 2010 & 1:46\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Norway                                           & 2010 & 1:35\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Poland                                           & 2010 & 1:25\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Romania                                          & 2010 & 2:29\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Serbia                                           & 2010 & 1:28\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Radio and music & United Kingdom                                   & 2010 & 1:44\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | isced97 &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Austria                                          | 2010 | 1:01 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Belgium                                          | 2010 | 1:18 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Germany (until 1990 former territory of the FRG) | 2010 | 1:11 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Estonia                                          | 2010 | 1:27 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Greece                                           | 2010 | 1:28 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Spain                                            | 2010 | 1:17 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Finland                                          | 2010 | 0:58 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | France                                           | 2010 | 1:50 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Hungary                                          | 2010 | 1:46 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Italy                                            | 2010 | 1:39 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Luxembourg                                       | 2010 | 1:06 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Netherlands                                      | 2010 | 0:54 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Norway                                           | 2010 | 0:57 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Poland                                           | 2010 | 0:55 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Romania                                          | 2010 | 1:12 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Serbia                                           | 2010 | 1:03 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | TV and video    | United Kingdom                                   | 2010 | 1:10 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Austria                                          | 2010 | 1:37 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Belgium                                          | 2010 | 2:00 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Germany (until 1990 former territory of the FRG) | 2010 | 2:00 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Estonia                                          | 2010 | 1:54 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Greece                                           | 2010 | 1:24 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Spain                                            | 2010 | 1:37 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Finland                                          | 2010 | 1:48 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | France                                           | 2010 | 2:10 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Hungary                                          | 2010 | 2:05 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Italy                                            | 2010 | 1:34 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Luxembourg                                       | 2010 | 1:31 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Netherlands                                      | 2010 | 1:46 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Norway                                           | 2010 | 1:35 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Poland                                           | 2010 | 1:25 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Romania                                          | 2010 | 2:29 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Serbia                                           | 2010 | 1:28 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Radio and music | United Kingdom                                   | 2010 | 1:44 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                       sex   isced97               acl00          \n",
       "1  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "2  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "3  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "4  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "5  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "6  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "7  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "8  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "9  Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "10 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "11 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "12 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "13 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "14 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "15 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "16 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "17 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "18 Participation time (hh:mm) Total All ISCED 1997 levels TV and video   \n",
       "19 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "20 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "21 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "22 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "23 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "24 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "25 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "26 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "27 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "28 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "29 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "30 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "31 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "32 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "33 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "34 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "35 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "36 Participation time (hh:mm) Total All ISCED 1997 levels Radio and music\n",
       "   geo                                              time values\n",
       "1  Austria                                          2010 1:01  \n",
       "2  Belgium                                          2010 1:18  \n",
       "3  Germany (until 1990 former territory of the FRG) 2010 1:11  \n",
       "4  Estonia                                          2010 1:27  \n",
       "5  Greece                                           2010 1:28  \n",
       "6  Spain                                            2010 1:17  \n",
       "7  Finland                                          2010 0:58  \n",
       "8  France                                           2010 1:50  \n",
       "9  Hungary                                          2010 1:46  \n",
       "10 Italy                                            2010 1:39  \n",
       "11 Luxembourg                                       2010 1:06  \n",
       "12 Netherlands                                      2010 0:54  \n",
       "13 Norway                                           2010 0:57  \n",
       "14 Poland                                           2010 0:55  \n",
       "15 Romania                                          2010 1:12  \n",
       "16 Serbia                                           2010 1:03  \n",
       "17 Turkey                                           2010 0:00  \n",
       "18 United Kingdom                                   2010 1:10  \n",
       "19 Austria                                          2010 1:37  \n",
       "20 Belgium                                          2010 2:00  \n",
       "21 Germany (until 1990 former territory of the FRG) 2010 2:00  \n",
       "22 Estonia                                          2010 1:54  \n",
       "23 Greece                                           2010 1:24  \n",
       "24 Spain                                            2010 1:37  \n",
       "25 Finland                                          2010 1:48  \n",
       "26 France                                           2010 2:10  \n",
       "27 Hungary                                          2010 2:05  \n",
       "28 Italy                                            2010 1:34  \n",
       "29 Luxembourg                                       2010 1:31  \n",
       "30 Netherlands                                      2010 1:46  \n",
       "31 Norway                                           2010 1:35  \n",
       "32 Poland                                           2010 1:25  \n",
       "33 Romania                                          2010 2:29  \n",
       "34 Serbia                                           2010 1:28  \n",
       "35 Turkey                                           2010 0:00  \n",
       "36 United Kingdom                                   2010 1:44  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ2\",filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^tv|^radio\",sex=\"total\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we convert the values from characters/factors to time values using the *chron* package and keep only the columns with activities, countries and values. We drop the values for Turkey as it is 0. Before plotting the values we need to cut the brackets from the name of Germany. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 34 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>TV and video   </td><td>Austria       </td><td>01:01:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Belgium       </td><td>01:18:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Germany       </td><td>01:11:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Estonia       </td><td>01:27:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Greece        </td><td>01:28:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Spain         </td><td>01:17:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Finland       </td><td>00:58:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>France        </td><td>01:50:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Hungary       </td><td>01:46:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Italy         </td><td>01:39:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Luxembourg    </td><td>01:06:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Netherlands   </td><td>00:54:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Norway        </td><td>00:57:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Poland        </td><td>00:55:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Romania       </td><td>01:12:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>Serbia        </td><td>01:03:00</td></tr>\n",
       "\t<tr><td>TV and video   </td><td>United Kingdom</td><td>01:10:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Austria       </td><td>01:37:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Belgium       </td><td>02:00:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Germany       </td><td>02:00:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Estonia       </td><td>01:54:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Greece        </td><td>01:24:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Spain         </td><td>01:37:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Finland       </td><td>01:48:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>France        </td><td>02:10:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Hungary       </td><td>02:05:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Italy         </td><td>01:34:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Luxembourg    </td><td>01:31:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Netherlands   </td><td>01:46:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Norway        </td><td>01:35:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Poland        </td><td>01:25:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Romania       </td><td>02:29:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>Serbia        </td><td>01:28:00</td></tr>\n",
       "\t<tr><td>Radio and music</td><td>United Kingdom</td><td>01:44:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 34 × 3\n",
       "\\begin{tabular}{lll}\n",
       " acl00 & geo & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t TV and video    & Austria        & 01:01:00\\\\\n",
       "\t TV and video    & Belgium        & 01:18:00\\\\\n",
       "\t TV and video    & Germany        & 01:11:00\\\\\n",
       "\t TV and video    & Estonia        & 01:27:00\\\\\n",
       "\t TV and video    & Greece         & 01:28:00\\\\\n",
       "\t TV and video    & Spain          & 01:17:00\\\\\n",
       "\t TV and video    & Finland        & 00:58:00\\\\\n",
       "\t TV and video    & France         & 01:50:00\\\\\n",
       "\t TV and video    & Hungary        & 01:46:00\\\\\n",
       "\t TV and video    & Italy          & 01:39:00\\\\\n",
       "\t TV and video    & Luxembourg     & 01:06:00\\\\\n",
       "\t TV and video    & Netherlands    & 00:54:00\\\\\n",
       "\t TV and video    & Norway         & 00:57:00\\\\\n",
       "\t TV and video    & Poland         & 00:55:00\\\\\n",
       "\t TV and video    & Romania        & 01:12:00\\\\\n",
       "\t TV and video    & Serbia         & 01:03:00\\\\\n",
       "\t TV and video    & United Kingdom & 01:10:00\\\\\n",
       "\t Radio and music & Austria        & 01:37:00\\\\\n",
       "\t Radio and music & Belgium        & 02:00:00\\\\\n",
       "\t Radio and music & Germany        & 02:00:00\\\\\n",
       "\t Radio and music & Estonia        & 01:54:00\\\\\n",
       "\t Radio and music & Greece         & 01:24:00\\\\\n",
       "\t Radio and music & Spain          & 01:37:00\\\\\n",
       "\t Radio and music & Finland        & 01:48:00\\\\\n",
       "\t Radio and music & France         & 02:10:00\\\\\n",
       "\t Radio and music & Hungary        & 02:05:00\\\\\n",
       "\t Radio and music & Italy          & 01:34:00\\\\\n",
       "\t Radio and music & Luxembourg     & 01:31:00\\\\\n",
       "\t Radio and music & Netherlands    & 01:46:00\\\\\n",
       "\t Radio and music & Norway         & 01:35:00\\\\\n",
       "\t Radio and music & Poland         & 01:25:00\\\\\n",
       "\t Radio and music & Romania        & 02:29:00\\\\\n",
       "\t Radio and music & Serbia         & 01:28:00\\\\\n",
       "\t Radio and music & United Kingdom & 01:44:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 34 × 3\n",
       "\n",
       "| acl00 &lt;chr&gt; | geo &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| TV and video    | Austria        | 01:01:00 |\n",
       "| TV and video    | Belgium        | 01:18:00 |\n",
       "| TV and video    | Germany        | 01:11:00 |\n",
       "| TV and video    | Estonia        | 01:27:00 |\n",
       "| TV and video    | Greece         | 01:28:00 |\n",
       "| TV and video    | Spain          | 01:17:00 |\n",
       "| TV and video    | Finland        | 00:58:00 |\n",
       "| TV and video    | France         | 01:50:00 |\n",
       "| TV and video    | Hungary        | 01:46:00 |\n",
       "| TV and video    | Italy          | 01:39:00 |\n",
       "| TV and video    | Luxembourg     | 01:06:00 |\n",
       "| TV and video    | Netherlands    | 00:54:00 |\n",
       "| TV and video    | Norway         | 00:57:00 |\n",
       "| TV and video    | Poland         | 00:55:00 |\n",
       "| TV and video    | Romania        | 01:12:00 |\n",
       "| TV and video    | Serbia         | 01:03:00 |\n",
       "| TV and video    | United Kingdom | 01:10:00 |\n",
       "| Radio and music | Austria        | 01:37:00 |\n",
       "| Radio and music | Belgium        | 02:00:00 |\n",
       "| Radio and music | Germany        | 02:00:00 |\n",
       "| Radio and music | Estonia        | 01:54:00 |\n",
       "| Radio and music | Greece         | 01:24:00 |\n",
       "| Radio and music | Spain          | 01:37:00 |\n",
       "| Radio and music | Finland        | 01:48:00 |\n",
       "| Radio and music | France         | 02:10:00 |\n",
       "| Radio and music | Hungary        | 02:05:00 |\n",
       "| Radio and music | Italy          | 01:34:00 |\n",
       "| Radio and music | Luxembourg     | 01:31:00 |\n",
       "| Radio and music | Netherlands    | 01:46:00 |\n",
       "| Radio and music | Norway         | 01:35:00 |\n",
       "| Radio and music | Poland         | 01:25:00 |\n",
       "| Radio and music | Romania        | 02:29:00 |\n",
       "| Radio and music | Serbia         | 01:28:00 |\n",
       "| Radio and music | United Kingdom | 01:44:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   acl00           geo            values  \n",
       "1  TV and video    Austria        01:01:00\n",
       "2  TV and video    Belgium        01:18:00\n",
       "3  TV and video    Germany        01:11:00\n",
       "4  TV and video    Estonia        01:27:00\n",
       "5  TV and video    Greece         01:28:00\n",
       "6  TV and video    Spain          01:17:00\n",
       "7  TV and video    Finland        00:58:00\n",
       "8  TV and video    France         01:50:00\n",
       "9  TV and video    Hungary        01:46:00\n",
       "10 TV and video    Italy          01:39:00\n",
       "11 TV and video    Luxembourg     01:06:00\n",
       "12 TV and video    Netherlands    00:54:00\n",
       "13 TV and video    Norway         00:57:00\n",
       "14 TV and video    Poland         00:55:00\n",
       "15 TV and video    Romania        01:12:00\n",
       "16 TV and video    Serbia         01:03:00\n",
       "17 TV and video    United Kingdom 01:10:00\n",
       "18 Radio and music Austria        01:37:00\n",
       "19 Radio and music Belgium        02:00:00\n",
       "20 Radio and music Germany        02:00:00\n",
       "21 Radio and music Estonia        01:54:00\n",
       "22 Radio and music Greece         01:24:00\n",
       "23 Radio and music Spain          01:37:00\n",
       "24 Radio and music Finland        01:48:00\n",
       "25 Radio and music France         02:10:00\n",
       "26 Radio and music Hungary        02:05:00\n",
       "27 Radio and music Italy          01:34:00\n",
       "28 Radio and music Luxembourg     01:31:00\n",
       "29 Radio and music Netherlands    01:46:00\n",
       "30 Radio and music Norway         01:35:00\n",
       "31 Radio and music Poland         01:25:00\n",
       "32 Radio and music Romania        02:29:00\n",
       "33 Radio and music Serbia         01:28:00\n",
       "34 Radio and music United Kingdom 01:44:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))][geo!=\"Turkey\"]\n",
    "dt<-dt[,c(\"acl00\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Radio and music*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(acl00=c(\"Radio and music\",\"Radio and music\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Radio and music\",acl00)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "acl_ord<-sort(unique(dt$acl00),decreasing=TRUE)\n",
    "dt$acl00<-factor(dt$acl00,levels=acl_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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n7F7KqKf2S/dGnj/2+ZX+0VKjgnVLTNSdg8TJzM6gVWk9zLa8VvTQxvMklKTmxz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\nzZSiyRPTfEUvWMtJXdv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ipcAiIs8v/KFbhQsrUw8vGj/m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\nDUBHfpij2rYvq3uvw4PzJHGrpr469w/1+tcqMTWkCYamdrdixQoRkcCG9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q6OQ/2mrdq2a+dlY8xWbWC6AwdFtcSZJfNvxeV91SX8loPn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\nhJg5c2aJrxYWFqZOLLtnMAaOb19j6u54IcShyOkDD7cZ1u+lhvX+WsA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YNmNEyS1/HP5jVKRn7lWKHVEedqta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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig11_colors<-c(\"#FAA519\",\"#286EB4\") #\"#FCC975\",\"#71A8DF\",\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt, aes(x=geo, y=values,fill=acl00)) + theme_minimal() +\n",
