{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo material for notebook on \"Digital economy and society statistics\"\n",
    "\n",
    "Prepared by [**F. Sheeka**](fsheeka@gmail.com).\n",
    "\n",
    "This notebook aims at illustrating the *Statistics Explained* article on [**Digital economy and society statistics - households and individuals**](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Digital_economy_and_society_statistics_-_households_and_individuals)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 1: Internet access and broadband internet connections of households, EU-28, 2008-2018 (% of all households)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "Loading required package: usethis\n",
      "restatapi: - config file with the API version 1 loaded from GitHub (the 'current' API version number is 1).\n",
      "           - 2 from the 4 cores are used for parallel computing.\n",
      "           - 'libcurl' will be used for file download.\n",
      "           - the Table of contents (TOC) was not pre-loaded into the deafult cache ('.restatapi_env').\n"
     ]
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(tidyr)\n",
    "library(repr)\n",
    "library(dplyr)\n",
    "library(devtools)\n",
    "library(restatapi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Using get_eurostat_data to pull data from the first dataset we want. \n",
    "I've called it dataset1_1 so we can differentiate from the other dataset we are pulling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1_1 <- get_eurostat_data(id=\"isoc_ci_in_h\", \n",
    "filters = list(geo = \"EU28\", unit = \"PC_HH\", hhtyp = \"TOTAL\"),\n",
    "date_filter = \"2008:2018\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The first dataset I pulled does not have the same variables as the second one. In order to differentiate them, I will create a new variable in my first dataset and call it \"INT_ACCESS\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1_1$indic_is <- \"INT_ACCESS\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* And now data from the other dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1_2 <- get_eurostat_data(id=\"isoc_ci_it_h\", \n",
    "filters = list(geo = \"EU28\", unit = \"PC_HH\", hhtyp = \"TOTAL\", indic_is = \"H_BROAD\"),\n",
    "date_filter = \"2008:2018\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Combining the datasets by row using r(ow)bind to transform it into one dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_fig1 <- rbind(dataset1_1, dataset1_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Converting the years in the dataset to numerical values. This is necessary for line graphs! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_fig1$time<-as.numeric(as.character(dataset_fig1$time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If you like, you can have a sneakpeak of the dataset using head() just to make sure everything looks okay before you go ahead with the visualisation. You can also just type in the name of the dataset and run it in order to see the full dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.table: 22 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>hhtyp</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th><th scope=col>indic_is</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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2008</td><td>60</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2009</td><td>66</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2010</td><td>70</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2011</td><td>73</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2012</td><td>76</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2013</td><td>79</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2014</td><td>81</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2015</td><td>83</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2016</td><td>85</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2017</td><td>87</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2018</td><td>89</td><td>INT_ACCESS</td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2008</td><td>48</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2009</td><td>56</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2010</td><td>61</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2011</td><td>67</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2012</td><td>72</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2013</td><td>76</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2014</td><td>78</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2015</td><td>80</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2016</td><td>83</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2017</td><td>85</td><td>H_BROAD   </td></tr>\n",
       "\t<tr><td>PC_HH</td><td>TOTAL</td><td>EU28</td><td>2018</td><td>86</td><td>H_BROAD   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 22 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " unit & hhtyp & geo & time & values & indic\\_is\\\\\n",
       " <fct> & <fct> & <fct> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t PC\\_HH & TOTAL & EU28 & 2008 & 60 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2009 & 66 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2010 & 70 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2011 & 73 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2012 & 76 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2013 & 79 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2014 & 81 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2015 & 83 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2016 & 85 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2017 & 87 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2018 & 89 & INT\\_ACCESS\\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2008 & 48 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2009 & 56 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2010 & 61 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2011 & 67 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2012 & 72 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2013 & 76 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2014 & 78 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2015 & 80 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2016 & 83 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2017 & 85 & H\\_BROAD   \\\\\n",
