{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# *Statistics coded*: Tourism statistics \n", "\n", "Prepared by **S. Hadj Hassen** ([EMOS](https://ec.europa.eu/eurostat/web/european-statistical-system/emos)).\n", "\n", "The objective of this notebook is to reproduce the figures of the *Statistics Explained* article on [**tourism statistics**](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Tourism_statistics)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configuration\n", "\n", "If necessary install the following packages and load them afterwards." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# install.packages(c(\"ggplot2\", \"plotly\", \"tidyr\", \"repr\", \"dplyr\", \"devtools\", \"restatapi\", \"eurostat\", \n", "# \"patchwork\", \"Matching\", \"ggforce\", \"h2o\", \"plyr\"))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: ‘dplyr’\n", "\n", "\n", "The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "\n", "restatapi: - config file with the API version 1 loaded from GitHub (the 'current' API version number is 1).\n", " - 4 from the 8 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", "\n", "------------------------------------------------------------------------------\n", "\n", "You have loaded plyr after dplyr - this is likely to cause problems.\n", "If you need functions from both plyr and dplyr, please load plyr first, then dplyr:\n", "library(plyr); library(dplyr)\n", "\n", "------------------------------------------------------------------------------\n", "\n", "\n", "Attaching package: ‘plyr’\n", "\n", "\n", "The following objects are masked from ‘package:dplyr’:\n", "\n", " arrange, count, desc, failwith, id, mutate, rename, summarise,\n", " summarize\n", "\n", "\n" ] } ], "source": [ "library(ggplot2)\n", "library(tidyr)\n", "library(dplyr)\n", "# library(eurostat)\n", "library(restatapi)\n", "#library(Matching)\n", "library(stats)\n", "library(base)\n", "library(ggforce)\n", "#library(h2o)\n", "library(plyr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tourism destinations - Nights spent at tourist accomodation establishments, 2018\n", "\n", "### Graph of the abstract\n", "\n", "#### First step\n", "\n", " - load the needed data: `data`\n", " - we just need the data for 2018, therefore we add a column, which indicates only the year - we will do this for each subsequent datset \n", " - create a vector with all needed countries: `countr`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data <- as_tibble(get_eurostat_data(id=\"tour_occ_ninat\")) \n", "\n", "data$year <- as.integer(substr(data$time,1,4))\n", "\n", "countr <- c(\"ES\",\"IT\",\"FR\",\"EL\",\"AT\",\"DE\",\"HR\",\"PT\",\"NL\",\"CZ\",\"BE\",\"BG\",\"PL\",\"IE\",\"CY\",\"SE\",\"HU\",\"DK\",\"SI\",\n", " \"MT\",\"FI\",\"SK\",\"RO\",\"EE\",\"LV\",\"LT\",\"LU\",\"UK\",\"CH\",\"NO\",\"IS\",\"LI\",\"TR\",\"ME\",\"RS\",\"MK\",\"XK\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Ireland, Slovenia, United Kingdom, Switzerland, Norway, Iceland, Montenegro, Serbia and Kosovo we need 2018 monthly data.\n", "- therefore we need another dataset and have to load it: `data_month`\n", "- add a column, which indicates only the year and one with the month\n", "- create a vector, which includes only the countries mentioned above: `countr_fig0`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_month <- as_tibble(get_eurostat_data(id=\"tour_occ_nim\"))\n", "\n", "data_month$year <- as.integer(substr(data_month$time,1,4))\n", "data_month$month <- as.integer(substr(data_month$time,6,7))\n", "\n", "countr_fig0 <- c(\"IE\",\"SI\",\"UK\",\"CH\",\"NO\",\"IS\",\"ME\",\"RS\",\"XK\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- build the subset, which we need for the graph: `data_mod`\n", "- because we have monthly data - but need yearly data - we have to aggregate the data per country\n", "- name the second column " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_mod <- subset(data_month, \n", " data_month$geo %in% countr_fig0 & #include just the countries of the vector above\n", " data_month$year==2018 & #only values for 2018\n", " data_month$c_resid==\"FOR\" & #just non-residents\n", " data_month$nace_r2==\"I551-I553\"& #the classification we need\n", " data_month$unit==\"NR\") #we need the unit \"number\"\n", "\n", "data_mod <- aggregate(data_mod$values, by = list(geo = data_mod$geo), FUN = sum)\n", "\n", "names(data_mod)[2] <- \"values\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build needed subset for the other countries (same like above with the dataset `data`): `data_mod2`\n", "- we only need column 4 and 6 for the following analysis" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [], "source": [ "data_mod2 <- subset(data, \n", " data$geo %in% countr & \n", " data$year==2018 &\n", " data$c_resid==\"FOR\" &\n", " data$nace_r2==\"I551-I553\"&\n", " data$unit==\"NR\")\n", "\n", "data_mod2 <- data_mod2[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Turkey (`countr_fig0_1`) we need data from 2016. Build subset `data_mod3`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "countr_fig0_1 <- c(\"TR\")\n", "\n", "data_mod3 <- subset(data, \n", " data$geo %in% countr_fig0_1 & \n", " data$year==2016 & \n", " data$c_resid==\"FOR\" &\n", " data$nace_r2==\"I551-I553\"&\n", " data$unit==\"NR\")\n", "\n", "data_mod3 <- data_mod3[,c(4,6)] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Match `data_mod1` and `data_mod2` and `data_mod3`\n", "- i.e. use rbind to stick the data frames together - we get the final dataset for the graph: `data_mod4`\n", "\n", "Bring the country names in the order the graphic shows: you can do this due to modify the levels of the variable `\"geo\"`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data_mod4 <- rbind(data_mod,data_mod2,data_mod3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
geovalues
<fct><dbl>
1CH26720046
2IE16759455
3IS 7446807
4ME 3763857
5NO10138183
6RS 3656787
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & geo & values\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & CH & 26720046\\\\\n", "\t2 & IE & 16759455\\\\\n", "\t3 & IS & 7446807\\\\\n", "\t4 & ME & 3763857\\\\\n", "\t5 & NO & 10138183\\\\\n", "\t6 & RS & 3656787\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | geo <fct> | values <dbl> |\n", "|---|---|---|\n", "| 1 | CH | 26720046 |\n", "| 2 | IE | 16759455 |\n", "| 3 | IS | 7446807 |\n", "| 4 | ME | 3763857 |\n", "| 5 | NO | 10138183 |\n", "| 6 | RS | 3656787 |\n", "\n" ], "text/plain": [ " geo values \n", "1 CH 26720046\n", "2 IE 16759455\n", "3 IS 7446807\n", "4 ME 3763857\n", "5 NO 10138183\n", "6 RS 3656787" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_mod4$geo <- factor(data_mod4$geo,\n", " levels = c(\"ES\",\"IT\",\"FR\",\"EL\",\"AT\",\"DE\",\"HR\",\"PT\",\"NL\",\"CZ\",\"BE\",\"BG\",\"PL\",\"IE\",\"CY\",\"SE\",\"HU\",\n", " \"DK\",\"SI\", \"MT\",\"FI\",\"SK\",\"RO\",\"EE\",\"LV\",\"LT\",\"LU\",\"UK\",\"CH\",\"NO\",\"IS\",\"LI\",\"TR\",\"ME\",\n", " \"RS\",\"MK\",\"XK\"))\n", "\n", "head(data_mod4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Third step\n", "\n", "Finally use the modified dataset `data_mod4` and build the graph by using `ggplot`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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URz8vPzP/744+eeey44OFhpCQ8PDw8PV5b79u17+fLljRs3jhkzRubVKisrzX8D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\nShDsAAAAVIJgBwAAoBIEOwAAAJUg2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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "options(scipen=999) #prevent scientific notation\n", "label_axes1 <- seq(0, 300, by = 50) #we need this later to label the axes\n", "\n", "ggplot(data_mod4, #needed dataset\n", " aes(x=geo,y=values)) + #the axes you want\n", " geom_bar(stat = \"identity\", position=\"dodge\", width=0.5, fill=\"steelblue3\") + #for creating a bar