    "  geom_bar(position=\"dodge\",stat=\"identity\",width=0.7)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,1/96)) +\n",
    "  scale_fill_manual(values = fig11_colors)+\n",
    "  ggtitle(\"Figure 11a: Participation time per day in the most common secondary activities watching TV and listening to radio, (hh mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 11b: Participation time per day in the most common secondary activities socialising with family visiting and feasts (hh mm; 2008 to 2015)\n",
    "\n",
    "The data is in a different dataset then in the previous figures, this the data is in the *tus_00educ2*. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^social|^visit\",sex=\"total\",isced97=\"^all\")`. This time again we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility, because we need the time values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>isced97</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Austria                                         </td><td>2010</td><td>1:18</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Belgium                                         </td><td>2010</td><td>1:40</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Estonia                                         </td><td>2010</td><td>1:55</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Greece                                          </td><td>2010</td><td>1:36</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Spain                                           </td><td>2010</td><td>1:34</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Finland                                         </td><td>2010</td><td>1:18</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>France                                          </td><td>2010</td><td>1:37</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Hungary                                         </td><td>2010</td><td>0:30</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Italy                                           </td><td>2010</td><td>1:29</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Luxembourg                                      </td><td>2010</td><td>1:34</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Netherlands                                     </td><td>2010</td><td>1:18</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Norway                                          </td><td>2010</td><td>1:42</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Poland                                          </td><td>2010</td><td>1:35</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Romania                                         </td><td>2010</td><td>1:59</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Serbia                                          </td><td>2010</td><td>1:35</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Socialising with family</td><td>United Kingdom                                  </td><td>2010</td><td>1:08</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Austria                                         </td><td>2010</td><td>0:51</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Belgium                                         </td><td>2010</td><td>1:08</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>1:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Estonia                                         </td><td>2010</td><td>1:49</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Greece                                          </td><td>2010</td><td>2:12</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Spain                                           </td><td>2010</td><td>2:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Finland                                         </td><td>2010</td><td>1:34</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>France                                          </td><td>2010</td><td>1:56</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Hungary                                         </td><td>2010</td><td>0:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Italy                                           </td><td>2010</td><td>1:07</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Luxembourg                                      </td><td>2010</td><td>2:01</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Netherlands                                     </td><td>2010</td><td>1:39</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Norway                                          </td><td>2010</td><td>0:56</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Poland                                          </td><td>2010</td><td>1:30</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Romania                                         </td><td>2010</td><td>1:01</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Serbia                                          </td><td>2010</td><td>1:56</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Total</td><td>All ISCED 1997 levels</td><td>Visiting and feasts    </td><td>United Kingdom                                  </td><td>2010</td><td>1:33</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & isced97 & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Austria                                          & 2010 & 1:18\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Belgium                                          & 2010 & 1:40\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Germany (until 1990 former territory of the FRG) & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Estonia                                          & 2010 & 1:55\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Greece                                           & 2010 & 1:36\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Spain                                            & 2010 & 1:34\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Finland                                          & 2010 & 1:18\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & France                                           & 2010 & 1:37\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Hungary                                          & 2010 & 0:30\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Italy                                            & 2010 & 1:29\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Luxembourg                                       & 2010 & 1:34\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Netherlands                                      & 2010 & 1:18\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Norway                                           & 2010 & 1:42\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Poland                                           & 2010 & 1:35\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Romania                                          & 2010 & 1:59\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Serbia                                           & 2010 & 1:35\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Socialising with family & United Kingdom                                   & 2010 & 1:08\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Austria                                          & 2010 & 0:51\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Belgium                                          & 2010 & 1:08\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Germany (until 1990 former territory of the FRG) & 2010 & 1:47\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Estonia                                          & 2010 & 1:49\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Greece                                           & 2010 & 2:12\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Spain                                            & 2010 & 2:47\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Finland                                          & 2010 & 1:34\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & France                                           & 2010 & 1:56\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Hungary                                          & 2010 & 0:50\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Italy                                            & 2010 & 1:07\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Luxembourg                                       & 2010 & 2:01\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Netherlands                                      & 2010 & 1:39\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Norway                                           & 2010 & 0:56\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Poland                                           & 2010 & 1:30\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Romania                                          & 2010 & 1:01\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Serbia                                           & 2010 & 1:56\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Total & All ISCED 1997 levels & Visiting and feasts     & United Kingdom                                   & 2010 & 1:33\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | isced97 &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Austria                                          | 2010 | 1:18 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Belgium                                          | 2010 | 1:40 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Germany (until 1990 former territory of the FRG) | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Estonia                                          | 2010 | 1:55 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Greece                                           | 2010 | 1:36 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Spain                                            | 2010 | 1:34 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Finland                                          | 2010 | 1:18 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | France                                           | 2010 | 1:37 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Hungary                                          | 2010 | 0:30 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Italy                                            | 2010 | 1:29 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Luxembourg                                       | 2010 | 1:34 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Netherlands                                      | 2010 | 1:18 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Norway                                           | 2010 | 1:42 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Poland                                           | 2010 | 1:35 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Romania                                          | 2010 | 1:59 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Serbia                                           | 2010 | 1:35 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Socialising with family | United Kingdom                                   | 2010 | 1:08 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Austria                                          | 2010 | 0:51 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Belgium                                          | 2010 | 1:08 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Germany (until 1990 former territory of the FRG) | 2010 | 1:47 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Estonia                                          | 2010 | 1:49 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Greece                                           | 2010 | 2:12 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Spain                                            | 2010 | 2:47 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Finland                                          | 2010 | 1:34 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | France                                           | 2010 | 1:56 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Hungary                                          | 2010 | 0:50 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Italy                                            | 2010 | 1:07 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Luxembourg                                       | 2010 | 2:01 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Netherlands                                      | 2010 | 1:39 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Norway                                           | 2010 | 0:56 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Poland                                           | 2010 | 1:30 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Romania                                          | 2010 | 1:01 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Serbia                                           | 2010 | 1:56 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Total | All ISCED 1997 levels | Visiting and feasts     | United Kingdom                                   | 2010 | 1:33 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                       sex   isced97              \n",
       "1  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "2  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "3  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "4  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "5  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "6  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "7  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "8  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "9  Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "10 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "11 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "12 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "13 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "14 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "15 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "16 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "17 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "18 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "19 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "20 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "21 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "22 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "23 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "24 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "25 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "26 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "27 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "28 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "29 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "30 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "31 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "32 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "33 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "34 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "35 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "36 Participation time (hh:mm) Total All ISCED 1997 levels\n",
       "   acl00                   geo                                             \n",
       "1  Socialising with family Austria                                         \n",
       "2  Socialising with family Belgium                                         \n",
       "3  Socialising with family Germany (until 1990 former territory of the FRG)\n",
       "4  Socialising with family Estonia                                         \n",
       "5  Socialising with family Greece                                          \n",
       "6  Socialising with family Spain                                           \n",
       "7  Socialising with family Finland                                         \n",
       "8  Socialising with family France                                          \n",
       "9  Socialising with family Hungary                                         \n",
       "10 Socialising with family Italy                                           \n",
       "11 Socialising with family Luxembourg                                      \n",
       "12 Socialising with family Netherlands                                     \n",
       "13 Socialising with family Norway                                          \n",
       "14 Socialising with family Poland                                          \n",
       "15 Socialising with family Romania                                         \n",
       "16 Socialising with family Serbia                                          \n",
       "17 Socialising with family Turkey                                          \n",
       "18 Socialising with family United Kingdom                                  \n",
       "19 Visiting and feasts     Austria                                         \n",
       "20 Visiting and feasts     Belgium                                         \n",
       "21 Visiting and feasts     Germany (until 1990 former territory of the FRG)\n",
       "22 Visiting and feasts     Estonia                                         \n",
       "23 Visiting and feasts     Greece                                          \n",
       "24 Visiting and feasts     Spain                                           \n",
       "25 Visiting and feasts     Finland                                         \n",
       "26 Visiting and feasts     France                                          \n",
       "27 Visiting and feasts     Hungary                                         \n",
       "28 Visiting and feasts     Italy                                           \n",
       "29 Visiting and feasts     Luxembourg                                      \n",
       "30 Visiting and feasts     Netherlands                                     \n",
       "31 Visiting and feasts     Norway                                          \n",
       "32 Visiting and feasts     Poland                                          \n",
       "33 Visiting and feasts     Romania                                         \n",
       "34 Visiting and feasts     Serbia                                          \n",
       "35 Visiting and feasts     Turkey                                          \n",
       "36 Visiting and feasts     United Kingdom                                  \n",
       "   time values\n",
       "1  2010 1:18  \n",
       "2  2010 1:40  \n",
       "3  2010 0:00  \n",
       "4  2010 1:55  \n",
       "5  2010 1:36  \n",
       "6  2010 1:34  \n",
       "7  2010 1:18  \n",
       "8  2010 1:37  \n",
       "9  2010 0:30  \n",
       "10 2010 1:29  \n",
       "11 2010 1:34  \n",
       "12 2010 1:18  \n",
       "13 2010 1:42  \n",
       "14 2010 1:35  \n",
       "15 2010 1:59  \n",
       "16 2010 1:35  \n",
       "17 2010 0:00  \n",
       "18 2010 1:08  \n",
       "19 2010 0:51  \n",
       "20 2010 1:08  \n",
       "21 2010 1:47  \n",
       "22 2010 1:49  \n",
       "23 2010 2:12  \n",
       "24 2010 2:47  \n",
       "25 2010 1:34  \n",
       "26 2010 1:56  \n",
       "27 2010 0:50  \n",
       "28 2010 1:07  \n",
       "29 2010 2:01  \n",
       "30 2010 1:39  \n",
       "31 2010 0:56  \n",
       "32 2010 1:30  \n",
       "33 2010 1:01  \n",
       "34 2010 1:56  \n",
       "35 2010 0:00  \n",