       "\t PC\\_HH & TOTAL & EU28 & 2018 & 86 & H\\_BROAD   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 22 × 6\n",
       "\n",
       "| unit &lt;fct&gt; | hhtyp &lt;fct&gt; | geo &lt;fct&gt; | time &lt;dbl&gt; | values &lt;dbl&gt; | indic_is &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| PC_HH | TOTAL | EU28 | 2008 | 60 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2009 | 66 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2010 | 70 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2011 | 73 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2012 | 76 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2013 | 79 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2014 | 81 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2015 | 83 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2016 | 85 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2017 | 87 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2018 | 89 | INT_ACCESS |\n",
       "| PC_HH | TOTAL | EU28 | 2008 | 48 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2009 | 56 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2010 | 61 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2011 | 67 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2012 | 72 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2013 | 76 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2014 | 78 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2015 | 80 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2016 | 83 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2017 | 85 | H_BROAD    |\n",
       "| PC_HH | TOTAL | EU28 | 2018 | 86 | H_BROAD    |\n",
       "\n"
      ],
      "text/plain": [
       "   unit  hhtyp geo  time values indic_is  \n",
       "1  PC_HH TOTAL EU28 2008 60     INT_ACCESS\n",
       "2  PC_HH TOTAL EU28 2009 66     INT_ACCESS\n",
       "3  PC_HH TOTAL EU28 2010 70     INT_ACCESS\n",
       "4  PC_HH TOTAL EU28 2011 73     INT_ACCESS\n",
       "5  PC_HH TOTAL EU28 2012 76     INT_ACCESS\n",
       "6  PC_HH TOTAL EU28 2013 79     INT_ACCESS\n",
       "7  PC_HH TOTAL EU28 2014 81     INT_ACCESS\n",
       "8  PC_HH TOTAL EU28 2015 83     INT_ACCESS\n",
       "9  PC_HH TOTAL EU28 2016 85     INT_ACCESS\n",
       "10 PC_HH TOTAL EU28 2017 87     INT_ACCESS\n",
       "11 PC_HH TOTAL EU28 2018 89     INT_ACCESS\n",
       "12 PC_HH TOTAL EU28 2008 48     H_BROAD   \n",
       "13 PC_HH TOTAL EU28 2009 56     H_BROAD   \n",
       "14 PC_HH TOTAL EU28 2010 61     H_BROAD   \n",
       "15 PC_HH TOTAL EU28 2011 67     H_BROAD   \n",
       "16 PC_HH TOTAL EU28 2012 72     H_BROAD   \n",
       "17 PC_HH TOTAL EU28 2013 76     H_BROAD   \n",
       "18 PC_HH TOTAL EU28 2014 78     H_BROAD   \n",
       "19 PC_HH TOTAL EU28 2015 80     H_BROAD   \n",
       "20 PC_HH TOTAL EU28 2016 83     H_BROAD   \n",
       "21 PC_HH TOTAL EU28 2017 85     H_BROAD   \n",
       "22 PC_HH TOTAL EU28 2018 86     H_BROAD   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_fig1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make your graph!\n",
    "\n",
    "* options(repr.plot.width/height): changes the size of your graph. \n",
    "\n",
    "* ggplot2: the package that facilitates data visualisation.\n",
    "\n",
    "* data: the dataframe/dataset you would like ggplot to visualise. \n",
    "\n",
    "* aes: your axes! \n",
    "\n",
    "* geom_line(), geom_bar() and more: define what type of graph you would like to build. I am building a line graph, so I am using geom_line().\n",
    "\n",
    "* scale_colour_manual: controls the colours of your lines, the labels for your lines, and the name of the legend (which in my case, is blank). \n",
    "\n",
    "* ggtitle: the name of the graph.\n",
    "\n",
    "* scale_y/x_continuous: changes the scale of your x and y axes.\n",
    "\n",
    "* theme(text = element_text: changes the size of your font. \n",
    "\n",
    "* y/xlab: labels the x and y axes. \n",
    "\n",
    "More information: \n",
    "* ggplot2 package documentation: https://www.rdocumentation.org/packages/ggplot2/versions/3.2.1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width=20, repr.plot.height=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O1QUXXNCz7iQBaz0JjPmmaYvY/a677lrmIq47jRKQFqOTV2/+e/KT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ECAAAECBAhMKiAAa1I52xEgQIAAAQIECBAgQIAAAQIE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/m5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\nBQSwKhLpQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBA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2GP/cX2x3K/BaLEcKViSf/v6ypSncwst0TZttz3Yv3kCUQZR6g1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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data=dataset_fig1, aes(x=time, y=values, group=factor(indic_is), \n",
    "color=factor(indic_is))) +\n",
    "\n",
    "geom_line() +\n",
    "\n",
    "scale_color_manual(values = c(\"#F06423\", \"#276EB4\"), labels = c(\"Internet access\", \n",
    "\"Broadband connection\"), , name = \" \") +\n",
    "\n",
    "ggtitle(\"Internet access and broadband internet connections of households, EU-28, 2008-2018 (% of all households)\") +\n",
    "  \n",
    "scale_y_continuous(limits = c(0, 100), breaks = seq(0, 180, by = 25)) +\n",
    "scale_x_continuous(limits = c(2008, 2018), breaks = seq(2008, 2018, by = 1)) +\n",
    "\n",
    "theme(text = element_text(size = 20)) + \n",
    "  \n",
    "ylab(\" \") +\n",
    "xlab(\" \")  "
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}