chart\n", " theme_classic() +\n", " scale_y_continuous(limits = c(0, 310000000), breaks = seq(0, 300000000, by = 50000000), #limit the axes\n", " labels=label_axes1) + #label the axes\n", " ggtitle(\"Tourism destinations - nights spent at tourist accomodation establishments, 2018\") +\n", " ylab(\"(million nights spent in the country by non-residents\") + #name the axes\n", " xlab(\" \")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bed places in the EU-27: Italy and France predominate\n", "\n", "### Table 1\n", "\n", "We need 3 datasets - one for each column:\n", "- create a countries vector - the countries are already in the same order the table shows: `countr_0`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "countr_0 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"UK\",\"IS\",\"LI\",\"NO\",\"CH\",\"ME\",\"MK\",\"RS\",\"TR\",\"XK\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### First step\n", "\n", "column 1: number of establishments (units)\n", "- load the needed data and add the year-column: `data_1`\n", "- exclude the countries from the country-vector, which need data from 2016 and 2017: `countr_tab1`" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "data_1 <- as_tibble(get_eurostat_data(id=\"tour_cap_nat\"))\n", "data_1$year <- as.integer(substr(data_1$time,1,4))\n", "\n", "countr_tab1 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PL\",\"PT\",\"RO\",\"SK\",\"FI\",\"SE\",\"IS\",\"LI\",\"NO\",\"CH\",\"ME\",\"MK\",\"RS\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build the subset we need for the first column of the table and choose column 4 and 6 again: `data_tab1`" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "data_tab1 <- subset(data_1, \n", " data_1$geo %in% countr_tab1 & \n", " data_1$year==2018 &\n", " data_1$nace_r2==\"I551-I553\"&\n", " data_1$unit==\"NR\"&\n", " data_1$accommod==\"ESTBL\")\n", "data_tab1 <- data_tab1[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Ireland and United Kingdom (`countr_tab1_1`) we need data from 2016. Build the subset: `data_tab1_1`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "countr_tab1_1 <- c(\"IE\",\"UK\")\n", "data_tab1_1 <- subset(data_1, \n", " data_1$geo %in% countr_tab1_1 & \n", " data_1$year==2016 &\n", " data_1$nace_r2==\"I551-I553\"&\n", " data_1$unit==\"NR\"&\n", " data_1$accommod==\"ESTBL\")\n", "data_tab1_1 <- data_tab1_1[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Luxembourg, Slovenia and Kosovo (`countr_tab1_2`) we need data from 2017. Build the subset: `data_tab1_2`" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "countr_tab1_2 <- c(\"LU\",\"SI\",\"XK\")\n", "data_tab1_2 <- subset(data_1, \n", " data_1$geo %in% countr_tab1_2 & \n", " data_1$year==2017 &\n", " data_1$nace_r2==\"I551-I553\"&\n", " data_1$unit==\"NR\"&\n", " data_1$accommod==\"ESTBL\")\n", "data_tab1_2 <- data_tab1_2[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Turkey we have NA. Assign to `data_tab1_3`" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "data_tab1_3 <- data.frame( geo = \"TR\", values= NA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Match the three calculated datasets to `data_tab_col1` and bring the country names in the order the graphic shows" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 2
geovalues
<fct><dbl>
BE 9211
BG 3458
CZ 9426
DK 1167
DE50020
EE 1535
\n" ], "text/latex": [ "A tibble: 6 × 2\n", "\\begin{tabular}{ll}\n", " geo & values\\\\\n", " & \\\\\n", "\\hline\n", "\t BE & 9211\\\\\n", "\t BG & 3458\\\\\n", "\t CZ & 9426\\\\\n", "\t DK & 1167\\\\\n", "\t DE & 50020\\\\\n", "\t EE & 1535\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 2\n", "\n", "| geo <fct> | values <dbl> |\n", "|---|---|\n", "| BE | 9211 |\n", "| BG | 3458 |\n", "| CZ | 9426 |\n", "| DK | 1167 |\n", "| DE | 50020 |\n", "| EE | 1535 |\n", "\n" ], "text/plain": [ " geo values\n", "1 BE 9211 \n", "2 BG 3458 \n", "3 CZ 9426 \n", "4 DK 1167 \n", "5 DE 50020 \n", "6 EE 1535 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab_col1 <- rbind(data_tab1,data_tab1_1,data_tab1_2,data_tab1_3)\n", "\n", "data_tab_col1 <- data_tab_col1[order(factor(data_tab_col1$geo, levels= countr_0)),]\n", "\n", "head(data_tab_col1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "column 2: number of bed places (thousands)\n", "- exclude the countries, which need data from 2016 and 2017: `countr_tab2`\n", "- build the subset and choose again column 4 and 6: `data_tab2`" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "countr_tab2 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PL\",\"PT\",\"RO\",\"SK\",\"FI\",\"SE\",\"IS\",\"LI\",\"NO\",\"CH\",\"ME\",\"MK\",\"RS\",\"TR\")\n", "data_tab2 <- subset(data_1, \n", " data_1$geo %in% countr_tab2 & \n", " data_1$year==2018 &\n", " data_1$nace_r2==\"I551-I553\"&\n", " data_1$unit==\"NR\"&\n", " data_1$accommod==\"BEDPL\")\n", "data_tab2 <- data_tab2[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Ireland and United Kingdom (`countr_tab2_1`) we need data from 2016. Build the subset: `data_tab2_1`" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "countr_tab2_1 <- c(\"IE\",\"UK\")\n", "data_tab2_1 <- subset(data_1, \n", " data_1$geo %in% countr_tab2_1 & \n", " data_1$year==2016 &\n", " data_1$nace_r2==\"I551-I553\"&\n", " data_1$unit==\"NR\"&\n", " data_1$accommod==\"BEDPL\")\n", "data_tab2_1 <- data_tab2_1[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Luxembourg, Slovenia and Kosovo (`countr_tab2_2`) we need data from 2017. Build the subset: `data_tab2_2`" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "countr_tab2_2 <- c(\"LU\",\"SI\",\"XK\")\n", "data_tab2_2 <- subset(data_1, \n", " data_1$geo %in% countr_tab2_2 & \n", " data_1$year==2017 &\n", " data_1$nace_r2==\"I551-I553\"&\n", " data_1$unit==\"NR\"&\n", " data_1$accommod==\"BEDPL\")\n", "data_tab2_2 <- data_tab2_2[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Turkey we have NA. Assign to `data_tab2_3`" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "data_tab2_3 <- data.frame( geo = \"TR\", values= NA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Match the three calculated datasets to `data_tab_col2` and bring the country names in the order the graph shows" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 2
geovalues
<fct><dbl>
BE 389826
BG 335597
CZ 741235
DK 426075
DE3473630
EE 61193
\n" ], "text/latex": [ "A tibble: 6 × 2\n", "\\begin{tabular}{ll}\n", " geo & values\\\\\n", " & \\\\\n", "\\hline\n", "\t BE & 389826\\\\\n", "\t BG & 335597\\\\\n", "\t CZ & 741235\\\\\n", "\t DK & 426075\\\\\n", "\t DE & 3473630\\\\\n", "\t EE & 61193\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 2\n", "\n", "| geo <fct> | values <dbl> |\n", "|---|---|\n", "| BE | 389826 |\n", "| BG | 335597 |\n", "| CZ | 741235 |\n", "| DK | 426075 |\n", "| DE | 3473630 |\n", "| EE | 61193 |\n", "\n" ], "text/plain": [ " geo values \n", "1 BE 389826\n", "2 BG 335597\n", "3 CZ 741235\n", "4 DK 426075\n", "5 DE 3473630\n", "6 EE 61193" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab_col2 <- rbind(data_tab2,data_tab2_1,data_tab2_2,data_tab2_3)\n", "\n", "data_tab_col2 <- data_tab_col2[order(factor(data_tab_col2$geo, levels= countr_0)),]\n", "\n", "head(data_tab_col2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Third step\n", "\n", "column 3: nights spent by residents and non-residents (millions)\n", "- exclude the countries, which need monthly data and data from 2016: `countr_tab3`\n", "- we already load the data (`data`) for a graph before. Build the subset: `data_tab3`" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "countr_tab3 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PL\",\"PT\",\"RO\",\"SK\",\"FI\",\"SE\",\"LI\",\"MK\",\"TR\")\n", "\n", "data_tab3 <- subset(data, \n", " data$geo %in% countr_tab3 & \n", " data$year==2018 &\n", " data$c_resid==\"TOTAL\" &\n", " data$nace_r2==\"I551-I553\"&\n", " data$unit==\"NR\")\n", "data_tab3 <- data_tab3[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Ireland, Slovenia, United Kingdom, Switzerland, Norway, Iceland, Montenegro, Serbia and Kosovo (`countr_tab3_1`) we need 2018 monthly data\n", "- we already load the data (`data_month`) for a graph before. Build the subset: `data_tab3_1`\n", "- because we have monthly data - but need yearly data - we have to aggregate the data per country\n", "- name the second column " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "countr_tab3_1 <- c(\"IE\",\"SI\",\"UK\",\"CH\",\"NO\",\"IS\",\"ME\",\"RS\",\"XK\")\n", "\n", "data_tab3_1 <- subset(data_month, \n", " data_month$geo %in% countr_tab3_1 & \n", " data_month$year==2018 &\n", " data_month$c_resid==\"TOTAL\" &\n", " data_month$nace_r2==\"I551-I553\"&\n", " data_month$unit==\"NR\")\n", "data_tab3_1 <- aggregate(data_tab3_1$values, by = list(geo = data_tab3_1$geo), FUN = sum)\n", "names(data_tab3_1)[2] <- \"values\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Turkey (`countr_tab3_2`) we need data from 2016. Build the subset: `data_tab3_2`" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "countr_tab3_2 <- c(\"TR\")\n", "\n", "data_tab3_2 <- subset(data, \n", " data$geo %in% countr_tab3_2 & \n", " data$year==2016 &\n", " data$c_resid==\"TOTAL\" &\n", " data$nace_r2==\"I551-I553\"&\n", " data$unit==\"NR\")\n", "data_tab3_2 <- data_tab3_2[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Match the three calculated datasets to `data_tab_col3` and bring the country names in the order the graphic shows" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 2
geovalues
<fct><dbl>
BE 41320284
BG 26845013
CZ 55513922
DK 33280395
DE419556284
EE 6630315
\n" ], "text/latex": [ "A tibble: 6 × 2\n", "\\begin{tabular}{ll}\n", " geo & values\\\\\n", " & \\\\\n", "\\hline\n", "\t BE & 41320284\\\\\n", "\t BG & 26845013\\\\\n", "\t CZ & 55513922\\\\\n", "\t DK & 33280395\\\\\n", "\t DE & 419556284\\\\\n", "\t EE & 6630315\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 2\n", "\n", "| geo <fct> | values <dbl> |\n", "|---|---|\n", "| BE | 41320284 |\n", "| BG | 26845013 |\n", "| CZ | 55513922 |\n", "| DK | 33280395 |\n", "| DE | 419556284 |\n", "| EE | 6630315 |\n", "\n" ], "text/plain": [ " geo values \n", "1 BE 41320284\n", "2 BG 26845013\n", "3 CZ 55513922\n", "4 DK 33280395\n", "5 DE 419556284\n", "6 EE 6630315" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab_col3 <- rbind(data_tab3,data_tab3_1,data_tab3_2)\n", "\n", "data_tab_col3 <- data_tab_col3[order(factor(data_tab_col3$geo, levels= countr_0)),]\n", "\n", "head(data_tab_col3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Forth step\n", "\n", "- create a column with the names of the countries: `countr_names`\n", "- match all columns together to create the data for the final table: `table`" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
countr_namesvaluesvaluesvalues
<fct><dbl><dbl><dbl>
1Belgium 9211 389826 41320284
2Bulgaria 3458 335597 26845013
3Czechia 9426 741235 55513922
4Denmark 1167 426075 33280395
5Germany 500203473630419556284
6Estonia 1535 61193 6630315
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & countr\\_names & values & values & values\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & Belgium & 9211 & 389826 & 41320284\\\\\n", "\t2 & Bulgaria & 3458 & 335597 & 26845013\\\\\n", "\t3 & Czechia & 9426 & 741235 & 55513922\\\\\n", "\t4 & Denmark & 1167 & 426075 & 33280395\\\\\n", "\t5 & Germany & 50020 & 3473630 & 419556284\\\\\n", "\t6 & Estonia & 1535 & 61193 & 6630315\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | countr_names <fct> | values <dbl> | values <dbl> | values <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | Belgium | 9211 | 389826 | 41320284 |\n", "| 2 | Bulgaria | 3458 | 335597 | 26845013 |\n", "| 3 | Czechia | 9426 | 741235 | 55513922 |\n", "| 4 | Denmark | 1167 | 426075 | 33280395 |\n", "| 5 | Germany | 50020 | 3473630 | 419556284 |\n", "| 6 | Estonia | 1535 | 61193 | 6630315 |\n", "\n" ], "text/plain": [ " countr_names values values values \n", "1 Belgium 9211 389826 41320284\n", "2 Bulgaria 3458 335597 26845013\n", "3 Czechia 9426 741235 55513922\n", "4 Denmark 1167 426075 33280395\n", "5 Germany 50020 3473630 419556284\n", "6 Estonia 1535 61193 6630315" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "countr_names <- c(\"Belgium\",\"Bulgaria\",\"Czechia\",\"Denmark\",\"Germany\",\"Estonia\", \"Ireland\", \"Greece\", \"Spain\", \"France\", \n", " \"Croatia\", \"Italy\", \"Cyprus\", \"Latvia\", \"Lithuania\", \"Luxembourg\", \"Hungary\", \"Malta\", \"Netherlands\", \n", " \"Austria\", \"Poland\", \"Portugal\", \"Romania\", \"Slovenia\", \"Slovakia\", \"Finland\", \"Sweden\",\n", " \"United Kingdom\", \"Iceland\", \"Liechtenstein\", \"Norway\", \"Switzerland\", \"Montenegro\", \"North Macedonia\", \n", " \"Serbia\", \"Turkey\", \"Kosovo\")\n", "\n", "table <- cbind(countr_names, data_tab_col1[,2], data_tab_col2[,2], data_tab_col3[,2])\n", "\n", "head(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fifth step\n", "\n", "Calculate the EU-27 value by summing up\n", "- first we create the vector with the 27 countries, we will use this vector in other graphics, too: `EU_27`" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "EU_27 <- c(\"AT\",\"BE\", \"BG\",\"HR\",\"CY\", \"CZ\",\"DK\", \"DE\", \"EE\",\"FI\",\"FR\",\"EL\",\"HU\",\"IE\",\n", " \"IT\",\"LV\",\"LT\",\"LU\",\"MT\",\"NL\",\"PL\",\"PT\",\"RO\",\"SK\",\"SI\",\"ES\",\"SE\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- We need the `table` from above (the three columns) with shortcuts instead of full names to exclude the not-EU countries: `tab_prep`\n", " - do this by using the `EU_27` vector\n", " - therefore we add the `countr_0` vector, which we create in the first step\n", "- then create a vector, which indicates due to FALSE and TRUE at the exact position, which countries of `countr_0` are within EU_27: `EU_27_prep`\n", "- by using the sapply-command we can calculate the sum for each column of the table `table`" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "tab_prep <- cbind(countr_0,data_tab_col1[,2], data_tab_col2[,2], data_tab_col3[,2])\n", "\n", "EU_27_prep <- tab_prep$countr_0 %in% EU_27\n", "\n", "tab_EU <- c(\"EU-27\",sapply(table[EU_27_prep,2:4],sum)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- put the summarized values to the calculated `table` from above: `table_final`\n", " - at this stage we get the warning message, that one NA for \"EU-27\" get generated: we solve this problem two steps later\n", "- give the columns the correct names\n", "- we have to convert the class, because we have to add \"EU-27\" and this only works by converting it from a factor first, then doing the change and re-convert it back\n", "- we want to modify the numbers: second column in thousands and third columns in millions" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in `[<-.factor`(`*tmp*`, ri, value = \"EU-27\"):\n", "“invalid factor level, NA generated”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 38 × 4 of type chr
Number of establishments (units)Number of bed places (thousands)Nights spent by residents and non-residents (millions)
EU-27 600157282942791.2
Belgium 9211 390 41.3
Bulgaria 3458 336 26.8
Czechia 9426 741 55.5
Denmark 1167 426 33.3
Germany 50020 3474 419.6
Estonia 1535 61 6.6
Ireland 3145 200 33.4
Greece 38180 1340 123.1
Spain 51418 3600 466.9
France 29652 5112 442.8
Croatia 108212 1116 89.6
Italy 216141 5113 428.8
Cyprus 802 87 17.2
Latvia 1145 54 5.4
Lithuania 3616 90 8.1
Luxembourg 425 64 2.9
Hungary 4587 419 32.8
Malta 211 45 10.1
Netherlands 9145 1398 116.1
Austria 21494 1046 125.2
Poland 11076 799 88.9
Portugal 5964 649 74.4
Romania 7720 349 28.5
Slovenia 3699 117 15.7
Slovakia 3087 191 15.2
Finland 1372 260 22.2
Sweden 4249 819 60.9
United Kingdom 84580 3944 354.4
Iceland 1120 47 8.6
Liechtenstein 96 2 0.2
Norway 2351 588 33.8