       "36 2010 1:33  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ2\",filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^social|^visit\",sex=\"total\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we convert the values from characters/factors to time values using the *chron* package and keep only the columns with activities, countries and values. We drop the values for Turkey as it is 0. Before plotting the values we need to cut the brackets from the name of Germany. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 34 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Socialising with family</td><td>Austria       </td><td>01:18:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Belgium       </td><td>01:40:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Germany       </td><td>00:00:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Estonia       </td><td>01:55:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Greece        </td><td>01:36:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Spain         </td><td>01:34:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Finland       </td><td>01:18:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>France        </td><td>01:37:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Hungary       </td><td>00:30:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Italy         </td><td>01:29:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Luxembourg    </td><td>01:34:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Netherlands   </td><td>01:18:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Norway        </td><td>01:42:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Poland        </td><td>01:35:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Romania       </td><td>01:59:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>Serbia        </td><td>01:35:00</td></tr>\n",
       "\t<tr><td>Socialising with family</td><td>United Kingdom</td><td>01:08:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Austria       </td><td>00:51:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Belgium       </td><td>01:08:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Germany       </td><td>01:47:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Estonia       </td><td>01:49:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Greece        </td><td>02:12:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Spain         </td><td>02:47:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Finland       </td><td>01:34:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>France        </td><td>01:56:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Hungary       </td><td>00:50:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Italy         </td><td>01:07:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Luxembourg    </td><td>02:01:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Netherlands   </td><td>01:39:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Norway        </td><td>00:56:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Poland        </td><td>01:30:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Romania       </td><td>01:01:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>Serbia        </td><td>01:56:00</td></tr>\n",
       "\t<tr><td>Visiting and feasts    </td><td>United Kingdom</td><td>01:33:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 34 × 3\n",
       "\\begin{tabular}{lll}\n",
       " acl00 & geo & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Socialising with family & Austria        & 01:18:00\\\\\n",
       "\t Socialising with family & Belgium        & 01:40:00\\\\\n",
       "\t Socialising with family & Germany        & 00:00:00\\\\\n",
       "\t Socialising with family & Estonia        & 01:55:00\\\\\n",
       "\t Socialising with family & Greece         & 01:36:00\\\\\n",
       "\t Socialising with family & Spain          & 01:34:00\\\\\n",
       "\t Socialising with family & Finland        & 01:18:00\\\\\n",
       "\t Socialising with family & France         & 01:37:00\\\\\n",
       "\t Socialising with family & Hungary        & 00:30:00\\\\\n",
       "\t Socialising with family & Italy          & 01:29:00\\\\\n",
       "\t Socialising with family & Luxembourg     & 01:34:00\\\\\n",
       "\t Socialising with family & Netherlands    & 01:18:00\\\\\n",
       "\t Socialising with family & Norway         & 01:42:00\\\\\n",
       "\t Socialising with family & Poland         & 01:35:00\\\\\n",
       "\t Socialising with family & Romania        & 01:59:00\\\\\n",
       "\t Socialising with family & Serbia         & 01:35:00\\\\\n",
       "\t Socialising with family & United Kingdom & 01:08:00\\\\\n",
       "\t Visiting and feasts     & Austria        & 00:51:00\\\\\n",
       "\t Visiting and feasts     & Belgium        & 01:08:00\\\\\n",
       "\t Visiting and feasts     & Germany        & 01:47:00\\\\\n",
       "\t Visiting and feasts     & Estonia        & 01:49:00\\\\\n",
       "\t Visiting and feasts     & Greece         & 02:12:00\\\\\n",
       "\t Visiting and feasts     & Spain          & 02:47:00\\\\\n",
       "\t Visiting and feasts     & Finland        & 01:34:00\\\\\n",
       "\t Visiting and feasts     & France         & 01:56:00\\\\\n",
       "\t Visiting and feasts     & Hungary        & 00:50:00\\\\\n",
       "\t Visiting and feasts     & Italy          & 01:07:00\\\\\n",
       "\t Visiting and feasts     & Luxembourg     & 02:01:00\\\\\n",
       "\t Visiting and feasts     & Netherlands    & 01:39:00\\\\\n",
       "\t Visiting and feasts     & Norway         & 00:56:00\\\\\n",
       "\t Visiting and feasts     & Poland         & 01:30:00\\\\\n",
       "\t Visiting and feasts     & Romania        & 01:01:00\\\\\n",
       "\t Visiting and feasts     & Serbia         & 01:56:00\\\\\n",
       "\t Visiting and feasts     & United Kingdom & 01:33:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 34 × 3\n",
       "\n",
       "| acl00 &lt;chr&gt; | geo &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Socialising with family | Austria        | 01:18:00 |\n",
       "| Socialising with family | Belgium        | 01:40:00 |\n",
       "| Socialising with family | Germany        | 00:00:00 |\n",
       "| Socialising with family | Estonia        | 01:55:00 |\n",
       "| Socialising with family | Greece         | 01:36:00 |\n",
       "| Socialising with family | Spain          | 01:34:00 |\n",
       "| Socialising with family | Finland        | 01:18:00 |\n",
       "| Socialising with family | France         | 01:37:00 |\n",
       "| Socialising with family | Hungary        | 00:30:00 |\n",
       "| Socialising with family | Italy          | 01:29:00 |\n",
       "| Socialising with family | Luxembourg     | 01:34:00 |\n",
       "| Socialising with family | Netherlands    | 01:18:00 |\n",
       "| Socialising with family | Norway         | 01:42:00 |\n",
       "| Socialising with family | Poland         | 01:35:00 |\n",
       "| Socialising with family | Romania        | 01:59:00 |\n",
       "| Socialising with family | Serbia         | 01:35:00 |\n",
       "| Socialising with family | United Kingdom | 01:08:00 |\n",
       "| Visiting and feasts     | Austria        | 00:51:00 |\n",
       "| Visiting and feasts     | Belgium        | 01:08:00 |\n",
       "| Visiting and feasts     | Germany        | 01:47:00 |\n",
       "| Visiting and feasts     | Estonia        | 01:49:00 |\n",
       "| Visiting and feasts     | Greece         | 02:12:00 |\n",
       "| Visiting and feasts     | Spain          | 02:47:00 |\n",
       "| Visiting and feasts     | Finland        | 01:34:00 |\n",
       "| Visiting and feasts     | France         | 01:56:00 |\n",
       "| Visiting and feasts     | Hungary        | 00:50:00 |\n",
       "| Visiting and feasts     | Italy          | 01:07:00 |\n",
       "| Visiting and feasts     | Luxembourg     | 02:01:00 |\n",
       "| Visiting and feasts     | Netherlands    | 01:39:00 |\n",
       "| Visiting and feasts     | Norway         | 00:56:00 |\n",
       "| Visiting and feasts     | Poland         | 01:30:00 |\n",
       "| Visiting and feasts     | Romania        | 01:01:00 |\n",
       "| Visiting and feasts     | Serbia         | 01:56:00 |\n",
       "| Visiting and feasts     | United Kingdom | 01:33:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   acl00                   geo            values  \n",
       "1  Socialising with family Austria        01:18:00\n",
       "2  Socialising with family Belgium        01:40:00\n",
       "3  Socialising with family Germany        00:00:00\n",
       "4  Socialising with family Estonia        01:55:00\n",
       "5  Socialising with family Greece         01:36:00\n",
       "6  Socialising with family Spain          01:34:00\n",
       "7  Socialising with family Finland        01:18:00\n",
       "8  Socialising with family France         01:37:00\n",
       "9  Socialising with family Hungary        00:30:00\n",
       "10 Socialising with family Italy          01:29:00\n",
       "11 Socialising with family Luxembourg     01:34:00\n",
       "12 Socialising with family Netherlands    01:18:00\n",
       "13 Socialising with family Norway         01:42:00\n",
       "14 Socialising with family Poland         01:35:00\n",
       "15 Socialising with family Romania        01:59:00\n",
       "16 Socialising with family Serbia         01:35:00\n",
       "17 Socialising with family United Kingdom 01:08:00\n",
       "18 Visiting and feasts     Austria        00:51:00\n",
       "19 Visiting and feasts     Belgium        01:08:00\n",
       "20 Visiting and feasts     Germany        01:47:00\n",
       "21 Visiting and feasts     Estonia        01:49:00\n",
       "22 Visiting and feasts     Greece         02:12:00\n",
       "23 Visiting and feasts     Spain          02:47:00\n",
       "24 Visiting and feasts     Finland        01:34:00\n",
       "25 Visiting and feasts     France         01:56:00\n",
       "26 Visiting and feasts     Hungary        00:50:00\n",
       "27 Visiting and feasts     Italy          01:07:00\n",
       "28 Visiting and feasts     Luxembourg     02:01:00\n",
       "29 Visiting and feasts     Netherlands    01:39:00\n",
       "30 Visiting and feasts     Norway         00:56:00\n",
       "31 Visiting and feasts     Poland         01:30:00\n",
       "32 Visiting and feasts     Romania        01:01:00\n",
       "33 Visiting and feasts     Serbia         01:56:00\n",
       "34 Visiting and feasts     United Kingdom 01:33:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))][geo!=\"Turkey\"]\n",
    "dt<-dt[,c(\"acl00\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Visiting and feasts*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(acl00=c(\"Visiting and feasts\",\"Visiting and feasts\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Visiting and feasts\",acl00)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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LjEbWuvTn48yww1T5nErRNBp+1mpU/Q7osLk5Z547jzN96XD4q0vqnRaAre7uje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\nfcjbcifumYQB84bGWJ/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890Jiaeq/MAAAIABJREFUZ5I+KCKq2yP9Y+5orEmk3oxeoJdff/31qve9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a8u5/sJ4bv/zSz3vhUr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\n7vE2AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig11_colors<-c(\"#FAA519\",\"#286EB4\") #\"#FCC975\",\"#71A8DF\",\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt, aes(x=geo, y=values,fill=acl00)) + theme_minimal() +\n",
    "  geom_bar(position=\"dodge\",stat=\"identity\",width=0.7)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,1/96)) +\n",
    "  scale_fill_manual(values = fig11_colors)+\n",
    "  ggtitle(\"Figure 11b: Participation time per day in the most common secondary activities socialising with family visiting and feasts (hh mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 11c: Participation time per day in childcare as secondary activity, by gender, (hh mm; 2008 to 2015)\n",
    "\n",
    "The data is in a different dataset then in the previous figures, this the data is in the *tus_00educ2*. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^child\",sex=\"male\",isced97=\"^all\")`. This time again we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility, because we need the time values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>isced97</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Austria                                         </td><td>2010</td><td>1:09</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Belgium                                         </td><td>2010</td><td>1:07</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>1:59</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Estonia                                         </td><td>2010</td><td>1:39</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Greece                                          </td><td>2010</td><td>1:10</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Spain                                           </td><td>2010</td><td>1:15</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Finland                                         </td><td>2010</td><td>1:27</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>France                                          </td><td>2010</td><td>1:09</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Hungary                                         </td><td>2010</td><td>1:26</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Italy                                           </td><td>2010</td><td>1:10</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Luxembourg                                      </td><td>2010</td><td>0:49</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Netherlands                                     </td><td>2010</td><td>0:54</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Norway                                          </td><td>2010</td><td>1:04</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Poland                                          </td><td>2010</td><td>1:18</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Romania                                         </td><td>2010</td><td>1:44</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Serbia                                          </td><td>2010</td><td>1:10</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>United Kingdom                                  </td><td>2010</td><td>1:11</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Austria                                         </td><td>2010</td><td>0:53</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Belgium                                         </td><td>2010</td><td>1:02</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>1:22</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Estonia                                         </td><td>2010</td><td>1:07</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Greece                                          </td><td>2010</td><td>0:59</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Spain                                           </td><td>2010</td><td>1:07</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Finland                                         </td><td>2010</td><td>0:51</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>France                                          </td><td>2010</td><td>1:02</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Hungary                                         </td><td>2010</td><td>0:51</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Italy                                           </td><td>2010</td><td>0:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Luxembourg                                      </td><td>2010</td><td>0:45</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Netherlands                                     </td><td>2010</td><td>0:43</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Norway                                          </td><td>2010</td><td>0:52</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Poland                                          </td><td>2010</td><td>0:56</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Romania                                         </td><td>2010</td><td>1:11</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Serbia                                          </td><td>2010</td><td>1:11</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Childcare, including teaching, reading and talking with child</td><td>United Kingdom                                  </td><td>2010</td><td>1:01</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & isced97 & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Austria                                          & 2010 & 1:09\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Belgium                                          & 2010 & 1:07\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Germany (until 1990 former territory of the FRG) & 2010 & 1:59\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Estonia                                          & 2010 & 1:39\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Greece                                           & 2010 & 1:10\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Spain                                            & 2010 & 1:15\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Finland                                          & 2010 & 1:27\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & France                                           & 2010 & 1:09\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Hungary                                          & 2010 & 1:26\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Italy                                            & 2010 & 1:10\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Luxembourg                                       & 2010 & 0:49\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Netherlands                                      & 2010 & 0:54\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Norway                                           & 2010 & 1:04\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Poland                                           & 2010 & 1:18\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Romania                                          & 2010 & 1:44\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Serbia                                           & 2010 & 1:10\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & United Kingdom                                   & 2010 & 1:11\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Austria                                          & 2010 & 0:53\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Belgium                                          & 2010 & 1:02\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Germany (until 1990 former territory of the FRG) & 2010 & 1:22\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Estonia                                          & 2010 & 1:07\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Greece                                           & 2010 & 0:59\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Spain                                            & 2010 & 1:07\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Finland                                          & 2010 & 0:51\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & France                                           & 2010 & 1:02\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Hungary                                          & 2010 & 0:51\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Italy                                            & 2010 & 0:47\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Luxembourg                                       & 2010 & 0:45\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Netherlands                                      & 2010 & 0:43\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Norway                                           & 2010 & 0:52\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Poland                                           & 2010 & 0:56\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Romania                                          & 2010 & 1:11\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Serbia                                           & 2010 & 1:11\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Childcare, including teaching, reading and talking with child & United Kingdom                                   & 2010 & 1:01\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | isced97 &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Austria                                          | 2010 | 1:09 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Belgium                                          | 2010 | 1:07 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Germany (until 1990 former territory of the FRG) | 2010 | 1:59 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Estonia                                          | 2010 | 1:39 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Greece                                           | 2010 | 1:10 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Spain                                            | 2010 | 1:15 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Finland                                          | 2010 | 1:27 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | France                                           | 2010 | 1:09 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Hungary                                          | 2010 | 1:26 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Italy                                            | 2010 | 1:10 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Luxembourg                                       | 2010 | 0:49 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Netherlands                                      | 2010 | 0:54 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Norway                                           | 2010 | 1:04 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Poland                                           | 2010 | 1:18 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Romania                                          | 2010 | 1:44 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Serbia                                           | 2010 | 1:10 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | United Kingdom                                   | 2010 | 1:11 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Austria                                          | 2010 | 0:53 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Belgium                                          | 2010 | 1:02 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Germany (until 1990 former territory of the FRG) | 2010 | 1:22 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Estonia                                          | 2010 | 1:07 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Greece                                           | 2010 | 0:59 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Spain                                            | 2010 | 1:07 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Finland                                          | 2010 | 0:51 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | France                                           | 2010 | 1:02 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Hungary                                          | 2010 | 0:51 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Italy                                            | 2010 | 0:47 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Luxembourg                                       | 2010 | 0:45 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Netherlands                                      | 2010 | 0:43 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Norway                                           | 2010 | 0:52 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Poland                                           | 2010 | 0:56 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Romania                                          | 2010 | 1:11 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Serbia                                           | 2010 | 1:11 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Childcare, including teaching, reading and talking with child | United Kingdom                                   | 2010 | 1:01 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                       sex     isced97              \n",
       "1  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "2  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "3  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "4  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "5  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "6  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "7  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "8  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "9  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "10 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "11 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "12 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "13 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "14 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "15 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "16 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "17 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "18 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "19 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "20 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "21 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "22 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "23 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "24 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "25 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "26 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "27 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "28 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "29 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "30 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "31 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "32 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "33 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "34 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "35 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "36 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "   acl00                                                        \n",
       "1  Childcare, including teaching, reading and talking with child\n",
       "2  Childcare, including teaching, reading and talking with child\n",
       "3  Childcare, including teaching, reading and talking with child\n",
       "4  Childcare, including teaching, reading and talking with child\n",
       "5  Childcare, including teaching, reading and talking with child\n",
       "6  Childcare, including teaching, reading and talking with child\n",
       "7  Childcare, including teaching, reading and talking with child\n",
       "8  Childcare, including teaching, reading and talking with child\n",
       "9  Childcare, including teaching, reading and talking with child\n",
       "10 Childcare, including teaching, reading and talking with child\n",
       "11 Childcare, including teaching, reading and talking with child\n",
       "12 Childcare, including teaching, reading and talking with child\n",
       "13 Childcare, including teaching, reading and talking with child\n",
       "14 Childcare, including teaching, reading and talking with child\n",
       "15 Childcare, including teaching, reading and talking with child\n",
       "16 Childcare, including teaching, reading and talking with child\n",
       "17 Childcare, including teaching, reading and talking with child\n",
       "18 Childcare, including teaching, reading and talking with child\n",
       "19 Childcare, including teaching, reading and talking with child\n",
       "20 Childcare, including teaching, reading and talking with child\n",
       "21 Childcare, including teaching, reading and talking with child\n",
       "22 Childcare, including teaching, reading and talking with child\n",
       "23 Childcare, including teaching, reading and talking with child\n",
       "24 Childcare, including teaching, reading and talking with child\n",
       "25 Childcare, including teaching, reading and talking with child\n",
       "26 Childcare, including teaching, reading and talking with child\n",
       "27 Childcare, including teaching, reading and talking with child\n",
       "28 Childcare, including teaching, reading and talking with child\n",
       "29 Childcare, including teaching, reading and talking with child\n",
       "30 Childcare, including teaching, reading and talking with child\n",
       "31 Childcare, including teaching, reading and talking with child\n",
       "32 Childcare, including teaching, reading and talking with child\n",
       "33 Childcare, including teaching, reading and talking with child\n",
       "34 Childcare, including teaching, reading and talking with child\n",
       "35 Childcare, including teaching, reading and talking with child\n",
       "36 Childcare, including teaching, reading and talking with child\n",
       "   geo                                              time values\n",
       "1  Austria                                          2010 1:09  \n",
       "2  Belgium                                          2010 1:07  \n",
       "3  Germany (until 1990 former territory of the FRG) 2010 1:59  \n",
       "4  Estonia                                          2010 1:39  \n",
       "5  Greece                                           2010 1:10  \n",
       "6  Spain                                            2010 1:15  \n",
       "7  Finland                                          2010 1:27  \n",
       "8  France                                           2010 1:09  \n",
       "9  Hungary                                          2010 1:26  \n",
       "10 Italy                                            2010 1:10  \n",
       "11 Luxembourg                                       2010 0:49  \n",
       "12 Netherlands                                      2010 0:54  \n",
       "13 Norway                                           2010 1:04  \n",
       "14 Poland                                           2010 1:18  \n",
       "15 Romania                                          2010 1:44  \n",
       "16 Serbia                                           2010 1:10  \n",
       "17 Turkey                                           2010 0:00  \n",
       "18 United Kingdom                                   2010 1:11  \n",
       "19 Austria                                          2010 0:53  \n",
       "20 Belgium                                          2010 1:02  \n",
       "21 Germany (until 1990 former territory of the FRG) 2010 1:22  \n",
       "22 Estonia                                          2010 1:07  \n",
       "23 Greece                                           2010 0:59  \n",
       "24 Spain                                            2010 1:07  \n",
       "25 Finland                                          2010 0:51  \n",
       "26 France                                           2010 1:02  \n",
       "27 Hungary                                          2010 0:51  \n",
       "28 Italy                                            2010 0:47  \n",
       "29 Luxembourg                                       2010 0:45  \n",
       "30 Netherlands                                      2010 0:43  \n",
       "31 Norway                                           2010 0:52  \n",
       "32 Poland                                           2010 0:56  \n",
       "33 Romania                                          2010 1:11  \n",
       "34 Serbia                                           2010 1:11  \n",
       "35 Turkey                                           2010 0:00  \n",
       "36 United Kingdom                                   2010 1:01  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ2\",filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^child\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we convert the values from characters/factors to time values using the *chron* package and keep only the columns with activities, countries and values. We drop the values for Turkey as it is 0. Before plotting the values we need to cut the brackets from the name of Germany. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 34 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Females</td><td>Austria       </td><td>01:09:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Belgium       </td><td>01:07:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Germany       </td><td>01:59:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Estonia       </td><td>01:39:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Greece        </td><td>01:10:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Spain         </td><td>01:15:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Finland       </td><td>01:27:00</td></tr>\n",
       "\t<tr><td>Females</td><td>France        </td><td>01:09:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Hungary       </td><td>01:26:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Italy         </td><td>01:10:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Luxembourg    </td><td>00:49:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Netherlands   </td><td>00:54:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Norway        </td><td>01:04:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Poland        </td><td>01:18:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Romania       </td><td>01:44:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Serbia        </td><td>01:10:00</td></tr>\n",
       "\t<tr><td>Females</td><td>United Kingdom</td><td>01:11:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Austria       </td><td>00:53:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Belgium       </td><td>01:02:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Germany       </td><td>01:22:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Estonia       </td><td>01:07:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Greece        </td><td>00:59:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Spain         </td><td>01:07:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Finland       </td><td>00:51:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>France        </td><td>01:02:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Hungary       </td><td>00:51:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Italy         </td><td>00:47:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Luxembourg    </td><td>00:45:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Netherlands   </td><td>00:43:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Norway        </td><td>00:52:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Poland        </td><td>00:56:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Romania       </td><td>01:11:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Serbia        </td><td>01:11:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>United Kingdom</td><td>01:01:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 34 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Females & Austria        & 01:09:00\\\\\n",
       "\t Females & Belgium        & 01:07:00\\\\\n",
       "\t Females & Germany        & 01:59:00\\\\\n",
       "\t Females & Estonia        & 01:39:00\\\\\n",
       "\t Females & Greece         & 01:10:00\\\\\n",
       "\t Females & Spain          & 01:15:00\\\\\n",
       "\t Females & Finland        & 01:27:00\\\\\n",
       "\t Females & France         & 01:09:00\\\\\n",
       "\t Females & Hungary        & 01:26:00\\\\\n",
       "\t Females & Italy          & 01:10:00\\\\\n",
       "\t Females & Luxembourg     & 00:49:00\\\\\n",
       "\t Females & Netherlands    & 00:54:00\\\\\n",
       "\t Females & Norway         & 01:04:00\\\\\n",
       "\t Females & Poland         & 01:18:00\\\\\n",
       "\t Females & Romania        & 01:44:00\\\\\n",
       "\t Females & Serbia         & 01:10:00\\\\\n",
       "\t Females & United Kingdom & 01:11:00\\\\\n",
       "\t Males   & Austria        & 00:53:00\\\\\n",
       "\t Males   & Belgium        & 01:02:00\\\\\n",
       "\t Males   & Germany        & 01:22:00\\\\\n",
       "\t Males   & Estonia        & 01:07:00\\\\\n",
       "\t Males   & Greece         & 00:59:00\\\\\n",
       "\t Males   & Spain          & 01:07:00\\\\\n",
       "\t Males   & Finland        & 00:51:00\\\\\n",
       "\t Males   & France         & 01:02:00\\\\\n",
       "\t Males   & Hungary        & 00:51:00\\\\\n",
       "\t Males   & Italy          & 00:47:00\\\\\n",
       "\t Males   & Luxembourg     & 00:45:00\\\\\n",
       "\t Males   & Netherlands    & 00:43:00\\\\\n",
       "\t Males   & Norway         & 00:52:00\\\\\n",
       "\t Males   & Poland         & 00:56:00\\\\\n",
       "\t Males   & Romania        & 01:11:00\\\\\n",
       "\t Males   & Serbia         & 01:11:00\\\\\n",
       "\t Males   & United Kingdom & 01:01:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 34 × 3\n",
       "\n",
       "| sex &lt;chr&gt; | geo &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Females | Austria        | 01:09:00 |\n",
       "| Females | Belgium        | 01:07:00 |\n",
       "| Females | Germany        | 01:59:00 |\n",
       "| Females | Estonia        | 01:39:00 |\n",
       "| Females | Greece         | 01:10:00 |\n",
       "| Females | Spain          | 01:15:00 |\n",
       "| Females | Finland        | 01:27:00 |\n",
       "| Females | France         | 01:09:00 |\n",
       "| Females | Hungary        | 01:26:00 |\n",
       "| Females | Italy          | 01:10:00 |\n",
       "| Females | Luxembourg     | 00:49:00 |\n",
       "| Females | Netherlands    | 00:54:00 |\n",
       "| Females | Norway         | 01:04:00 |\n",
       "| Females | Poland         | 01:18:00 |\n",
       "| Females | Romania        | 01:44:00 |\n",
       "| Females | Serbia         | 01:10:00 |\n",
       "| Females | United Kingdom | 01:11:00 |\n",
       "| Males   | Austria        | 00:53:00 |\n",
       "| Males   | Belgium        | 01:02:00 |\n",
       "| Males   | Germany        | 01:22:00 |\n",
       "| Males   | Estonia        | 01:07:00 |\n",
       "| Males   | Greece         | 00:59:00 |\n",
       "| Males   | Spain          | 01:07:00 |\n",
       "| Males   | Finland        | 00:51:00 |\n",
       "| Males   | France         | 01:02:00 |\n",
       "| Males   | Hungary        | 00:51:00 |\n",
       "| Males   | Italy          | 00:47:00 |\n",
       "| Males   | Luxembourg     | 00:45:00 |\n",
       "| Males   | Netherlands    | 00:43:00 |\n",
       "| Males   | Norway         | 00:52:00 |\n",
       "| Males   | Poland         | 00:56:00 |\n",
       "| Males   | Romania        | 01:11:00 |\n",
       "| Males   | Serbia         | 01:11:00 |\n",
       "| Males   | United Kingdom | 01:01:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   sex     geo            values  \n",
       "1  Females Austria        01:09:00\n",
       "2  Females Belgium        01:07:00\n",
       "3  Females Germany        01:59:00\n",
       "4  Females Estonia        01:39:00\n",
       "5  Females Greece         01:10:00\n",
       "6  Females Spain          01:15:00\n",
       "7  Females Finland        01:27:00\n",
       "8  Females France         01:09:00\n",
       "9  Females Hungary        01:26:00\n",
       "10 Females Italy          01:10:00\n",
       "11 Females Luxembourg     00:49:00\n",
       "12 Females Netherlands    00:54:00\n",
       "13 Females Norway         01:04:00\n",
       "14 Females Poland         01:18:00\n",
       "15 Females Romania        01:44:00\n",
       "16 Females Serbia         01:10:00\n",
       "17 Females United Kingdom 01:11:00\n",
       "18 Males   Austria        00:53:00\n",
       "19 Males   Belgium        01:02:00\n",
       "20 Males   Germany        01:22:00\n",
       "21 Males   Estonia        01:07:00\n",
       "22 Males   Greece         00:59:00\n",
       "23 Males   Spain          01:07:00\n",
       "24 Males   Finland        00:51:00\n",
       "25 Males   France         01:02:00\n",
       "26 Males   Hungary        00:51:00\n",
       "27 Males   Italy          00:47:00\n",
       "28 Males   Luxembourg     00:45:00\n",
       "29 Males   Netherlands    00:43:00\n",
       "30 Males   Norway         00:52:00\n",
       "31 Males   Poland         00:56:00\n",
       "32 Males   Romania        01:11:00\n",
       "33 Males   Serbia         01:11:00\n",
       "34 Males   United Kingdom 01:01:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))][geo!=\"Turkey\"]\n",
    "dt<-dt[,c(\"sex\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(sex=c(\"Males\",\"Males\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Females\",sex)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "sex_ord<-sort(unique(dt$sex),decreasing=TRUE)\n",