Switzerland 39057 665 55.3
Montenegro 382 43 4.2
North Macedonia494 48 2.2
Serbia 3651 109 9.3
Turkey NA NA 112.5
Kosovo 217 8 0.3
\n" ], "text/latex": [ "A matrix: 38 × 4 of type chr\n", "\\begin{tabular}{llll}\n", " & Number of establishments (units) & Number of bed places (thousands) & Nights spent by residents and non-residents (millions)\\\\\n", "\\hline\n", "\t EU-27 & 600157 & 28294 & 2791.2\\\\\n", "\t Belgium & 9211 & 390 & 41.3\\\\\n", "\t Bulgaria & 3458 & 336 & 26.8\\\\\n", "\t Czechia & 9426 & 741 & 55.5\\\\\n", "\t Denmark & 1167 & 426 & 33.3\\\\\n", "\t Germany & 50020 & 3474 & 419.6\\\\\n", "\t Estonia & 1535 & 61 & 6.6\\\\\n", "\t Ireland & 3145 & 200 & 33.4\\\\\n", "\t Greece & 38180 & 1340 & 123.1\\\\\n", "\t Spain & 51418 & 3600 & 466.9\\\\\n", "\t France & 29652 & 5112 & 442.8\\\\\n", "\t Croatia & 108212 & 1116 & 89.6\\\\\n", "\t Italy & 216141 & 5113 & 428.8\\\\\n", "\t Cyprus & 802 & 87 & 17.2\\\\\n", "\t Latvia & 1145 & 54 & 5.4\\\\\n", "\t Lithuania & 3616 & 90 & 8.1\\\\\n", "\t Luxembourg & 425 & 64 & 2.9\\\\\n", "\t Hungary & 4587 & 419 & 32.8\\\\\n", "\t Malta & 211 & 45 & 10.1\\\\\n", "\t Netherlands & 9145 & 1398 & 116.1\\\\\n", "\t Austria & 21494 & 1046 & 125.2\\\\\n", "\t Poland & 11076 & 799 & 88.9\\\\\n", "\t Portugal & 5964 & 649 & 74.4\\\\\n", "\t Romania & 7720 & 349 & 28.5\\\\\n", "\t Slovenia & 3699 & 117 & 15.7\\\\\n", "\t Slovakia & 3087 & 191 & 15.2\\\\\n", "\t Finland & 1372 & 260 & 22.2\\\\\n", "\t Sweden & 4249 & 819 & 60.9\\\\\n", "\t United Kingdom & 84580 & 3944 & 354.4\\\\\n", "\t Iceland & 1120 & 47 & 8.6\\\\\n", "\t Liechtenstein & 96 & 2 & 0.2\\\\\n", "\t Norway & 2351 & 588 & 33.8\\\\\n", "\t Switzerland & 39057 & 665 & 55.3\\\\\n", "\t Montenegro & 382 & 43 & 4.2\\\\\n", "\t North Macedonia & 494 & 48 & 2.2\\\\\n", "\t Serbia & 3651 & 109 & 9.3\\\\\n", "\t Turkey & NA & NA & 112.5\\\\\n", "\t Kosovo & 217 & 8 & 0.3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 38 × 4 of type chr\n", "\n", "| | Number of establishments (units) | Number of bed places (thousands) | Nights spent by residents and non-residents (millions) |\n", "|---|---|---|---|\n", "| EU-27 | 600157 | 28294 | 2791.2 |\n", "| Belgium | 9211 | 390 | 41.3 |\n", "| Bulgaria | 3458 | 336 | 26.8 |\n", "| Czechia | 9426 | 741 | 55.5 |\n", "| Denmark | 1167 | 426 | 33.3 |\n", "| Germany | 50020 | 3474 | 419.6 |\n", "| Estonia | 1535 | 61 | 6.6 |\n", "| Ireland | 3145 | 200 | 33.4 |\n", "| Greece | 38180 | 1340 | 123.1 |\n", "| Spain | 51418 | 3600 | 466.9 |\n", "| France | 29652 | 5112 | 442.8 |\n", "| Croatia | 108212 | 1116 | 89.6 |\n", "| Italy | 216141 | 5113 | 428.8 |\n", "| Cyprus | 802 | 87 | 17.2 |\n", "| Latvia | 1145 | 54 | 5.4 |\n", "| Lithuania | 3616 | 90 | 8.1 |\n", "| Luxembourg | 425 | 64 | 2.9 |\n", "| Hungary | 4587 | 419 | 32.8 |\n", "| Malta | 211 | 45 | 10.1 |\n", "| Netherlands | 9145 | 1398 | 116.1 |\n", "| Austria | 21494 | 1046 | 125.2 |\n", "| Poland | 11076 | 799 | 88.9 |\n", "| Portugal | 5964 | 649 | 74.4 |\n", "| Romania | 7720 | 349 | 28.5 |\n", "| Slovenia | 3699 | 117 | 15.7 |\n", "| Slovakia | 3087 | 191 | 15.2 |\n", "| Finland | 1372 | 260 | 22.2 |\n", "| Sweden | 4249 | 819 | 60.9 |\n", "| United Kingdom | 84580 | 3944 | 354.4 |\n", "| Iceland | 1120 | 47 | 8.6 |\n", "| Liechtenstein | 96 | 2 | 0.2 |\n", "| Norway | 2351 | 588 | 33.8 |\n", "| Switzerland | 39057 | 665 | 55.3 |\n", "| Montenegro | 382 | 43 | 4.2 |\n", "| North Macedonia | 494 | 48 | 2.2 |\n", "| Serbia | 3651 | 109 | 9.3 |\n", "| Turkey | NA | NA | 112.5 |\n", "| Kosovo | 217 | 8 | 0.3 |\n", "\n" ], "text/plain": [ " Number of establishments (units)\n", " [1,] EU-27 600157 \n", " [2,] Belgium 9211 \n", " [3,] Bulgaria 3458 \n", " [4,] Czechia 9426 \n", " [5,] Denmark 1167 \n", " [6,] Germany 50020 \n", " [7,] Estonia 1535 \n", " [8,] Ireland 3145 \n", " [9,] Greece 38180 \n", "[10,] Spain 51418 \n", "[11,] France 29652 \n", "[12,] Croatia 108212 \n", "[13,] Italy 216141 \n", "[14,] Cyprus 802 \n", "[15,] Latvia 1145 \n", "[16,] Lithuania 3616 \n", "[17,] Luxembourg 425 \n", "[18,] Hungary 4587 \n", "[19,] Malta 211 \n", "[20,] Netherlands 9145 \n", "[21,] Austria 21494 \n", "[22,] Poland 11076 \n", "[23,] Portugal 5964 \n", "[24,] Romania 7720 \n", "[25,] Slovenia 3699 \n", "[26,] Slovakia 3087 \n", "[27,] Finland 1372 \n", "[28,] Sweden 4249 \n", "[29,] United Kingdom 84580 \n", "[30,] Iceland 1120 \n", "[31,] Liechtenstein 96 \n", "[32,] Norway 2351 \n", "[33,] Switzerland 39057 \n", "[34,] Montenegro 382 \n", "[35,] North Macedonia 494 \n", "[36,] Serbia 3651 \n", "[37,] Turkey NA \n", "[38,] Kosovo 217 \n", " Number of bed places (thousands)\n", " [1,] 28294 \n", " [2,] 390 \n", " [3,] 336 \n", " [4,] 741 \n", " [5,] 426 \n", " [6,] 3474 \n", " [7,] 61 \n", " [8,] 200 \n", " [9,] 1340 \n", "[10,] 3600 \n", "[11,] 5112 \n", "[12,] 1116 \n", "[13,] 5113 \n", "[14,] 87 \n", "[15,] 54 \n", "[16,] 90 \n", "[17,] 64 \n", "[18,] 419 \n", "[19,] 45 \n", "[20,] 1398 \n", "[21,] 1046 \n", "[22,] 799 \n", "[23,] 649 \n", "[24,] 349 \n", "[25,] 117 \n", "[26,] 191 \n", "[27,] 260 \n", "[28,] 819 \n", "[29,] 3944 \n", "[30,] 47 \n", "[31,] 2 \n", "[32,] 588 \n", "[33,] 665 \n", "[34,] 43 \n", "[35,] 48 \n", "[36,] 109 \n", "[37,] NA \n", "[38,] 8 \n", " Nights spent by residents and non-residents (millions)\n", " [1,] 2791.2 \n", " [2,] 41.3 \n", " [3,] 26.8 \n", " [4,] 55.5 \n", " [5,] 33.3 \n", " [6,] 419.6 \n", " [7,] 6.6 \n", " [8,] 33.4 \n", " [9,] 123.1 \n", "[10,] 466.9 \n", "[11,] 442.8 \n", "[12,] 89.6 \n", "[13,] 428.8 \n", "[14,] 17.2 \n", "[15,] 5.4 \n", "[16,] 8.1 \n", "[17,] 2.9 \n", "[18,] 32.8 \n", "[19,] 10.1 \n", "[20,] 116.1 \n", "[21,] 125.2 \n", "[22,] 88.9 \n", "[23,] 74.4 \n", "[24,] 28.5 \n", "[25,] 15.7 \n", "[26,] 15.2 \n", "[27,] 22.2 \n", "[28,] 60.9 \n", "[29,] 354.4 \n", "[30,] 8.6 \n", "[31,] 0.2 \n", "[32,] 33.8 \n", "[33,] 55.3 \n", "[34,] 4.2 \n", "[35,] 2.2 \n", "[36,] 9.3 \n", "[37,] 112.5 \n", "[38,] 0.3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table_final <- rbind(tab_EU,table)\n", "\n", "names(table_final) <- c(\"\", \"Number of establishments (units)\", \"Number of bed places (thousands)\",\n", " \"Nights spent by residents and non-residents (millions)\")\n", "\n", "table_final[,1] <- as.character(table_final[,1])\n", "table_final[1,1] <- \"EU-27\"\n", "table_final[,1] <- factor(table_final[,1])\n", "\n", "table_final[,3] <- round(as.numeric(table_final[,3])/ 1000,0)\n", "table_final[,4] <- round(as.numeric(table_final[,4])/ 1000000,1)\n", "\n", "as.matrix(table_final)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nights spent by non-residents in the EU-27: Spain on top\n", "\n", "### Figure 1\n", "\n", "#### First step\n", "\n", "- choose the countries from the EU 2020 and build the country vector like we did above: `EU_27`\n", "- we already load the data (`data`) for the first graph we create" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "EU_27 <- c(\"AT\",\"BE\", \"BG\",\"HR\",\"CY\", \"CZ\",\"DK\", \"DE\", \"EE\",\"FI\",\"FR\",\"EL\",\"HU\",\"IE\",\n", " \"IT\",\"LV\",\"LT\",\"LU\",\"MT\",\"NL\",\"PL\",\"PT\",\"RO\",\"SK\",\"SI\",\"ES\",\"SE\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build a loop for getting the data for the three lines\n", "- we have to create a vector with the essential difference between the three columns: `night`\n", "- create an empty data frame, which we can fill while running the loop: `data_fig1_final`\n", "- when we look at the data `data`, we see that there are some years without datapoints, therefore we can omit them\n", "- for every year we have data from the 27 EU-countries - we just need one aggregate value for every year\n", "- assign the calculated data `data_fig1` in each intermediate step to the empty data frame" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 14 × 4
FOR.yearFOR.xNAT.xTOTAL.x
<int><dbl><dbl><dbl>
12005 86193458011436929102005627490