    "dt$sex<-factor(dt$sex,levels=sex_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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fUhd383YDWUEonkh9V3dISPubvGXdMTJjVPThGrc3Iy3jMHjTau7fQe6avNjZkp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\nFFPNz4g5JYIJv3OwT8PitbqOvcIornkq9/j9WdCpBh5an2IlZz9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4szo8laKonLQXk57FKz+vMmWWQOnRwry6ajSd+8f9GO7hb868wtwM\nGFxaR2CBs4L0jKh4+gkGIV11m2m2FETdEvkvVshMvGXClMBXblaz3m8Z7x9xqdDj+quzrUo4\n6NiFo0ENvJibO65zfVH6ypcJzM3xFVzVw5CLvKg165KvJwY+2CPxeTJz07aIYTfiG01s4wKB\nyxUW6aFwmlDDQ/k3rcj+5fB7jjs+nb/CiK9TeTWFNo8XnjEici4ci0+2leX/nENP/pHF+TEp\nlCKjd6vmTb7o0HcPiRSKX//WrMZg+togjju+3vhvKINHaUcCqTOxkP0hzM2Ok2pz3DH4iMGv\ndjMV8RSA/WPPcQ1KZ41e/Yr5QcPhrNkB8zpxAUOaMjefLpjLfd8HM/5ibvo2/4GfNIEhxNwS\n8fizEvNh6iW19B7pm/8kxtysqOGcJ5RTI9bt0ndxt5B1jnvlBcUZg88XS1ZQFLV66j3lJxa2\nZZfU8NSw59dBPI2akEiXcBK5KsKWWjy1HLnMyUl/2ZChRYffuUQrtym3dnpl5idRz418/COh\nBBA9dyTKv5n2/QpTY2oc4Ztgonliji1mzTlDmJurFj2jKOrj8bFpuZ8npiRSm78Ha31LujDM\nrqt2YvQRjiEVWZHDTrHK5KgWvjrCC58VRGdExdNPMAjpqttMs6UgfFp6K//OTL6iIyQAOTF3\nJ89hPJXExq3xrTs7K/P0dKVKExswNx9M4fRgkk+hKy8n5r8AwtajV10HDekhF3lbV2vm5rZ7\nsVxiVloTzLoIuFp5gZ7KI6pxgfDlCov0UDjVmc162NTN8b9ncugeKbKjR23iOiRjesh+74Xm\nyHPif9r5xojIuZBZ+s0o56LczEg8P/ySnss8lSKujdlz5vylL8LLlSOTRrGr+Fs35ubTBb8m\n5XLqU8/f+Fr5t0Rq8XMRUbzem19p4awBSXl7C20hmRTZUT/ru9xYPMRTACIuj7iaxOlpQm/3\nDLiRnN85k1l6zQxkXUFpXifOudQ0f6v8yYu06F0z73G6myEnPWjw9tfMTzpNrshz4oADMbdE\nPP6sxHyYXAwYzfrS80PHJHOr6/YMma03jJB1jkTm+Ne3+U+V/BSx7tDr/UtDU5SfFO+84mt+\nooZ4GjWBES3hJHJVhC21eGo5cpkjs/C8ce3a1S/On1wcw+1Rao6BrEU4mlsBECwBRM8difJv\nvn2/QtOYGkf4Jphonphji+lYbHxzl/wp+Ddb/6Aoasvk28pPPKouqs4tl8gxu65azP0xO0M/\ncQl5c1ancMaz8e28+ndxs9ERXvisIDojKp5+gqGIVt3mmy1G826W/0ST7E8PU4ztFAEUxKb+\na5mbY0/+w3wRQwF5VFvszXhWfMLL6ecYC+TaXB2/hLlZ7ueJAkferHdx5ub1CVv0RquU/Hbl\n8bj8RS6pzHasHw/3jnMhqnGB8OUKi/RQOHnWXFKJ8Zqu1Ig93Ta+0LvXrT/b3+H2ZkErD9ZF\nSXdm63+wz5XfWwelE3wAXZ9lbZmbu7r2DErX/0grOjd5RPcdyk2JRPLzTya+4NpUnEvPaMp4\ngGRGwrl28+/o3Svq+sT9sflTBs6lppXjr9YWD7vidszNp584leSjY1t+zOT6XDWTE08ByM2O\n69V1ld5gWcn32w4+wPykSIsV3hasZt28TpxE7rqynT/zk4Xth3KZIzswtP2rtPxDs3JqODtQ\nw7OkQACibYl4/FlRIj5MLsr8NN+K8aTHtOijrf64pHevmDtzh5wM0RtM4Dqn4QLWc0GH9B7K\nfBbuyLlcb7MrlMTTqAmMaAknkavibKlFUsuRyxyJ3JX5nkJakT76MqeZ9Nd7PjA3/Sq7aAtp\nqgSQO3ckyr/59v0KU2NqHIGbYKJ5Yp4tpnTOoPxHPaXHn1gZdHrO2/xHa3Re1U3TXkIzr64a\nrcgc3WZahr4rplLe7W87l1VC6s6ZqjdygbOC9IyoSPoJhiJadVNmmy1Gcwr4Ufk3TefsjdV/\n9xoAv3LSnk4Pyn/2u51X/zm1+HwqidTCY3X7/ItRaDp36LD9unfJiD838NB75aZEarVgdKDA\nkZcbNZa5Gft42qSLEbpj/kyR/ns71vO93CrP87EUaPlYPOMCk5QrLNJD4SSROW6fxurmnhxe\nd8k1Xa/3CDk1vcnsuxzjt/VhVYIfjvf/N1pXdyTs3II2C7lGbhy/puvbuudfvZuRcLVR6/Eh\njMt7NaBz1gysdTgqf2zp9s2sH7wEeteI6Ejka5awnqxy/ffG046/17FHduqzvt/9zfyk4bx+\n6sEyE882ZOsx+T4fKRaORwMP5uYxDtMW5xd/33X1E5UPE7jdJGS09MwCxE+sABgh7Pwvzacc\n1REgJ+1Fr6pNmL0QicRi9oa2KsGEP3EFOgUU1XzNQuawOTXyUJVOs3RPlFxb0qvX9mDmJ40W\nrrcQ0S0ZnBQw38RDzC0RXz8rStyHqZeVU5N1rVgjn1t/Nv9p40MdyeFzfgAAIABJREFUu6S8\nO1Sv0TTmTKI2Atc5bpXmBTAmxOPu5r+H1da92xh/ga74FikxNWpCIlrCSeSqOLtYIqnliGbO\nSPbrG4/2GxGv7/1l6dFnBux+q9yUSKRjA4x//COhBBA8d2RqFTPt+xWmxtQ4AjfBZPNEqBaT\n3w5/hf+xbp77vWff3C+ly9K+ypIaHpp24gf3A+GrnKjPqHQadpN7grmLf7qkxo/rdBxeWuSF\nplX7JTGqayvHOjv7B+iNWeCfDOkZUZH0EwxFtpco1mwh9/Oxdm1fgXEtyKEXCToCA5AoipFX\nJ2Uq8n+eJfuPKmCE6lqumm0hye9lvtv7/ewb0VpD01l/tOwTlZX/q/drtqaZs5XAkdt6DZhb\nl/XW80Vtam97oOeh97QidfmAGssZL7yXSKS/bed/QkBHL0Ik4wKTlCuKBuCdIkOl5J2IT+f3\nGzISLzDjrzHvkYZU5CR382M9cExm4TF62fFshXrQrIMLhznLP1+zIpWx9rqUmKEeeW5mmDJ8\nHnu/tpdDP2lKrOLS5qnKwNZe+ddoO5f8y+ij0yj2/iK5hFUPOZVpv+NisMbAUU/OjmnLGktI\npNZrghI5fldBnKzjw/zeKe+TxBK5InNYIOt+FKnM4cc/d37KVS80dMLLk+3LsObFbD2+0xgy\nNXonxVai0wWjD1CFD+MBKRRFrQjXWAgL6lM46w5Uqcxh6cUQbYHTIu9P6F6D0qTOovsadzG6\nzMcH/cjcse6q59pCcvoKAgWAy/eqnESlWr0mP0/IVA//9PjfNdWGbd+MOqkekvSJo3k/BTR9\nYWJNlW/3bzjgwptk9ZA56R/m/dRcyq70nMsOScstUAI0JKlTCeaOGRqKg8H4zbecjHfMMBXH\n3tKbgKT3U5i7tLsZqXeXoC0NmbvsjE7VGEwkLRG5nxXpwzS6oHKXlXKnpDXrZi+JRNpsyNx3\nKVlqYXP/2zy1GCOwhHHUtZc+VQktQJ2j4mj7YppP9KInBuQIZ1zOjoiqGhM1ahoxu2dSuYvG\nMFwiT/44ixmm9aUw9TDkSjhN85+rou1iiaEyJ5o5kdd/Ugnm13j0nXdaBg6KjGuHVlVjv9/R\n/ZtZBTk6cgkgeO7IjJXMtO9nvo0pXz0NIZtg4h0MMmWb94GSigFerAcMKFX4+RqX3bl/L/cD\nUcdLOVGfUfGpo7WXzpXavKVSyRbD70akqYXP/m/rtBJqz0v49YKGrohGAvdaic6I0qIcDZm2\n6hZtthD5+XyxvqK7Mtpyg/XXPCYfShgduUHEMz8jnpTQZIri5V6sX5B98bIVC+Bteo7Gb9nb\nuxTzW2SW3n+d09AJyc2KntyBlR6p3OWMvhUxQpGnRR2yk7HWraQyh8FztoakZWsM/+LS3m5V\n3Cm2ou026E48Rwb1IsiNC7gzSbnCIj0QII5FepqmE1+ttZaqXjzjWLTK4AmzN2zddfzUiT3b\nNs6ZOKRK0fx3+8ksvZaf28YMf1tDX42maXpLa3+VmGVWPn1/nbP70KmHL98nxEa8fHxvz6rZ\nHevmB3Mo3uXi1gb5FQffi/Q0TZ8aW5VSU7pWy7HTF23duffE6ZN7tq5f8Ofv/VtVUanFKIpq\n9MdV7l9UEOJdpKfp1MhjHhaqSzs23oF9R01bu2n70ZOn9mxZO/v3Cf061LFgFy2J1GLmjSjN\ncZr/Ij1N5/5YjPUKTIlEVq3ND5uPnLn76HlYdELYm2cXTh5cs3TeT12/tWX0AyydWG28VGbf\nbewfm3bu3rGHNUoxuswnvB7J3NHKqcGuS4/CYxKiw94/unsjJSd/WMbxK3gvAIYu0sut/Vg5\nJndq1HnQ7AVLt+zcs2nN8hm/jaxbRrXbRFGUrVe7mGyN3RCyJ47EKVDkJPdUu5NAIrWu0bLX\nnKWrdu8/curYwU1r/h7Tv52P2hSJzMLjaITm8QD3BKgjsUjPb76JauhFi6MlIvmzInuYAizS\n0zQdvKOveuJlFi5Nu/2UlzMbVy+b9r8hVYuyKhCn0n02V89/zJemySnidY6KuKfj1A9EIrG4\nkqThUoyCM7NFehM1ahoJuUhPEyzhJHJVpF0sWhSVOdHMyR1UnBU5RVESibx6q35zFi7dsGXn\nkROnj+zbuXLJgkm/DK7ko3oVl1TuuE/z9dkGHB25BJA7dyTGSubb9zPTxpSvnoawTTDxDgaJ\nss37QEnF0yUanpEukUj2xagtMGvB8Xu5H4g6XsqJAIv0k6a1YZ9Wm3odfpi1YOmWnbs3rFwy\n9ddBlYvYU2pKdV7D/QsF7rXShGdEafGNhkxddYs0W4gu0r9cV18ZraP/b3rDm3woYXTkBhHP\n/Ix4UkKTKYpTi6r2pQvihZYF7JyM9x3Y1zxJpBYNuo3af/bKy3fh8VEhj+5c3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j8VKav6jz6t3/btNH75aFSW+08KsmWJaVnCcu6VvN2LPVSoeLIjdFdmxQ98mwd8XLtS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\nQw8AQJWa8u2q1KDDgc02+SYFbTqlBovffisNmQBgM+ZNekiDuvFYaVm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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig11_colors<-c(\"#FAA519\",\"#286EB4\") #\"#FCC975\",\"#71A8DF\",\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt, aes(x=geo, y=values,fill=sex)) + theme_minimal() +\n",
    "  geom_bar(position=\"dodge\",stat=\"identity\",width=0.7)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,1/96)) +\n",
    "  scale_fill_manual(values = fig11_colors)+\n",
    "  ggtitle(\"Figure 11c: Participation time per day in childcare as secondary activity, by gender, (hh mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 11d: Participation time per day in household and family care as secondary activity, by gender, (hh mm; 2008 to 2015)\n",
    "\n",
    "The data is in a different dataset then in the previous figures, this the data is in the *tus_00educ2*. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^house\",sex=\"male\",isced97=\"^all\")`. This time again we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility, because we need the time values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>isced97</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Austria                                         </td><td>2010</td><td>0:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Belgium                                         </td><td>2010</td><td>0:55</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>0:53</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Estonia                                         </td><td>2010</td><td>1:06</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Greece                                          </td><td>2010</td><td>0:51</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Spain                                           </td><td>2010</td><td>0:48</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Finland                                         </td><td>2010</td><td>0:44</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>France                                          </td><td>2010</td><td>1:17</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Hungary                                         </td><td>2010</td><td>1:06</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Italy                                           </td><td>2010</td><td>0:39</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Luxembourg                                      </td><td>2010</td><td>0:48</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Netherlands                                     </td><td>2010</td><td>0:39</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Norway                                          </td><td>2010</td><td>0:49</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Poland                                          </td><td>2010</td><td>0:37</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Romania                                         </td><td>2010</td><td>0:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Serbia                                          </td><td>2010</td><td>0:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>United Kingdom                                  </td><td>2010</td><td>0:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Austria                                         </td><td>2010</td><td>0:33</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Belgium                                         </td><td>2010</td><td>0:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>0:37</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Estonia                                         </td><td>2010</td><td>0:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Greece                                          </td><td>2010</td><td>0:34</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Spain                                           </td><td>2010</td><td>0:47</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Finland                                         </td><td>2010</td><td>0:23</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>France                                          </td><td>2010</td><td>1:08</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Hungary                                         </td><td>2010</td><td>0:45</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Italy                                           </td><td>2010</td><td>0:27</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Luxembourg                                      </td><td>2010</td><td>0:45</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Netherlands                                     </td><td>2010</td><td>0:32</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Norway                                          </td><td>2010</td><td>0:26</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Poland                                          </td><td>2010</td><td>0:33</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Romania                                         </td><td>2010</td><td>0:44</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Serbia                                          </td><td>2010</td><td>0:37</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>Turkey                                          </td><td>2010</td><td>0:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>All ISCED 1997 levels</td><td>Household and family care, except childcare</td><td>United Kingdom                                  </td><td>2010</td><td>0:35</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " unit & sex & isced97 & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Austria                                          & 2010 & 0:47\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Belgium                                          & 2010 & 0:55\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Germany (until 1990 former territory of the FRG) & 2010 & 0:53\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Estonia                                          & 2010 & 1:06\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Greece                                           & 2010 & 0:51\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Spain                                            & 2010 & 0:48\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Finland                                          & 2010 & 0:44\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & France                                           & 2010 & 1:17\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Hungary                                          & 2010 & 1:06\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Italy                                            & 2010 & 0:39\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Luxembourg                                       & 2010 & 0:48\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Netherlands                                      & 2010 & 0:39\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Norway                                           & 2010 & 0:49\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Poland                                           & 2010 & 0:37\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Romania                                          & 2010 & 0:46\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Serbia                                           & 2010 & 0:47\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Females & All ISCED 1997 levels & Household and family care, except childcare & United Kingdom                                   & 2010 & 0:50\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Austria                                          & 2010 & 0:33\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Belgium                                          & 2010 & 0:47\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Germany (until 1990 former territory of the FRG) & 2010 & 0:37\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Estonia                                          & 2010 & 0:46\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Greece                                           & 2010 & 0:34\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Spain                                            & 2010 & 0:47\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Finland                                          & 2010 & 0:23\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & France                                           & 2010 & 1:08\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Hungary                                          & 2010 & 0:45\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Italy                                            & 2010 & 0:27\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Luxembourg                                       & 2010 & 0:45\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Netherlands                                      & 2010 & 0:32\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Norway                                           & 2010 & 0:26\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Poland                                           & 2010 & 0:33\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Romania                                          & 2010 & 0:44\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Serbia                                           & 2010 & 0:37\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & Turkey                                           & 2010 & 0:00\\\\\n",
       "\t Participation time (hh:mm) & Males   & All ISCED 1997 levels & Household and family care, except childcare & United Kingdom                                   & 2010 & 0:35\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 7\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | isced97 &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Austria                                          | 2010 | 0:47 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Belgium                                          | 2010 | 0:55 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Germany (until 1990 former territory of the FRG) | 2010 | 0:53 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Estonia                                          | 2010 | 1:06 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Greece                                           | 2010 | 0:51 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Spain                                            | 2010 | 0:48 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Finland                                          | 2010 | 0:44 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | France                                           | 2010 | 1:17 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Hungary                                          | 2010 | 1:06 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Italy                                            | 2010 | 0:39 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Luxembourg                                       | 2010 | 0:48 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Netherlands                                      | 2010 | 0:39 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Norway                                           | 2010 | 0:49 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Poland                                           | 2010 | 0:37 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Romania                                          | 2010 | 0:46 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Serbia                                           | 2010 | 0:47 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Females | All ISCED 1997 levels | Household and family care, except childcare | United Kingdom                                   | 2010 | 0:50 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Austria                                          | 2010 | 0:33 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Belgium                                          | 2010 | 0:47 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Germany (until 1990 former territory of the FRG) | 2010 | 0:37 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Estonia                                          | 2010 | 0:46 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Greece                                           | 2010 | 0:34 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Spain                                            | 2010 | 0:47 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Finland                                          | 2010 | 0:23 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | France                                           | 2010 | 1:08 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Hungary                                          | 2010 | 0:45 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Italy                                            | 2010 | 0:27 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Luxembourg                                       | 2010 | 0:45 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Netherlands                                      | 2010 | 0:32 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Norway                                           | 2010 | 0:26 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Poland                                           | 2010 | 0:33 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Romania                                          | 2010 | 0:44 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Serbia                                           | 2010 | 0:37 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | Turkey                                           | 2010 | 0:00 |\n",
       "| Participation time (hh:mm) | Males   | All ISCED 1997 levels | Household and family care, except childcare | United Kingdom                                   | 2010 | 0:35 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                       sex     isced97              \n",
       "1  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "2  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "3  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "4  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "5  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "6  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "7  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "8  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "9  Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "10 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "11 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "12 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "13 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "14 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "15 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "16 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "17 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "18 Participation time (hh:mm) Females All ISCED 1997 levels\n",
       "19 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "20 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "21 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "22 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "23 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "24 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "25 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "26 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "27 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "28 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "29 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "30 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "31 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "32 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "33 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "34 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "35 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "36 Participation time (hh:mm) Males   All ISCED 1997 levels\n",
       "   acl00                                      \n",
       "1  Household and family care, except childcare\n",
       "2  Household and family care, except childcare\n",
       "3  Household and family care, except childcare\n",
       "4  Household and family care, except childcare\n",
       "5  Household and family care, except childcare\n",
       "6  Household and family care, except childcare\n",
       "7  Household and family care, except childcare\n",
       "8  Household and family care, except childcare\n",
       "9  Household and family care, except childcare\n",
       "10 Household and family care, except childcare\n",
       "11 Household and family care, except childcare\n",
       "12 Household and family care, except childcare\n",
       "13 Household and family care, except childcare\n",
       "14 Household and family care, except childcare\n",
       "15 Household and family care, except childcare\n",
       "16 Household and family care, except childcare\n",
       "17 Household and family care, except childcare\n",
       "18 Household and family care, except childcare\n",
       "19 Household and family care, except childcare\n",
       "20 Household and family care, except childcare\n",
       "21 Household and family care, except childcare\n",
       "22 Household and family care, except childcare\n",
       "23 Household and family care, except childcare\n",
       "24 Household and family care, except childcare\n",
       "25 Household and family care, except childcare\n",
       "26 Household and family care, except childcare\n",
       "27 Household and family care, except childcare\n",
       "28 Household and family care, except childcare\n",
       "29 Household and family care, except childcare\n",
       "30 Household and family care, except childcare\n",
       "31 Household and family care, except childcare\n",
       "32 Household and family care, except childcare\n",
       "33 Household and family care, except childcare\n",
       "34 Household and family care, except childcare\n",
       "35 Household and family care, except childcare\n",
       "36 Household and family care, except childcare\n",
       "   geo                                              time values\n",
       "1  Austria                                          2010 0:47  \n",
       "2  Belgium                                          2010 0:55  \n",
       "3  Germany (until 1990 former territory of the FRG) 2010 0:53  \n",
       "4  Estonia                                          2010 1:06  \n",
       "5  Greece                                           2010 0:51  \n",
       "6  Spain                                            2010 0:48  \n",
       "7  Finland                                          2010 0:44  \n",
       "8  France                                           2010 1:17  \n",
       "9  Hungary                                          2010 1:06  \n",
       "10 Italy                                            2010 0:39  \n",
       "11 Luxembourg                                       2010 0:48  \n",
       "12 Netherlands                                      2010 0:39  \n",
       "13 Norway                                           2010 0:49  \n",
       "14 Poland                                           2010 0:37  \n",
       "15 Romania                                          2010 0:46  \n",
       "16 Serbia                                           2010 0:47  \n",
       "17 Turkey                                           2010 0:00  \n",
       "18 United Kingdom                                   2010 0:50  \n",
       "19 Austria                                          2010 0:33  \n",
       "20 Belgium                                          2010 0:47  \n",
       "21 Germany (until 1990 former territory of the FRG) 2010 0:37  \n",
       "22 Estonia                                          2010 0:46  \n",
       "23 Greece                                           2010 0:34  \n",
       "24 Spain                                            2010 0:47  \n",
       "25 Finland                                          2010 0:23  \n",
       "26 France                                           2010 1:08  \n",
       "27 Hungary                                          2010 0:45  \n",
       "28 Italy                                            2010 0:27  \n",
       "29 Luxembourg                                       2010 0:45  \n",
       "30 Netherlands                                      2010 0:32  \n",
       "31 Norway                                           2010 0:26  \n",
       "32 Poland                                           2010 0:33  \n",
       "33 Romania                                          2010 0:44  \n",
       "34 Serbia                                           2010 0:37  \n",
       "35 Turkey                                           2010 0:00  \n",
       "36 United Kingdom                                   2010 0:35  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00educ2\",filters=list(unit=\"Participation time\",age=\"total\",acl00=\"^house\",sex=\"male\",isced97=\"^all\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we convert the values from characters/factors to time values using the *chron* package and keep only the columns with activities, countries and values. We drop the values for Turkey as it is 0. Before plotting the values we need to cut the brackets from the name of Germany. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 34 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Females</td><td>Austria       </td><td>00:47:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Belgium       </td><td>00:55:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Germany       </td><td>00:53:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Estonia       </td><td>01:06:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Greece        </td><td>00:51:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Spain         </td><td>00:48:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Finland       </td><td>00:44:00</td></tr>\n",
       "\t<tr><td>Females</td><td>France        </td><td>01:17:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Hungary       </td><td>01:06:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Italy         </td><td>00:39:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Luxembourg    </td><td>00:48:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Netherlands   </td><td>00:39:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Norway        </td><td>00:49:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Poland        </td><td>00:37:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Romania       </td><td>00:46:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Serbia        </td><td>00:47:00</td></tr>\n",
       "\t<tr><td>Females</td><td>United Kingdom</td><td>00:50:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Austria       </td><td>00:33:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Belgium       </td><td>00:47:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Germany       </td><td>00:37:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Estonia       </td><td>00:46:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Greece        </td><td>00:34:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Spain         </td><td>00:47:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Finland       </td><td>00:23:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>France        </td><td>01:08:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Hungary       </td><td>00:45:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Italy         </td><td>00:27:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Luxembourg    </td><td>00:45:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Netherlands   </td><td>00:32:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Norway        </td><td>00:26:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Poland        </td><td>00:33:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Romania       </td><td>00:44:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Serbia        </td><td>00:37:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>United Kingdom</td><td>00:35:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 34 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Females & Austria        & 00:47:00\\\\\n",
       "\t Females & Belgium        & 00:55:00\\\\\n",
       "\t Females & Germany        & 00:53:00\\\\\n",
       "\t Females & Estonia        & 01:06:00\\\\\n",
       "\t Females & Greece         & 00:51:00\\\\\n",
       "\t Females & Spain          & 00:48:00\\\\\n",
       "\t Females & Finland        & 00:44:00\\\\\n",
       "\t Females & France         & 01:17:00\\\\\n",
       "\t Females & Hungary        & 01:06:00\\\\\n",
       "\t Females & Italy          & 00:39:00\\\\\n",
       "\t Females & Luxembourg     & 00:48:00\\\\\n",
       "\t Females & Netherlands    & 00:39:00\\\\\n",
       "\t Females & Norway         & 00:49:00\\\\\n",
       "\t Females & Poland         & 00:37:00\\\\\n",
       "\t Females & Romania        & 00:46:00\\\\\n",
       "\t Females & Serbia         & 00:47:00\\\\\n",
       "\t Females & United Kingdom & 00:50:00\\\\\n",
       "\t Males   & Austria        & 00:33:00\\\\\n",
       "\t Males   & Belgium        & 00:47:00\\\\\n",
       "\t Males   & Germany        & 00:37:00\\\\\n",
       "\t Males   & Estonia        & 00:46:00\\\\\n",
       "\t Males   & Greece         & 00:34:00\\\\\n",
       "\t Males   & Spain          & 00:47:00\\\\\n",
       "\t Males   & Finland        & 00:23:00\\\\\n",
       "\t Males   & France         & 01:08:00\\\\\n",
       "\t Males   & Hungary        & 00:45:00\\\\\n",
       "\t Males   & Italy          & 00:27:00\\\\\n",
       "\t Males   & Luxembourg     & 00:45:00\\\\\n",
       "\t Males   & Netherlands    & 00:32:00\\\\\n",
       "\t Males   & Norway         & 00:26:00\\\\\n",
       "\t Males   & Poland         & 00:33:00\\\\\n",
       "\t Males   & Romania        & 00:44:00\\\\\n",
       "\t Males   & Serbia         & 00:37:00\\\\\n",
       "\t Males   & United Kingdom & 00:35:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 34 × 3\n",
       "\n",
       "| sex &lt;chr&gt; | geo &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Females | Austria        | 00:47:00 |\n",
       "| Females | Belgium        | 00:55:00 |\n",
       "| Females | Germany        | 00:53:00 |\n",
       "| Females | Estonia        | 01:06:00 |\n",
       "| Females | Greece         | 00:51:00 |\n",
       "| Females | Spain          | 00:48:00 |\n",
       "| Females | Finland        | 00:44:00 |\n",
       "| Females | France         | 01:17:00 |\n",
       "| Females | Hungary        | 01:06:00 |\n",
       "| Females | Italy          | 00:39:00 |\n",
       "| Females | Luxembourg     | 00:48:00 |\n",
       "| Females | Netherlands    | 00:39:00 |\n",
       "| Females | Norway         | 00:49:00 |\n",
       "| Females | Poland         | 00:37:00 |\n",
       "| Females | Romania        | 00:46:00 |\n",
       "| Females | Serbia         | 00:47:00 |\n",
       "| Females | United Kingdom | 00:50:00 |\n",
       "| Males   | Austria        | 00:33:00 |\n",
       "| Males   | Belgium        | 00:47:00 |\n",
       "| Males   | Germany        | 00:37:00 |\n",
       "| Males   | Estonia        | 00:46:00 |\n",
       "| Males   | Greece         | 00:34:00 |\n",
       "| Males   | Spain          | 00:47:00 |\n",
       "| Males   | Finland        | 00:23:00 |\n",
       "| Males   | France         | 01:08:00 |\n",
       "| Males   | Hungary        | 00:45:00 |\n",
       "| Males   | Italy          | 00:27:00 |\n",
       "| Males   | Luxembourg     | 00:45:00 |\n",
       "| Males   | Netherlands    | 00:32:00 |\n",
       "| Males   | Norway         | 00:26:00 |\n",
       "| Males   | Poland         | 00:33:00 |\n",
       "| Males   | Romania        | 00:44:00 |\n",
       "| Males   | Serbia         | 00:37:00 |\n",
       "| Males   | United Kingdom | 00:35:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   sex     geo            values  \n",
       "1  Females Austria        00:47:00\n",
       "2  Females Belgium        00:55:00\n",
       "3  Females Germany        00:53:00\n",
       "4  Females Estonia        01:06:00\n",
       "5  Females Greece         00:51:00\n",
       "6  Females Spain          00:48:00\n",
       "7  Females Finland        00:44:00\n",
       "8  Females France         01:17:00\n",
       "9  Females Hungary        01:06:00\n",
       "10 Females Italy          00:39:00\n",
       "11 Females Luxembourg     00:48:00\n",
       "12 Females Netherlands    00:39:00\n",
       "13 Females Norway         00:49:00\n",
       "14 Females Poland         00:37:00\n",
       "15 Females Romania        00:46:00\n",
       "16 Females Serbia         00:47:00\n",
       "17 Females United Kingdom 00:50:00\n",
       "18 Males   Austria        00:33:00\n",
       "19 Males   Belgium        00:47:00\n",
       "20 Males   Germany        00:37:00\n",
       "21 Males   Estonia        00:46:00\n",
       "22 Males   Greece         00:34:00\n",
       "23 Males   Spain          00:47:00\n",
       "24 Males   Finland        00:23:00\n",
       "25 Males   France         01:08:00\n",
       "26 Males   Hungary        00:45:00\n",
       "27 Males   Italy          00:27:00\n",
       "28 Males   Luxembourg     00:45:00\n",
       "29 Males   Netherlands    00:32:00\n",
       "30 Males   Norway         00:26:00\n",
       "31 Males   Poland         00:33:00\n",
       "32 Males   Romania        00:44:00\n",
       "33 Males   Serbia         00:37:00\n",
       "34 Males   United Kingdom 00:35:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))][geo!=\"Turkey\"]\n",
    "dt<-dt[,c(\"sex\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(sex=c(\"Males\",\"Males\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Females\",sex)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "sex_ord<-sort(unique(dt$sex),decreasing=TRUE)\n",
    "dt$sex<-factor(dt$sex,levels=sex_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
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lQG+VuejMUbEd9I4xGmIR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\nrl1+f2KNyz7weu+cOrd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NTY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\nTxOYuK3YeuDakRUrD98ioifHFj/pe7CuMT6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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig11_colors<-c(\"#FAA519\",\"#286EB4\") #\"#FCC975\",\"#71A8DF\",\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt, aes(x=geo, y=values,fill=sex)) + theme_minimal() +\n",
    "  geom_bar(position=\"dodge\",stat=\"identity\",width=0.7)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,1/96)) +\n",
    "  scale_fill_manual(values = fig11_colors)+\n",
    "  ggtitle(\"Figure 11d: Participation time per day in household and family care as secondary activity, by gender, (hh mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 12: Participation time per day in unpaid work (main activity), by gender, (hh mm; 2008 to 2015)\n",
    "\n",
    "The data is this time in the *tus_00npaywork* dataset. We use the same method as for Figure 3. We apply the same filter to the data for the year (`date_filter=yr`) and a modified one for the values in the graph (`filters=list(unit=\"Participation time\",age=\"total\",acl00=\"main\",sex=\"male\")`. Again in order to get the data we have to apply the filter locally (`force_local_filter=T`) on the dataset retrieved from the bulk download facility. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Forcing to apply filter locally. The whole dataset is downloaded through the raw download and the filters are applied locally.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>acl00</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Austria                                         </td><td>2010</td><td>4:39</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Belgium                                         </td><td>2010</td><td>4:08</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>4:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Estonia                                         </td><td>2010</td><td>4:14</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Greece                                          </td><td>2010</td><td>4:43</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Spain                                           </td><td>2010</td><td>4:57</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Finland                                         </td><td>2010</td><td>3:55</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>France                                          </td><td>2010</td><td>4:13</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Hungary                                         </td><td>2010</td><td>4:57</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Italy                                           </td><td>2010</td><td>5:30</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Luxembourg                                      </td><td>2010</td><td>4:00</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Netherlands                                     </td><td>2010</td><td>3:43</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Norway                                          </td><td>2010</td><td>3:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Poland                                          </td><td>2010</td><td>4:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Romania                                         </td><td>2010</td><td>5:14</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Serbia                                          </td><td>2010</td><td>5:08</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>Turkey                                          </td><td>2010</td><td>5:22</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Females</td><td>Productive unpaid main activities</td><td>United Kingdom                                  </td><td>2010</td><td>4:01</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Austria                                         </td><td>2010</td><td>2:54</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Belgium                                         </td><td>2010</td><td>2:50</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Germany (until 1990 former territory of the FRG)</td><td>2010</td><td>2:45</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Estonia                                         </td><td>2010</td><td>3:03</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Greece                                          </td><td>2010</td><td>2:13</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Spain                                           </td><td>2010</td><td>2:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Finland                                         </td><td>2010</td><td>2:44</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>France                                          </td><td>2010</td><td>2:55</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Hungary                                         </td><td>2010</td><td>3:04</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Italy                                           </td><td>2010</td><td>2:27</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Luxembourg                                      </td><td>2010</td><td>2:20</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Netherlands                                     </td><td>2010</td><td>2:39</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Norway                                          </td><td>2010</td><td>3:01</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Poland                                          </td><td>2010</td><td>2:54</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Romania                                         </td><td>2010</td><td>2:48</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Serbia                                          </td><td>2010</td><td>2:46</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>Turkey                                          </td><td>2010</td><td>1:49</td></tr>\n",