22006 89742183511784075542075829389
32007 92312950911670293222090158831
42008 91729102711685376322085828659
52009 86598476611618702942027855060
62010 92118704512407922452161979290
72011 98809386512572580872245351952
82012102810329512541408912282244186
92013107363869212569321572330570849
102014110727298512784064822385679467
112015115342764813326043342486031982
122016121244911813732569632585706081
132017127420530214101267302684332032
142018130138159414407743402742155934
\n" ], "text/latex": [ "A data.frame: 14 × 4\n", "\\begin{tabular}{r|llll}\n", " & FOR.year & FOR.x & NAT.x & TOTAL.x\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 2005 & 861934580 & 1143692910 & 2005627490\\\\\n", "\t2 & 2006 & 897421835 & 1178407554 & 2075829389\\\\\n", "\t3 & 2007 & 923129509 & 1167029322 & 2090158831\\\\\n", "\t4 & 2008 & 917291027 & 1168537632 & 2085828659\\\\\n", "\t5 & 2009 & 865984766 & 1161870294 & 2027855060\\\\\n", "\t6 & 2010 & 921187045 & 1240792245 & 2161979290\\\\\n", "\t7 & 2011 & 988093865 & 1257258087 & 2245351952\\\\\n", "\t8 & 2012 & 1028103295 & 1254140891 & 2282244186\\\\\n", "\t9 & 2013 & 1073638692 & 1256932157 & 2330570849\\\\\n", "\t10 & 2014 & 1107272985 & 1278406482 & 2385679467\\\\\n", "\t11 & 2015 & 1153427648 & 1332604334 & 2486031982\\\\\n", "\t12 & 2016 & 1212449118 & 1373256963 & 2585706081\\\\\n", "\t13 & 2017 & 1274205302 & 1410126730 & 2684332032\\\\\n", "\t14 & 2018 & 1301381594 & 1440774340 & 2742155934\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 14 × 4\n", "\n", "| | FOR.year <int> | FOR.x <dbl> | NAT.x <dbl> | TOTAL.x <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | 2005 | 861934580 | 1143692910 | 2005627490 |\n", "| 2 | 2006 | 897421835 | 1178407554 | 2075829389 |\n", "| 3 | 2007 | 923129509 | 1167029322 | 2090158831 |\n", "| 4 | 2008 | 917291027 | 1168537632 | 2085828659 |\n", "| 5 | 2009 | 865984766 | 1161870294 | 2027855060 |\n", "| 6 | 2010 | 921187045 | 1240792245 | 2161979290 |\n", "| 7 | 2011 | 988093865 | 1257258087 | 2245351952 |\n", "| 8 | 2012 | 1028103295 | 1254140891 | 2282244186 |\n", "| 9 | 2013 | 1073638692 | 1256932157 | 2330570849 |\n", "| 10 | 2014 | 1107272985 | 1278406482 | 2385679467 |\n", "| 11 | 2015 | 1153427648 | 1332604334 | 2486031982 |\n", "| 12 | 2016 | 1212449118 | 1373256963 | 2585706081 |\n", "| 13 | 2017 | 1274205302 | 1410126730 | 2684332032 |\n", "| 14 | 2018 | 1301381594 | 1440774340 | 2742155934 |\n", "\n" ], "text/plain": [ " FOR.year FOR.x NAT.x TOTAL.x \n", "1 2005 861934580 1143692910 2005627490\n", "2 2006 897421835 1178407554 2075829389\n", "3 2007 923129509 1167029322 2090158831\n", "4 2008 917291027 1168537632 2085828659\n", "5 2009 865984766 1161870294 2027855060\n", "6 2010 921187045 1240792245 2161979290\n", "7 2011 988093865 1257258087 2245351952\n", "8 2012 1028103295 1254140891 2282244186\n", "9 2013 1073638692 1256932157 2330570849\n", "10 2014 1107272985 1278406482 2385679467\n", "11 2015 1153427648 1332604334 2486031982\n", "12 2016 1212449118 1373256963 2585706081\n", "13 2017 1274205302 1410126730 2684332032\n", "14 2018 1301381594 1440774340 2742155934" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "night <- c(\"FOR\",\"NAT\",\"TOTAL\")\n", "\n", "data_fig1_final <- list(year = c(2018,2017,2016,2015,2014,2013,2012,2011,2010,2009,2008,2007,2006,2005),\n", " FOR = matrix(ncol=2, nrow=14),\n", " NAT = matrix(ncol=2, nrow=14),\n", " TOTAL = matrix(ncol=2, nrow=14))\n", "# build the loop\n", "for (i in 1:3) {\n", "\n", "data_fig1 <- subset(data, \n", " data$geo %in% EU_27 & \n", " data$year %in% c(2005,2006, 2007, 2008,2009,2010,2011,2012, 2013,2014,2015,2016,2017,2018) &\n", " data$c_resid== night[i] & #here we have the essential difference between the columns we create\n", " data$unit==\"NR\" &\n", " data$nace_r2==\"I551-I553\") \n", "\n", "data_fig1 <- aggregate(data_fig1$values, by = list(year = data_fig1$year), FUN = sum) \n", " \n", "# print(data_fig1) #if you want to see the intermediate steps\n", "\n", "data_fig1_final[[i+1]] <- data_fig1 \n", " \n", " }\n", "\n", "data_fig1_final <- as.data.frame(data_fig1_final)\n", "data_fig1_final <- data_fig1_final[,c(2,3,5,7)] #attention! the first year-column is wrongly in an decreasing order!\n", "data_fig1_final <- data_fig1_final[order(data_fig1_final[,1]),]\n", "data_fig1_final" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Now we have to calculate the changes from year to year \n", "- index: 2005=100: `index`\n", "- create an empty data frame, which we can fill while running the loop: `data_fig1_final_perc`\n", "- we take the values for 2005 as reference value. Each value from the subsequent years should be devided by the reference value from 2005. We have to multiply the result by 100 to achieve, that we start in 2005 with the value 100." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
V1V2V3V4
<dbl><dbl><dbl><dbl>
12005100.0000100.0000100.0000
22006104.1172103.0353103.5002
32007107.0997102.0404104.2147
42008106.4223102.1723103.9988
52009100.4699101.5894101.1083
62010106.8744108.4900107.7957
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & V1 & V2 & V3 & V4\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 2005 & 100.0000 & 100.0000 & 100.0000\\\\\n", "\t2 & 2006 & 104.1172 & 103.0353 & 103.5002\\\\\n", "\t3 & 2007 & 107.0997 & 102.0404 & 104.2147\\\\\n", "\t4 & 2008 & 106.4223 & 102.1723 & 103.9988\\\\\n", "\t5 & 2009 & 100.4699 & 101.5894 & 101.1083\\\\\n", "\t6 & 2010 & 106.8744 & 108.4900 & 107.7957\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | V1 <dbl> | V2 <dbl> | V3 <dbl> | V4 <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | 2005 | 100.0000 | 100.0000 | 100.0000 |\n", "| 2 | 2006 | 104.1172 | 103.0353 | 103.5002 |\n", "| 3 | 2007 | 107.0997 | 102.0404 | 104.2147 |\n", "| 4 | 2008 | 106.4223 | 102.1723 | 103.9988 |\n", "| 5 | 2009 | 100.4699 | 101.5894 | 101.1083 |\n", "| 6 | 2010 | 106.8744 | 108.4900 | 107.7957 |\n", "\n" ], "text/plain": [ " V1 V2 V3 V4 \n", "1 2005 100.0000 100.0000 100.0000\n", "2 2006 104.1172 103.0353 103.5002\n", "3 2007 107.0997 102.0404 104.2147\n", "4 2008 106.4223 102.1723 103.9988\n", "5 2009 100.4699 101.5894 101.1083\n", "6 2010 106.8744 108.4900 107.7957" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "index <- data_fig1_final[1,2:4]\n", "\n", "data_fig1_final_perc <- as.data.frame(matrix(ncol=4, nrow=14))\n", "\n", "for (i in 1:14) {\n", "data_fig1_final_perc[i,2:4] <- (data_fig1_final[i,2:4]/index)*100 }\n", "\n", "data_fig1_final_perc$V1 <- c(2005,2006, 2007, 2008,2009,2010,2011,2012, 2013,2014,2015,2016,2017,2018)\n", "head(data_fig1_final_perc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Thrid step\n", "\n", "Finally use the modified dataset `data_fig1_final_perc` and build the graph by using ggplot" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Continuous limits supplied to discrete scale.\n", "Did you mean `limits = factor(...)` or `scale_*_continuous()`?”\n" ] }, { "data": { "image/png": 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aK//e1ve/fuXbFixfjx4/lfFjv685///MILL6SkpERFRT388MOe8ri4OM8T0hIS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\nf//+s2bNkslkBw8eXLZsmcPhmDJlChHl5OS8/vr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cZD1aUtOwZCwlKCUj4jXhXtUVfkd7niDdxVYO43OmXGEAACAASURBVAlp8GN08xsk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\nRy5VCG0/Pf73zEA3YZty79qS96r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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "ggplot(data_fig1_final_perc,aes(x=V1)) + \n", " geom_line(aes(y=V2, group=1, color=\"line\"), color=\"lightblue\", size=1) + #we add three lines\n", " geom_line(aes(y=V3, group=1, color=\"line\"), color=\"darkblue\", size=1) +\n", " geom_line(aes(y=V4, group=1, color=\"line\"), color=\"darkorange\", size=2) +\n", " ggtitle(\"Trends in nights spent at EU-27 tourist accommodation establishments, EU-27, 2005-2018\",\n", " subtitle=\"(index 2005=100)\") +\n", " scale_y_continuous(limits = c(100, 155), breaks = seq(100, 155, by = 5)) +\n", " scale_x_discrete(limits = c(2005,2006, 2007, 2008,2009,2010,2011,2012, 2013,2014,2015,2016,2017,2018)) +\n", " ylab(\" \") +\n", " xlab(\" \") +\n", " theme(panel.grid.major.y = element_line(color=\"grey\"),\n", " panel.background = element_rect(fill = NA),\n", " axis.ticks.y = element_line(colour= \"white\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure 2\n", "\n", "It's in principal the same graph like in the abstract part. Check the code there." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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7927dqhQ4fu3bs3d+5cc3Pz/Pz827dv379/X/kTrlscS7tmQ3kqx1JhpOvXr//3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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "label_axes2 <- seq(0, 320, by = 20)\n", "\n", "ggplot(data_mod4, aes(x=geo,y=values)) +\n", " geom_bar(stat = \"identity\", position=\"dodge\", width=0.5, fill=\"steelblue3\") +\n", " scale_y_continuous(limits = c(0, 320000000), breaks = seq(0, 320000000, by = 20000000),\n", " labels=label_axes2) +\n", " ggtitle(\"Tourism destinations - nights spent at tourist accomodation establishments, 2018\",\n", " subtitle = \"(million nights spent in the country by non-residents)\") +\n", " coord_flip() +\n", " scale_x_discrete(limits = rev(levels(data_mod4$geo)),\n", " labels = c(\"Kosovo\", \"North Macedonia\", \"Serbia\", \"Montenegro\", \"Turkey\", \"Liechtenstein\", \n", " \"Iceland\", \"Norway\", \"Switzerland\", \"United Kingdom\", \"Luxembourg\", \"Lithuania\", \n", " \"Latvia\", \"Estonia\", \"Romania\", \"Slovakia\", \"Finland\", \"Malta\", \"Slovenia\", \n", " \"Denmark\", \"Hungary\", \"Sweden\", \"Cyprus\", \"Ireland\", \"Poland\", \"Bulgaria\", \n", " \"Belgium\", \"Czechia\", \"Netherlands\", \"Portugal\", \"Croatia\", \"Germany\", \"Austria\", \n", " \"Greece\", \"France\", \"Italy\", \"Spain\")) +\n", " ylab(\" \") +\n", " xlab(\" \") +\n", " theme(panel.grid.major.x = element_line(color=\"grey\"),\n", " panel.background = element_rect(fill = NA),\n", " axis.line = element_line(colour = \"white\"),\n", " axis.ticks = element_line(colour= \"white\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure 3\n", "\n", "#### First step\n", "\n", "Calculate the EU-27 value by summing up\n", "- create again a vector, which indicates due to FALSE and TRUE at the exact position, which countries of `countr_0` are within `EU_27`: `EU_27_prep2`\n", "- by using the sapply-command we can calculate the sum for each column of `data_mod4`" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "EU_27_prep2 <- data_mod4$geo %in% EU_27\n", "\n", "tab_EU2 <- sapply(as.data.frame(data_mod4[EU_27_prep2,2]),sum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add a column with the ratios we need for the pie chart: `tab_ratio`\n", "- shorten the table through `EU_27_prep2` (countries outside the EU should be delete)\n", "- sort the ratios in a decreasing order\n", "- keep the top 9 countries in `tab_ratio_top9` and summing up the others to \"Rest of EU-27\" in `tab_ratio_other` by using again the sapply command\n", "- add afterwards the sum in `tab_ratio_other_sum` to the top 9: `tab_ratio_new`" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in `[<-.factor`(`*tmp*`, ri, value = 15.9651602486859):\n", "“invalid factor level, NA generated”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 10 × 2
geovalues
<fct><dbl>
19ES22.645198
24IT16.287560
21FR10.586126
18EL 7.716184
10AT 6.717353
15DE 6.541928
22HR 6.254410
33PT 3.750269
31NL 3.535812
101NA15.965160
\n" ], "text/latex": [ "A data.frame: 10 × 2\n", "\\begin{tabular}{r|ll}\n", " & geo & values\\\\\n", " & & \\\\\n", "\\hline\n", "\t19 & ES & 22.645198\\\\\n", "\t24 & IT & 16.287560\\\\\n", "\t21 & FR & 10.586126\\\\\n", "\t18 & EL & 7.716184\\\\\n", "\t10 & AT & 6.717353\\\\\n", "\t15 & DE & 6.541928\\\\\n", "\t22 & HR & 6.254410\\\\\n", "\t33 & PT & 3.750269\\\\\n", "\t31 & NL & 3.535812\\\\\n", "\t101 & NA & 15.965160\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 10 × 2\n", "\n", "| | geo <fct> | values <dbl> |\n", "|---|---|---|\n", "| 19 | ES | 22.645198 |\n", "| 24 | IT | 16.287560 |\n", "| 21 | FR | 10.586126 |\n", "| 18 | EL | 7.716184 |\n", "| 10 | AT | 6.717353 |\n", "| 15 | DE | 6.541928 |\n", "| 22 | HR | 6.254410 |\n", "| 33 | PT | 3.750269 |\n", "| 31 | NL | 3.535812 |\n", "| 101 | NA | 15.965160 |\n", "\n" ], "text/plain": [ " geo values \n", "19 ES 22.645198\n", "24 IT 16.287560\n", "21 FR 10.586126\n", "18 EL 7.716184\n", "10 AT 6.717353\n", "15 DE 6.541928\n", "22 HR 6.254410\n", "33 PT 3.750269\n", "31 NL 3.535812\n", "101 NA 15.965160" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tab_ratio <- cbind(data_mod4,data_mod4[2]/tab_EU2*100)\n", "\n", "tab_ratio <- tab_ratio[EU_27_prep2,]\n", "\n", "tab_ratio <- tab_ratio[order(tab_ratio[,3], decreasing=T),]\n", "\n", "tab_ratio_top9 <- tab_ratio[1:9,]\n", "tab_ratio_other <- tab_ratio[10:27,]\n", "tab_ratio_other_sum <- sapply(as.data.frame(tab_ratio_other[,3]),sum)\n", "\n", "tab_ratio_new <- rbind(tab_ratio_top9[,c(1,3)], tab_ratio_other_sum)\n", "tab_ratio_new" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Plot the pie chart \n", "- first create the labels\n", " - round the values and put a `\"%\"` behind it: `label_pie1`\n", " - create a vector with the needed country names: `label_pie2`\n", " - combine the both created label parts: `label_pie3`\n", "- within ggplot we have to create bars first. Afterwards we can convert it to a pie chart" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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wvbsGFDOVcEAABwjR0Y5siRI0SUmJhoZWUlnD5y5EgiSklJ+fvvv42ufPDgwWpT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\nLcf4tEW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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "label_pie1 <- paste(round(tab_ratio_new$values,0), \"%\")\n", "label_pie2 <- c(\"Spain\",\"Italy\",\"France\",\"Greece\",\"Austria\",\"Germany\",\"Croatia\",\"Portugal\",\"Netherlands\",\"Rest of EU-27\")\n", "label_pie3 <- paste(label_pie2,\"\\n\",label_pie1) #we can add \"/n\" to achieve a line break\n", "\n", "ggplot(tab_ratio_new, aes(x=\"\", y=values, fill=geo)) +\n", " geom_bar(stat = \"identity\", width=1) + \n", " coord_polar(\"y\", direction = -1, start=0) +\n", " geom_text(label = label_pie3, position = position_stack(vjust = 0.5),size=2.5) +\n", " ylab(\" \") +\n", " xlab(\" \") +\n", " ggtitle(\"Share of nights spent at EU-27 tourist accommodation by tourists travelling outside their own country \n", " of residence, 2018\",\n", " subtitle = \"(% of all nights spent in EU-27 tourist accommodation establishements)\") +\n", " theme_classic() + \n", " theme(legend.position = \"none\",\n", " axis.line = element_blank(),\n", " axis.text = element_blank(),\n", " axis.ticks = element_blank(),\n", " panel.grid = element_blank())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure 4\n", "\n", "#### First step\n", "\n", "- we already load the dataset and can therefore use the values from the third colomn of `table_final`\n", "- additionally we need the subsequent dataset: `bev`\n", "- build the needed subset and choose only the relevant columns: `bev_subs`" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "bev <- as_tibble(get_eurostat_data(id=\"demo_gind\"))\n", "\n", "bev_subs <- subset(bev, \n", " bev$geo %in% countr_0 & \n", " bev$indic_de==\"AVG\" &\n", " bev$time==\"2018\")\n", "bev_subs <- bev_subs[,c(2,4)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- bring the country's names in the same order (`countr_0`) like the table above, that we can add the columns with the tourist accomodation\n", "- add the columns with the tourist accomodation from `data_tab_col3` and add the needed