       "\t<tr><td>Participation time (hh:mm)</td><td>Males  </td><td>Productive unpaid main activities</td><td>United Kingdom                                  </td><td>2010</td><td>2:26</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " unit & sex & acl00 & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Austria                                          & 2010 & 4:39\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Belgium                                          & 2010 & 4:08\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Germany (until 1990 former territory of the FRG) & 2010 & 4:00\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Estonia                                          & 2010 & 4:14\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Greece                                           & 2010 & 4:43\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Spain                                            & 2010 & 4:57\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Finland                                          & 2010 & 3:55\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & France                                           & 2010 & 4:13\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Hungary                                          & 2010 & 4:57\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Italy                                            & 2010 & 5:30\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Luxembourg                                       & 2010 & 4:00\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Netherlands                                      & 2010 & 3:43\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Norway                                           & 2010 & 3:46\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Poland                                           & 2010 & 4:50\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Romania                                          & 2010 & 5:14\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Serbia                                           & 2010 & 5:08\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & Turkey                                           & 2010 & 5:22\\\\\n",
       "\t Participation time (hh:mm) & Females & Productive unpaid main activities & United Kingdom                                   & 2010 & 4:01\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Austria                                          & 2010 & 2:54\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Belgium                                          & 2010 & 2:50\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Germany (until 1990 former territory of the FRG) & 2010 & 2:45\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Estonia                                          & 2010 & 3:03\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Greece                                           & 2010 & 2:13\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Spain                                            & 2010 & 2:46\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Finland                                          & 2010 & 2:44\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & France                                           & 2010 & 2:55\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Hungary                                          & 2010 & 3:04\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Italy                                            & 2010 & 2:27\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Luxembourg                                       & 2010 & 2:20\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Netherlands                                      & 2010 & 2:39\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Norway                                           & 2010 & 3:01\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Poland                                           & 2010 & 2:54\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Romania                                          & 2010 & 2:48\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Serbia                                           & 2010 & 2:46\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & Turkey                                           & 2010 & 1:49\\\\\n",
       "\t Participation time (hh:mm) & Males   & Productive unpaid main activities & United Kingdom                                   & 2010 & 2:26\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 6\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | acl00 &lt;chr&gt; | geo &lt;chr&gt; | time &lt;chr&gt; | values &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Austria                                          | 2010 | 4:39 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Belgium                                          | 2010 | 4:08 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Germany (until 1990 former territory of the FRG) | 2010 | 4:00 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Estonia                                          | 2010 | 4:14 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Greece                                           | 2010 | 4:43 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Spain                                            | 2010 | 4:57 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Finland                                          | 2010 | 3:55 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | France                                           | 2010 | 4:13 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Hungary                                          | 2010 | 4:57 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Italy                                            | 2010 | 5:30 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Luxembourg                                       | 2010 | 4:00 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Netherlands                                      | 2010 | 3:43 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Norway                                           | 2010 | 3:46 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Poland                                           | 2010 | 4:50 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Romania                                          | 2010 | 5:14 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Serbia                                           | 2010 | 5:08 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | Turkey                                           | 2010 | 5:22 |\n",
       "| Participation time (hh:mm) | Females | Productive unpaid main activities | United Kingdom                                   | 2010 | 4:01 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Austria                                          | 2010 | 2:54 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Belgium                                          | 2010 | 2:50 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Germany (until 1990 former territory of the FRG) | 2010 | 2:45 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Estonia                                          | 2010 | 3:03 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Greece                                           | 2010 | 2:13 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Spain                                            | 2010 | 2:46 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Finland                                          | 2010 | 2:44 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | France                                           | 2010 | 2:55 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Hungary                                          | 2010 | 3:04 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Italy                                            | 2010 | 2:27 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Luxembourg                                       | 2010 | 2:20 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Netherlands                                      | 2010 | 2:39 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Norway                                           | 2010 | 3:01 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Poland                                           | 2010 | 2:54 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Romania                                          | 2010 | 2:48 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Serbia                                           | 2010 | 2:46 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | Turkey                                           | 2010 | 1:49 |\n",
       "| Participation time (hh:mm) | Males   | Productive unpaid main activities | United Kingdom                                   | 2010 | 2:26 |\n",
       "\n"
      ],
      "text/plain": [
       "   unit                       sex     acl00                            \n",
       "1  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "2  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "3  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "4  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "5  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "6  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "7  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "8  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "9  Participation time (hh:mm) Females Productive unpaid main activities\n",
       "10 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "11 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "12 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "13 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "14 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "15 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "16 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "17 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "18 Participation time (hh:mm) Females Productive unpaid main activities\n",
       "19 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "20 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "21 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "22 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "23 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "24 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "25 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "26 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "27 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "28 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "29 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "30 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "31 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "32 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "33 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "34 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "35 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "36 Participation time (hh:mm) Males   Productive unpaid main activities\n",
       "   geo                                              time values\n",
       "1  Austria                                          2010 4:39  \n",
       "2  Belgium                                          2010 4:08  \n",
       "3  Germany (until 1990 former territory of the FRG) 2010 4:00  \n",
       "4  Estonia                                          2010 4:14  \n",
       "5  Greece                                           2010 4:43  \n",
       "6  Spain                                            2010 4:57  \n",
       "7  Finland                                          2010 3:55  \n",
       "8  France                                           2010 4:13  \n",
       "9  Hungary                                          2010 4:57  \n",
       "10 Italy                                            2010 5:30  \n",
       "11 Luxembourg                                       2010 4:00  \n",
       "12 Netherlands                                      2010 3:43  \n",
       "13 Norway                                           2010 3:46  \n",
       "14 Poland                                           2010 4:50  \n",
       "15 Romania                                          2010 5:14  \n",
       "16 Serbia                                           2010 5:08  \n",
       "17 Turkey                                           2010 5:22  \n",
       "18 United Kingdom                                   2010 4:01  \n",
       "19 Austria                                          2010 2:54  \n",
       "20 Belgium                                          2010 2:50  \n",
       "21 Germany (until 1990 former territory of the FRG) 2010 2:45  \n",
       "22 Estonia                                          2010 3:03  \n",
       "23 Greece                                           2010 2:13  \n",
       "24 Spain                                            2010 2:46  \n",
       "25 Finland                                          2010 2:44  \n",
       "26 France                                           2010 2:55  \n",
       "27 Hungary                                          2010 3:04  \n",
       "28 Italy                                            2010 2:27  \n",
       "29 Luxembourg                                       2010 2:20  \n",
       "30 Netherlands                                      2010 2:39  \n",
       "31 Norway                                           2010 3:01  \n",
       "32 Poland                                           2010 2:54  \n",
       "33 Romania                                          2010 2:48  \n",
       "34 Serbia                                           2010 2:46  \n",
       "35 Turkey                                           2010 1:49  \n",
       "36 United Kingdom                                   2010 2:26  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt<-get_eurostat_data(\"tus_00npaywork\",filters=list(unit=\"Participation time\",age=\"total\",acl00=\"main\",sex=\"male\"),date_filter=eval(yr),label=T,ignore.case=T,exact_match=F,perl=T,stringsAsFactors=F,force_local_filter=T)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then again we convert the values from characters/factors to time values using the *chron* package and keep only the columns with sex, countries and values. Before plotting the values we need to cut the brackets from the name of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 36 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Females</td><td>Austria       </td><td>04:39:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Belgium       </td><td>04:08:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Germany       </td><td>04:00:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Estonia       </td><td>04:14:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Greece        </td><td>04:43:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Spain         </td><td>04:57:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Finland       </td><td>03:55:00</td></tr>\n",
       "\t<tr><td>Females</td><td>France        </td><td>04:13:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Hungary       </td><td>04:57:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Italy         </td><td>05:30:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Luxembourg    </td><td>04:00:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Netherlands   </td><td>03:43:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Norway        </td><td>03:46:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Poland        </td><td>04:50:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Romania       </td><td>05:14:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Serbia        </td><td>05:08:00</td></tr>\n",
       "\t<tr><td>Females</td><td>Turkey        </td><td>05:22:00</td></tr>\n",
       "\t<tr><td>Females</td><td>United Kingdom</td><td>04:01:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Austria       </td><td>02:54:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Belgium       </td><td>02:50:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Germany       </td><td>02:45:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Estonia       </td><td>03:03:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Greece        </td><td>02:13:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Spain         </td><td>02:46:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Finland       </td><td>02:44:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>France        </td><td>02:55:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Hungary       </td><td>03:04:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Italy         </td><td>02:27:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Luxembourg    </td><td>02:20:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Netherlands   </td><td>02:39:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Norway        </td><td>03:01:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Poland        </td><td>02:54:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Romania       </td><td>02:48:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Serbia        </td><td>02:46:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>Turkey        </td><td>01:49:00</td></tr>\n",
       "\t<tr><td>Males  </td><td>United Kingdom</td><td>02:26:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 36 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Females & Austria        & 04:39:00\\\\\n",
       "\t Females & Belgium        & 04:08:00\\\\\n",
       "\t Females & Germany        & 04:00:00\\\\\n",
       "\t Females & Estonia        & 04:14:00\\\\\n",
       "\t Females & Greece         & 04:43:00\\\\\n",
       "\t Females & Spain          & 04:57:00\\\\\n",
       "\t Females & Finland        & 03:55:00\\\\\n",
       "\t Females & France         & 04:13:00\\\\\n",
       "\t Females & Hungary        & 04:57:00\\\\\n",
       "\t Females & Italy          & 05:30:00\\\\\n",
       "\t Females & Luxembourg     & 04:00:00\\\\\n",
       "\t Females & Netherlands    & 03:43:00\\\\\n",
       "\t Females & Norway         & 03:46:00\\\\\n",
       "\t Females & Poland         & 04:50:00\\\\\n",
       "\t Females & Romania        & 05:14:00\\\\\n",
       "\t Females & Serbia         & 05:08:00\\\\\n",
       "\t Females & Turkey         & 05:22:00\\\\\n",
       "\t Females & United Kingdom & 04:01:00\\\\\n",
       "\t Males   & Austria        & 02:54:00\\\\\n",
       "\t Males   & Belgium        & 02:50:00\\\\\n",
       "\t Males   & Germany        & 02:45:00\\\\\n",
       "\t Males   & Estonia        & 03:03:00\\\\\n",
       "\t Males   & Greece         & 02:13:00\\\\\n",
       "\t Males   & Spain          & 02:46:00\\\\\n",
       "\t Males   & Finland        & 02:44:00\\\\\n",
       "\t Males   & France         & 02:55:00\\\\\n",
       "\t Males   & Hungary        & 03:04:00\\\\\n",
       "\t Males   & Italy          & 02:27:00\\\\\n",
       "\t Males   & Luxembourg     & 02:20:00\\\\\n",
       "\t Males   & Netherlands    & 02:39:00\\\\\n",
       "\t Males   & Norway         & 03:01:00\\\\\n",
       "\t Males   & Poland         & 02:54:00\\\\\n",
       "\t Males   & Romania        & 02:48:00\\\\\n",
       "\t Males   & Serbia         & 02:46:00\\\\\n",
       "\t Males   & Turkey         & 01:49:00\\\\\n",
       "\t Males   & United Kingdom & 02:26:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 36 × 3\n",
       "\n",
       "| sex &lt;chr&gt; | geo &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Females | Austria        | 04:39:00 |\n",
       "| Females | Belgium        | 04:08:00 |\n",
       "| Females | Germany        | 04:00:00 |\n",
       "| Females | Estonia        | 04:14:00 |\n",
       "| Females | Greece         | 04:43:00 |\n",
       "| Females | Spain          | 04:57:00 |\n",
       "| Females | Finland        | 03:55:00 |\n",
       "| Females | France         | 04:13:00 |\n",
       "| Females | Hungary        | 04:57:00 |\n",
       "| Females | Italy          | 05:30:00 |\n",
       "| Females | Luxembourg     | 04:00:00 |\n",
       "| Females | Netherlands    | 03:43:00 |\n",
       "| Females | Norway         | 03:46:00 |\n",
       "| Females | Poland         | 04:50:00 |\n",
       "| Females | Romania        | 05:14:00 |\n",
       "| Females | Serbia         | 05:08:00 |\n",
       "| Females | Turkey         | 05:22:00 |\n",
       "| Females | United Kingdom | 04:01:00 |\n",
       "| Males   | Austria        | 02:54:00 |\n",
       "| Males   | Belgium        | 02:50:00 |\n",
       "| Males   | Germany        | 02:45:00 |\n",
       "| Males   | Estonia        | 03:03:00 |\n",
       "| Males   | Greece         | 02:13:00 |\n",
       "| Males   | Spain          | 02:46:00 |\n",
       "| Males   | Finland        | 02:44:00 |\n",
       "| Males   | France         | 02:55:00 |\n",
       "| Males   | Hungary        | 03:04:00 |\n",
       "| Males   | Italy          | 02:27:00 |\n",
       "| Males   | Luxembourg     | 02:20:00 |\n",
       "| Males   | Netherlands    | 02:39:00 |\n",
       "| Males   | Norway         | 03:01:00 |\n",
       "| Males   | Poland         | 02:54:00 |\n",
       "| Males   | Romania        | 02:48:00 |\n",
       "| Males   | Serbia         | 02:46:00 |\n",
       "| Males   | Turkey         | 01:49:00 |\n",
       "| Males   | United Kingdom | 02:26:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   sex     geo            values  \n",
       "1  Females Austria        04:39:00\n",
       "2  Females Belgium        04:08:00\n",
       "3  Females Germany        04:00:00\n",
       "4  Females Estonia        04:14:00\n",
       "5  Females Greece         04:43:00\n",
       "6  Females Spain          04:57:00\n",
       "7  Females Finland        03:55:00\n",
       "8  Females France         04:13:00\n",
       "9  Females Hungary        04:57:00\n",
       "10 Females Italy          05:30:00\n",
       "11 Females Luxembourg     04:00:00\n",
       "12 Females Netherlands    03:43:00\n",
       "13 Females Norway         03:46:00\n",
       "14 Females Poland         04:50:00\n",
       "15 Females Romania        05:14:00\n",
       "16 Females Serbia         05:08:00\n",
       "17 Females Turkey         05:22:00\n",
       "18 Females United Kingdom 04:01:00\n",
       "19 Males   Austria        02:54:00\n",
       "20 Males   Belgium        02:50:00\n",
       "21 Males   Germany        02:45:00\n",
       "22 Males   Estonia        03:03:00\n",
       "23 Males   Greece         02:13:00\n",
       "24 Males   Spain          02:46:00\n",
       "25 Males   Finland        02:44:00\n",
       "26 Males   France         02:55:00\n",
       "27 Males   Hungary        03:04:00\n",
       "28 Males   Italy          02:27:00\n",
       "29 Males   Luxembourg     02:20:00\n",
       "30 Males   Netherlands    02:39:00\n",
       "31 Males   Norway         03:01:00\n",
       "32 Males   Poland         02:54:00\n",
       "33 Males   Romania        02:48:00\n",
       "34 Males   Serbia         02:46:00\n",
       "35 Males   Turkey         01:49:00\n",
       "36 Males   United Kingdom 02:26:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt$geo<-gsub(\" \\\\(.*\\\\)\",\"\",dt$geo)\n",
    "if (is.factor(dt$values)|is.character(dt$values)) dt<-dt[,values:=chron::times(paste0(values,\":00\"))]\n",
    "dt<-dt[,c(\"sex\",\"geo\",\"values\")]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then calculate the gender gap values as females minus males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 54 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>geo</th><th scope=col>sex</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;times&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria       </td><td>Females   </td><td>04:39:00</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>Females   </td><td>04:08:00</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>Females   </td><td>04:14:00</td></tr>\n",
       "\t<tr><td>Finland       </td><td>Females   </td><td>03:55:00</td></tr>\n",
       "\t<tr><td>France        </td><td>Females   </td><td>04:13:00</td></tr>\n",
       "\t<tr><td>Germany       </td><td>Females   </td><td>04:00:00</td></tr>\n",
       "\t<tr><td>Greece        </td><td>Females   </td><td>04:43:00</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>Females   </td><td>04:57:00</td></tr>\n",
       "\t<tr><td>Italy         </td><td>Females   </td><td>05:30:00</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>Females   </td><td>04:00:00</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>Females   </td><td>03:43:00</td></tr>\n",
       "\t<tr><td>Norway        </td><td>Females   </td><td>03:46:00</td></tr>\n",
       "\t<tr><td>Poland        </td><td>Females   </td><td>04:50:00</td></tr>\n",
       "\t<tr><td>Romania       </td><td>Females   </td><td>05:14:00</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>Females   </td><td>05:08:00</td></tr>\n",
       "\t<tr><td>Spain         </td><td>Females   </td><td>04:57:00</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>Females   </td><td>05:22:00</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>Females   </td><td>04:01:00</td></tr>\n",
       "\t<tr><td>Austria       </td><td>Males     </td><td>02:54:00</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>Males     </td><td>02:50:00</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>Males     </td><td>03:03:00</td></tr>\n",
       "\t<tr><td>Finland       </td><td>Males     </td><td>02:44:00</td></tr>\n",
       "\t<tr><td>France        </td><td>Males     </td><td>02:55:00</td></tr>\n",
       "\t<tr><td>Germany       </td><td>Males     </td><td>02:45:00</td></tr>\n",
       "\t<tr><td>Greece        </td><td>Males     </td><td>02:13:00</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>Males     </td><td>03:04:00</td></tr>\n",
       "\t<tr><td>Italy         </td><td>Males     </td><td>02:27:00</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>Males     </td><td>02:20:00</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>Males     </td><td>02:39:00</td></tr>\n",
       "\t<tr><td>Norway        </td><td>Males     </td><td>03:01:00</td></tr>\n",
       "\t<tr><td>Poland        </td><td>Males     </td><td>02:54:00</td></tr>\n",
       "\t<tr><td>Romania       </td><td>Males     </td><td>02:48:00</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>Males     </td><td>02:46:00</td></tr>\n",
       "\t<tr><td>Spain         </td><td>Males     </td><td>02:46:00</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>Males     </td><td>01:49:00</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>Males     </td><td>02:26:00</td></tr>\n",
       "\t<tr><td>Austria       </td><td>Gender gap</td><td>01:45:00</td></tr>\n",
       "\t<tr><td>Belgium       </td><td>Gender gap</td><td>01:18:00</td></tr>\n",
       "\t<tr><td>Estonia       </td><td>Gender gap</td><td>01:11:00</td></tr>\n",
       "\t<tr><td>Finland       </td><td>Gender gap</td><td>01:11:00</td></tr>\n",
       "\t<tr><td>France        </td><td>Gender gap</td><td>01:18:00</td></tr>\n",
       "\t<tr><td>Germany       </td><td>Gender gap</td><td>01:15:00</td></tr>\n",
       "\t<tr><td>Greece        </td><td>Gender gap</td><td>02:30:00</td></tr>\n",
       "\t<tr><td>Hungary       </td><td>Gender gap</td><td>01:53:00</td></tr>\n",
       "\t<tr><td>Italy         </td><td>Gender gap</td><td>03:03:00</td></tr>\n",
       "\t<tr><td>Luxembourg    </td><td>Gender gap</td><td>01:40:00</td></tr>\n",
       "\t<tr><td>Netherlands   </td><td>Gender gap</td><td>01:04:00</td></tr>\n",
       "\t<tr><td>Norway        </td><td>Gender gap</td><td>00:45:00</td></tr>\n",
       "\t<tr><td>Poland        </td><td>Gender gap</td><td>01:56:00</td></tr>\n",
       "\t<tr><td>Romania       </td><td>Gender gap</td><td>02:26:00</td></tr>\n",
       "\t<tr><td>Serbia        </td><td>Gender gap</td><td>02:22:00</td></tr>\n",
       "\t<tr><td>Spain         </td><td>Gender gap</td><td>02:11:00</td></tr>\n",
       "\t<tr><td>Turkey        </td><td>Gender gap</td><td>03:33:00</td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>Gender gap</td><td>01:35:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 54 × 3\n",
       "\\begin{tabular}{lll}\n",
       " geo & sex & values\\\\\n",
       " <chr> & <chr> & <times>\\\\\n",
       "\\hline\n",
       "\t Austria        & Females    & 04:39:00\\\\\n",
       "\t Belgium        & Females    & 04:08:00\\\\\n",
       "\t Estonia        & Females    & 04:14:00\\\\\n",
       "\t Finland        & Females    & 03:55:00\\\\\n",
       "\t France         & Females    & 04:13:00\\\\\n",
       "\t Germany        & Females    & 04:00:00\\\\\n",
       "\t Greece         & Females    & 04:43:00\\\\\n",
       "\t Hungary        & Females    & 04:57:00\\\\\n",
       "\t Italy          & Females    & 05:30:00\\\\\n",
       "\t Luxembourg     & Females    & 04:00:00\\\\\n",
       "\t Netherlands    & Females    & 03:43:00\\\\\n",
       "\t Norway         & Females    & 03:46:00\\\\\n",
       "\t Poland         & Females    & 04:50:00\\\\\n",
       "\t Romania        & Females    & 05:14:00\\\\\n",
       "\t Serbia         & Females    & 05:08:00\\\\\n",
       "\t Spain          & Females    & 04:57:00\\\\\n",
       "\t Turkey         & Females    & 05:22:00\\\\\n",
       "\t United Kingdom & Females    & 04:01:00\\\\\n",
       "\t Austria        & Males      & 02:54:00\\\\\n",
       "\t Belgium        & Males      & 02:50:00\\\\\n",
       "\t Estonia        & Males      & 03:03:00\\\\\n",
       "\t Finland        & Males      & 02:44:00\\\\\n",
       "\t France         & Males      & 02:55:00\\\\\n",
       "\t Germany        & Males      & 02:45:00\\\\\n",
       "\t Greece         & Males      & 02:13:00\\\\\n",
       "\t Hungary        & Males      & 03:04:00\\\\\n",
       "\t Italy          & Males      & 02:27:00\\\\\n",
       "\t Luxembourg     & Males      & 02:20:00\\\\\n",
       "\t Netherlands    & Males      & 02:39:00\\\\\n",
       "\t Norway         & Males      & 03:01:00\\\\\n",
       "\t Poland         & Males      & 02:54:00\\\\\n",
       "\t Romania        & Males      & 02:48:00\\\\\n",
       "\t Serbia         & Males      & 02:46:00\\\\\n",
       "\t Spain          & Males      & 02:46:00\\\\\n",
       "\t Turkey         & Males      & 01:49:00\\\\\n",
       "\t United Kingdom & Males      & 02:26:00\\\\\n",
       "\t Austria        & Gender gap & 01:45:00\\\\\n",
       "\t Belgium        & Gender gap & 01:18:00\\\\\n",
       "\t Estonia        & Gender gap & 01:11:00\\\\\n",
       "\t Finland        & Gender gap & 01:11:00\\\\\n",
       "\t France         & Gender gap & 01:18:00\\\\\n",
       "\t Germany        & Gender gap & 01:15:00\\\\\n",
       "\t Greece         & Gender gap & 02:30:00\\\\\n",
       "\t Hungary        & Gender gap & 01:53:00\\\\\n",
       "\t Italy          & Gender gap & 03:03:00\\\\\n",
       "\t Luxembourg     & Gender gap & 01:40:00\\\\\n",
       "\t Netherlands    & Gender gap & 01:04:00\\\\\n",
       "\t Norway         & Gender gap & 00:45:00\\\\\n",
       "\t Poland         & Gender gap & 01:56:00\\\\\n",
       "\t Romania        & Gender gap & 02:26:00\\\\\n",
       "\t Serbia         & Gender gap & 02:22:00\\\\\n",
       "\t Spain          & Gender gap & 02:11:00\\\\\n",
       "\t Turkey         & Gender gap & 03:33:00\\\\\n",
       "\t United Kingdom & Gender gap & 01:35:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 54 × 3\n",
       "\n",
       "| geo &lt;chr&gt; | sex &lt;chr&gt; | values &lt;times&gt; |\n",
       "|---|---|---|\n",
       "| Austria        | Females    | 04:39:00 |\n",
       "| Belgium        | Females    | 04:08:00 |\n",
       "| Estonia        | Females    | 04:14:00 |\n",
       "| Finland        | Females    | 03:55:00 |\n",
       "| France         | Females    | 04:13:00 |\n",
       "| Germany        | Females    | 04:00:00 |\n",
       "| Greece         | Females    | 04:43:00 |\n",
       "| Hungary        | Females    | 04:57:00 |\n",
       "| Italy          | Females    | 05:30:00 |\n",
       "| Luxembourg     | Females    | 04:00:00 |\n",
       "| Netherlands    | Females    | 03:43:00 |\n",
       "| Norway         | Females    | 03:46:00 |\n",
       "| Poland         | Females    | 04:50:00 |\n",
       "| Romania        | Females    | 05:14:00 |\n",
       "| Serbia         | Females    | 05:08:00 |\n",
       "| Spain          | Females    | 04:57:00 |\n",
       "| Turkey         | Females    | 05:22:00 |\n",
       "| United Kingdom | Females    | 04:01:00 |\n",
       "| Austria        | Males      | 02:54:00 |\n",
       "| Belgium        | Males      | 02:50:00 |\n",
       "| Estonia        | Males      | 03:03:00 |\n",
       "| Finland        | Males      | 02:44:00 |\n",
       "| France         | Males      | 02:55:00 |\n",
       "| Germany        | Males      | 02:45:00 |\n",
       "| Greece         | Males      | 02:13:00 |\n",
       "| Hungary        | Males      | 03:04:00 |\n",
       "| Italy          | Males      | 02:27:00 |\n",
       "| Luxembourg     | Males      | 02:20:00 |\n",
       "| Netherlands    | Males      | 02:39:00 |\n",
       "| Norway         | Males      | 03:01:00 |\n",
       "| Poland         | Males      | 02:54:00 |\n",
       "| Romania        | Males      | 02:48:00 |\n",
       "| Serbia         | Males      | 02:46:00 |\n",
       "| Spain          | Males      | 02:46:00 |\n",
       "| Turkey         | Males      | 01:49:00 |\n",
       "| United Kingdom | Males      | 02:26:00 |\n",
       "| Austria        | Gender gap | 01:45:00 |\n",
       "| Belgium        | Gender gap | 01:18:00 |\n",
       "| Estonia        | Gender gap | 01:11:00 |\n",
       "| Finland        | Gender gap | 01:11:00 |\n",
       "| France         | Gender gap | 01:18:00 |\n",
       "| Germany        | Gender gap | 01:15:00 |\n",
       "| Greece         | Gender gap | 02:30:00 |\n",
       "| Hungary        | Gender gap | 01:53:00 |\n",
       "| Italy          | Gender gap | 03:03:00 |\n",
       "| Luxembourg     | Gender gap | 01:40:00 |\n",
       "| Netherlands    | Gender gap | 01:04:00 |\n",
       "| Norway         | Gender gap | 00:45:00 |\n",
       "| Poland         | Gender gap | 01:56:00 |\n",
       "| Romania        | Gender gap | 02:26:00 |\n",
       "| Serbia         | Gender gap | 02:22:00 |\n",
       "| Spain          | Gender gap | 02:11:00 |\n",
       "| Turkey         | Gender gap | 03:33:00 |\n",
       "| United Kingdom | Gender gap | 01:35:00 |\n",
       "\n"
      ],
      "text/plain": [
       "   geo            sex        values  \n",
       "1  Austria        Females    04:39:00\n",
       "2  Belgium        Females    04:08:00\n",
       "3  Estonia        Females    04:14:00\n",
       "4  Finland        Females    03:55:00\n",
       "5  France         Females    04:13:00\n",
       "6  Germany        Females    04:00:00\n",
       "7  Greece         Females    04:43:00\n",
       "8  Hungary        Females    04:57:00\n",
       "9  Italy          Females    05:30:00\n",
       "10 Luxembourg     Females    04:00:00\n",
       "11 Netherlands    Females    03:43:00\n",
       "12 Norway         Females    03:46:00\n",
       "13 Poland         Females    04:50:00\n",
       "14 Romania        Females    05:14:00\n",
       "15 Serbia         Females    05:08:00\n",
       "16 Spain          Females    04:57:00\n",
       "17 Turkey         Females    05:22:00\n",
       "18 United Kingdom Females    04:01:00\n",
       "19 Austria        Males      02:54:00\n",
       "20 Belgium        Males      02:50:00\n",
       "21 Estonia        Males      03:03:00\n",
       "22 Finland        Males      02:44:00\n",
       "23 France         Males      02:55:00\n",
       "24 Germany        Males      02:45:00\n",
       "25 Greece         Males      02:13:00\n",
       "26 Hungary        Males      03:04:00\n",
       "27 Italy          Males      02:27:00\n",
       "28 Luxembourg     Males      02:20:00\n",
       "29 Netherlands    Males      02:39:00\n",
       "30 Norway         Males      03:01:00\n",
       "31 Poland         Males      02:54:00\n",
       "32 Romania        Males      02:48:00\n",
       "33 Serbia         Males      02:46:00\n",
       "34 Spain          Males      02:46:00\n",
       "35 Turkey         Males      01:49:00\n",
       "36 United Kingdom Males      02:26:00\n",
       "37 Austria        Gender gap 01:45:00\n",
       "38 Belgium        Gender gap 01:18:00\n",
       "39 Estonia        Gender gap 01:11:00\n",
       "40 Finland        Gender gap 01:11:00\n",
       "41 France         Gender gap 01:18:00\n",
       "42 Germany        Gender gap 01:15:00\n",
       "43 Greece         Gender gap 02:30:00\n",
       "44 Hungary        Gender gap 01:53:00\n",
       "45 Italy          Gender gap 03:03:00\n",
       "46 Luxembourg     Gender gap 01:40:00\n",
       "47 Netherlands    Gender gap 01:04:00\n",
       "48 Norway         Gender gap 00:45:00\n",
       "49 Poland         Gender gap 01:56:00\n",
       "50 Romania        Gender gap 02:26:00\n",
       "51 Serbia         Gender gap 02:22:00\n",
       "52 Spain          Gender gap 02:11:00\n",
       "53 Turkey         Gender gap 03:33:00\n",
       "54 United Kingdom Gender gap 01:35:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "casted<-dcast(dt,geo~sex,value.var=\"values\")\n",
    "casted[,gap:=Females-Males]\n",
    "dt<-melt(casted,measure.vars=c(\"Females\",\"Males\",\"gap\"),variable.name=\"sex\",value.name=\"values\")\n",
    "dt[,sex:=gsub(\"gap\",\"Gender gap\",sex)]\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make the graph again with *ggplot*. We have to order by increasing value of *Females*, add the empty spaces before the EFTA and accession countries. Finally adjust scaling, remove vertical grid lines, column width and resize the plotting area to better see the figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sep<-data.table::data.table(sex=c(\"Males\",\"Males\"),geo=c(\" \",\"  \"),values=c(chron::times(NA),chron::times(NA)))\n",
    "dt<-rbind(dt,dt_sep)\n",
    "geo_ord<-dt[(geo %in% eu_ctry_names)&grepl(\"Females\",sex)]\n",
    "geo_ord<-geo_ord[order(values)]$geo\n",
    "geo_ord<-c(geo_ord,' ','Norway','  ','Serbia','Turkey')\n",
    "dt$geo<-factor(dt$geo,levels=geo_ord)\n",
    "sex_ord<-c('Males','Females',\"Gender gap\")\n",
    "dt$sex<-factor(dt$sex,levels=sex_ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 2 rows containing missing values (geom_bar).\"\n"
     ]
    },
    {
     "data": {
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DTrM3OXurPfpA8PzFEqfJeac3V7LzEecxOFTcrCpfamC88l5JcXZsccXfang1jZ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WKgEn3IA4TvEwNCw+8dmnPf7vtf1s1pqNgfXq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\nHrCBZm3xtJE3jv8mOT785NYpilj9/bX/5ev5JvXfly8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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1350
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig12_colors<-c(\"#FAA519\",\"#286EB4\",\"black\") #F06423\n",
    "\n",
    "options(repr.plot.width=9, repr.plot.height=6,repr.plot.res=300)\n",
    "ggplot(dt,aes(x=geo,y=values)) + theme_minimal() +\n",
    "  geom_bar(data=dt[sex!=\"Gender gap\"], aes(fill=sex),position=\"dodge\",stat=\"identity\",width=0.6)+\n",
    "  geom_point(data=dt[grepl(\"Gender gap\",sex)],aes(shape=sex),colour=\"#F06423\",fill=\"black\",size=3)+\n",
    "  scale_y_chron(format=\"%H:%M\",breaks=seq(0,1,1/24)) +\n",
    "  scale_fill_manual(values = fig12_colors)+\n",
    "  scale_shape_manual(values=c(\"Males\"=NA,\"Females\"=NA,\"Gender gap\"=21))+\n",
    "  ggtitle(\"Figure 12: Participation time per day in unpaid work (main activity), by gender, (hh mm; 2008 to 2015)\") +\n",
    "  ylab(\"\")+\n",
    "  xlab(\"\")+\n",
    "  theme(legend.title = element_blank(),\n",
    "        legend.position= \"bottom\",\n",
    "        axis.text.x = element_text(angle = 90, hjust = 1),\n",
    "        panel.grid.major.x = element_blank(),\n",
    "        panel.grid.minor.y = element_blank())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}