ratio-column" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "bev_subs <- bev_subs[order(factor(bev_subs$geo, levels= countr_0)),]\n", "\n", "bev_subs <- cbind(bev_subs,data_tab_col3)\n", "\n", "bev_subs <- cbind(bev_subs,bev_subs[,4]/bev_subs[,2])\n", "\n", "names(bev_subs)[5] <- \"values\"\n", "bev_subs <- bev_subs[,c(1,5)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- create new country vector `countr_fig4` to bring the countries in the order the graph shows" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "countr_fig4 <- c(\"HR\", \"MT\",\"CY\",\"AT\",\"EL\",\"ES\",\"SI\",\"PT\",\"IT\",\"IE\",\"NL\",\"FR\",\"SE\",\"DK\",\"CZ\",\"DE\",\"EE\",\"LU\",\"FI\",\"BG\",\n", " \"BE\",\"HU\",\"LT\",\"SK\",\"LV\",\"PL\",\"RO\", \"UK\", \"IS\",\"CH\",\"NO\",\"LI\",\"ME\",\"TR\",\"RS\",\"MK\",\"XK\")\n", "\n", "bev_subs <- bev_subs[order(factor(bev_subs$geo, levels= countr_fig4)),]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Calculate the EU-27 value by summing up \n", "- create a vector, which indicates due to FALSE and TRUE at the exact position, which countries of bev_subs are within `EU_27`: `EU_27_prep_2_1`\n", "- by using the sapply-command we can calculate the average for the column of the table `bev_subs`\n", " - at this stage we get the warning message, that one NA for `\"EU-27\"` get generated: we solve this problem two steps later\n", "- put the summarized values to the calculated table from above\n", "- we have to convert the class, because we have to add `\"EU-27\"` and this only works by converting it from a factor first, then doing the change and re-convert it back" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in `[<-.factor`(`*tmp*`, ri, value = \"EU-27\"):\n", "“invalid factor level, NA generated”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
geovalues
<fct><chr>
1EU-277.37476857884317
11HR 21.894524392122
18MT 20.8699337639024
13CY 19.7274029156342
20AT 14.1653608424209
8EL 11.4682317386886
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & geo & values\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & EU-27 & 7.37476857884317\\\\\n", "\t11 & HR & 21.894524392122 \\\\\n", "\t18 & MT & 20.8699337639024\\\\\n", "\t13 & CY & 19.7274029156342\\\\\n", "\t20 & AT & 14.1653608424209\\\\\n", "\t8 & EL & 11.4682317386886\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | geo <fct> | values <chr> |\n", "|---|---|---|\n", "| 1 | EU-27 | 7.37476857884317 |\n", "| 11 | HR | 21.894524392122 |\n", "| 18 | MT | 20.8699337639024 |\n", "| 13 | CY | 19.7274029156342 |\n", "| 20 | AT | 14.1653608424209 |\n", "| 8 | EL | 11.4682317386886 |\n", "\n" ], "text/plain": [ " geo values \n", "1 EU-27 7.37476857884317\n", "11 HR 21.894524392122 \n", "18 MT 20.8699337639024\n", "13 CY 19.7274029156342\n", "20 AT 14.1653608424209\n", "8 EL 11.4682317386886" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "EU_27_prep_2_1 <- bev_subs$geo %in% EU_27\n", "\n", "tab_EU2_1 <- c(\"EU-27\",sapply(as.data.frame(bev_subs[EU_27_prep_2_1,2]),mean))\n", "\n", "bev_subs <- rbind(tab_EU2_1,bev_subs)\n", "\n", "bev_subs[,1] <- as.character(bev_subs[,1])\n", "bev_subs[1,1] <- \"EU-27\"\n", "bev_subs[,1] <- factor(bev_subs[,1])\n", "\n", "head(bev_subs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Third step\n", "\n", "Plot the graph via `ggplot`\n", "- bring the country names in the order the graphic shows: you can do this due to modify the levels of the variable `\"geo\"`\n", "- to achieve that the y-axes in the chart is later in an ascending order, we have to convert the ratio-axes to numeric" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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lebM2cOIUT0pEfpZy3+888/QUFBXC5XV1fXx8fnzz//9PPzMzIyalKzQqHw1KlT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\nYCJQ2AEAAACYCBR2AAAAACYChR0AAACAiUBhB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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "bev_subs$geo <- factor(bev_subs$geo,\n", " levels = c(\"EU-27\",\"HR\", \"MT\",\"CY\",\"AT\",\"EL\",\"ES\",\"SI\",\"PT\",\"IT\",\"IE\",\"NL\",\"FR\",\"SE\",\"DK\",\"CZ\",\"DE\",\n", " \"EE\",\"LU\",\"FI\",\"BG\",\"BE\",\"HU\",\"LT\",\"SK\",\"LV\",\"PL\",\"RO\", \"UK\", \"IS\",\"CH\",\"NO\",\"LI\",\n", " \"ME\",\"TR\",\"RS\",\"MK\",\"XK\"))\n", "\n", "bev_subs$values <- as.numeric(as.character(bev_subs$values))\n", "\n", "ggplot(bev_subs, aes(x=geo,y=values)) +\n", " geom_bar(stat = \"identity\", position=\"dodge\", width=0.5, fill=\"steelblue3\") +\n", " scale_y_continuous(limits = c(0, 30), breaks = seq(0, 30, by = 5)) +\n", " ggtitle(\"Tourism intensity, 2018\",\n", " subtitle=\"(nights spent by residents and non-residents at tourist accommodation \n", " establishments per inhabitant)\") +\n", " coord_flip() +\n", " scale_x_discrete(limits = rev(levels(bev_subs$geo)),\n", " labels = c(\"Kosovo\", \"North Macedonia\", \"Serbia\", \"Turkey\", \"Montenegro\", \"Liechtenstein\", \n", " \"Norway\", \"Switzerland\", \"Iceland\", \"United Kingdom\", \"Romania\", \"Poland\", \n", " \"Latvia\", \"Slovakia\", \"Lithuania\", \"Hungary\", \"Belgium\", \"Bulgaria\", \"Finland\", \n", " \"Luxembourg\", \"Estonia\", \"Germany\", \"Czechia\", \"Denmark\", \"Sweden\", \"France\", \n", " \"Netherlands\", \"Ireland\", \"Italy\", \"Portugal\", \"Slovenia\", \"Spain\", \"Greece\", \n", " \"Austria\", \"Cyprus\", \"Malta\", \"Croatia\", \"EU-27\")) +\n", " ylab(\" \") +\n", " xlab(\" \") +\n", " theme(panel.grid.major.x = element_line(color=\"grey\"),\n", " panel.background = element_rect(fill = NA),\n", " axis.line = element_line(colour = \"white\"),\n", " axis.ticks = element_line(colour= \"white\"))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tourism trips: Residents of Luxembourg, Belgium, Malta and Slovenia made more outbound than domestic trips\n", "\n", "### Table 2\n", "\n", "First we create the table and afterwards figure 5, because it is easier to build the graph with the data from the table.\n", "\n", "#### First step\n", "\n", "We start with the first three columns, which are based on the same dataset\n", "- load the dataset: `data_2`\n", "- again we add a year-column" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "data_2 <- as_tibble(get_eurostat_data(id=\"tour_dem_tttot\"))\n", "\n", "data_2$year <- as.integer(substr(data_2$time,1,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build a loop\n", "- due to we want to use a loop for getting the data for the three columns, we have to create a vector with the essential difference between the three columns: `duration_vec`\n", "- create an empty data frame, which we can fill while running the loop: `data_tab2_part1`\n", " - we have 3 columns in the needed dataset\n", " - and we have data for 30 countries and therefore 30 rows in the needed dataset" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "duration_vec <- c(\"N_GE1\",\"N1-3\",\"N_GE4\")\n", "\n", "data_tab2_part1 <- as.data.frame(matrix(ncol = 3, nrow = 30))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Within the loop create several subsets from `data_2`\n", "- first subset `data_2_col1_1`: exclude the countries `countr_tab2_1_1`, which need data from 2017\n", "- second subset `data_2_col1_2`: for Poland and Romania in `countr_tab2_1_2` we need data from 2017\n", "- third subset `data_2_col1_3`: for United Kingdom we have NA\n", "- we match afterwards in `data_tab2_col1` the three calculated subsets in every step within the loop\n", "- then we bring the country names in the order the table shows: we can use the vector `countr_0` from beginning\n", "- we calculate the `data_tab2_col1` three times for each case of duration_vec\n", " - to look at the three intermediate step we can print it\n", "- last but not least we assign the three intermediate step to the empty data frame `data_tab2_part1` we create before" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 3
V1V2V3
<dbl><dbl><dbl>
1 16893 7058 9835
2 5090 2748 2343
3 34569 21380 13189
4 18875 10380 8495
5267879132289135591
6 6468 5109 1358
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & V1 & V2 & V3\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 16893 & 7058 & 9835\\\\\n", "\t2 & 5090 & 2748 & 2343\\\\\n", "\t3 & 34569 & 21380 & 13189\\\\\n", "\t4 & 18875 & 10380 & 8495\\\\\n", "\t5 & 267879 & 132289 & 135591\\\\\n", "\t6 & 6468 & 5109 & 1358\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| | V1 <dbl> | V2 <dbl> | V3 <dbl> |\n", "|---|---|---|---|\n", "| 1 | 16893 | 7058 | 9835 |\n", "| 2 | 5090 | 2748 | 2343 |\n", "| 3 | 34569 | 21380 | 13189 |\n", "| 4 | 18875 | 10380 | 8495 |\n", "| 5 | 267879 | 132289 | 135591 |\n", "| 6 | 6468 | 5109 | 1358 |\n", "\n" ], "text/plain": [ " V1 V2 V3 \n", "1 16893 7058 9835\n", "2 5090 2748 2343\n", "3 34569 21380 13189\n", "4 18875 10380 8495\n", "5 267879 132289 135591\n", "6 6468 5109 1358" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for (i in 1:3) {\n", "\n", "countr_tab2_1_1 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PT\",\"SI\",\"SK\",\"FI\",\"SE\",\"UK\",\"IS\",\"LI\",\"NO\",\"CH\",\"ME\",\"MK\",\"RS\",\"TR\",\"XK\")\n", "data_2_col1_1 <- subset(data_2, \n", " data_2$geo %in% countr_tab2_1_1 & \n", " data_2$year==2018 &\n", " data_2$purpose==\"TOTAL\"&\n", " data_2$duration==duration_vec[i]&\n", " data_2$unit==\"THS\" &\n", " data_2$partner==\"WORLD\")\n", "data_2_col1_1 <- data_2_col1_1[,c(5,7)]\n", "\n", "countr_tab2_1_2 <- c(\"PL\",\"RO\")\n", "data_2_col1_2 <- subset(data_2, \n", " data_2$geo %in% countr_tab2_1_2 & \n", " data_2$year==2017 &\n", " data_2$purpose==\"TOTAL\"&\n", " data_2$duration==duration_vec[i]&\n", " data_2$unit==\"THS\" &\n", " data_2$partner==\"WORLD\")\n", "data_2_col1_2 <- data_2_col1_2[,c(5,7)]\n", "\n", "data_2_col1_3 <- data.frame( geo = \"UK\", values= NA)\n", "\n", "data_tab2_col1 <- rbind(data_2_col1_1,data_2_col1_2,data_2_col1_3)\n", "\n", "data_tab2_col1 <- data_tab2_col1[order(factor(data_tab2_col1$geo, levels= countr_0)),]\n", "\n", "data_tab2_col1$values <- round(data_tab2_col1$values,0)\n", "data_tab2_col1\n", " \n", "# print(data_tab2_col1) \n", "\n", "data_tab2_part1[,i] <- data_tab2_col1[,2]\n", " \n", " }\n", "\n", "head(data_tab2_part1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a column with the names of the countries (`countr_names2`), this will be the first column of the complete table 2. Add this column to the three calculated columns from above: `table2_vers1`" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
countr_names2V1V2V3
<fct><dbl><dbl><dbl>
1Belgium 16893 7058 9835
2Bulgaria 5090 2748 2343
3Czechia 34569 21380 13189
4Denmark 18875 10380 8495
5Germany 267879132289135591
6Estonia 6468 5109 1358
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & countr\\_names2 & V1 & V2 & V3\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & Belgium & 16893 & 7058 & 9835\\\\\n", "\t2 & Bulgaria & 5090 & 2748 & 2343\\\\\n", "\t3 & Czechia & 34569 & 21380 & 13189\\\\\n", "\t4 & Denmark & 18875 & 10380 & 8495\\\\\n", "\t5 & Germany & 267879 & 132289 & 135591\\\\\n", "\t6 & Estonia & 6468 & 5109 & 1358\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | countr_names2 <fct> | V1 <dbl> | V2 <dbl> | V3 <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | Belgium | 16893 | 7058 | 9835 |\n", "| 2 | Bulgaria | 5090 | 2748 | 2343 |\n", "| 3 | Czechia | 34569 | 21380 | 13189 |\n", "| 4 | Denmark | 18875 | 10380 | 8495 |\n", "| 5 | Germany | 267879 | 132289 | 135591 |\n", "| 6 | Estonia | 6468 | 5109 | 1358 |\n", "\n" ], "text/plain": [ " countr_names2 V1 V2 V3 \n", "1 Belgium 16893 7058 9835\n", "2 Bulgaria 5090 2748 2343\n", "3 Czechia 34569 21380 13189\n", "4 Denmark 18875 10380 8495\n", "5 Germany 267879 132289 135591\n", "6 Estonia 6468 5109 1358" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "countr_names2 <- c(\"Belgium\",\"Bulgaria\",\"Czechia\",\"Denmark\",\"Germany\",\"Estonia\", \"Ireland\", \"Greece\", \"Spain\", \"France\", \n", " \"Croatia\", \"Italy\", \"Cyprus\", \"Latvia\", \"Lithuania\", \"Luxembourg\", \"Hungary\", \"Malta\", \"Netherlands\", \n", " \"Austria\", \"Poland\", \"Portugal\", \"Romania\", \"Slovenia\", \"Slovakia\", \"Finland\", \"Sweden\",\n", " \"United Kingdom\", \"Norway\", \"Switzerland\")\n", "\n", "table2_vers1 <- cbind(countr_names2, data_tab2_part1)\n", "head(table2_vers1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Continuing with column four and five, which are based on the same dataset like column one to three (`data_2`), but with different arguments.\n", "- due to the different arguments we need another for-loop, but basically we have the same process\n", "- create again a vector with the essential difference between the two columns: `duration_vec2`\n", "- create an empty data frame, which we can fill while running the loop: `data_tab2_part2`\n", "\n", "We want to calculate the ratio, also for the EU (this we will do later on).\n", "- we need therefore the sum of the column we will create here\n", "- we will save the two sums in a vector called `EU_tab2_sum1` (first we create the empty vector and assign the sums during the loop)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "duration_vec2 <- c(\"N1-3\",\"N_GE4\")\n", "\n", "data_tab2_part2 <- as.data.frame(matrix(ncol = 2, nrow = 30))\n", " \n", "EU_tab2_sum1 <- as.data.frame(matrix(ncol = 2, nrow = 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Within the loop create several subsets\n", "- first subset `data_2_col2_1`: exclude the countries (`countr_tab2_1_1`), which need data from 2017\n", "- for Poland and Romania in `countr_tab2_1_2` we need data from 2017. Assign in `data_2_col2_2`\n", "- for United Kingdom we have NA. Assign in `data_2_col2_3`\n", "- we match afterwards the three calculated subsets in every step within the loop in `data_tab2_col2`\n", "- bring the country names in the order the table shows (we can use the vector `countr_0` from beginning)\n", "\n", "Within the loop calculate the ratio for each country and a sum for the EU-value\n", "- create a vector, which indicates due to FALSE and TRUE at the exact position, which countries of data_tab2_col3 are within EU_27: `EU_27_prep_col3`\n", "- by using the sapply-command we can calculate the sum: `tab_EU3_col3`\n", "- build the ratio we need (the column just calculated devided by the first column from above (all trips))\n", "- then we assign the ratios to the empty data frame we create before: `data_tab2_part2`" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
V1V2
<dbl><dbl>
115.7 6.8
248.033.5
355.723.0
444.019.2
540.019.5
663.5 4.5
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & V1 & V2\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 15.7 & 6.8\\\\\n", "\t2 & 48.0 & 33.5\\\\\n", "\t3 & 55.7 & 23.0\\\\\n", "\t4 & 44.0 & 19.2\\\\\n", "\t5 & 40.0 & 19.5\\\\\n", "\t6 & 63.5 & 4.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | V1 <dbl> | V2 <dbl> |\n", "|---|---|---|\n", "| 1 | 15.7 | 6.8 |\n", "| 2 | 48.0 | 33.5 |\n", "| 3 | 55.7 | 23.0 |\n", "| 4 | 44.0 | 19.2 |\n", "| 5 | 40.0 | 19.5 |\n", "| 6 | 63.5 | 4.5 |\n", "\n" ], "text/plain": [ " V1 V2 \n", "1 15.7 6.8\n", "2 48.0 33.5\n", "3 55.7 23.0\n", "4 44.0 19.2\n", "5 40.0 19.5\n", "6 63.5 4.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for (i in 1:2) {\n", "\n", "countr_tab2_1_1 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PT\",\"SI\",\"SK\",\"FI\",\"SE\",\"UK\",\"IS\",\"LI\",\"NO\",\"CH\",\"ME\",\"MK\",\"RS\",\"TR\",\"XK\")\n", "data_2_col2_1 <- subset(data_2, \n", " data_2$geo %in% countr_tab2_1_1 & \n", " data_2$year==2018 &\n", " data_2$purpose==\"TOTAL\"&\n", " data_2$duration==duration_vec2[i]&\n", " data_2$unit==\"THS\" &\n", " data_2$partner==\"DOM\")\n", "data_2_col2_1 <- data_2_col2_1[,c(5,7)]\n", "\n", "countr_tab2_1_2 <- c(\"PL\",\"RO\")\n", "data_2_col2_2 <- subset(data_2, \n", " data_2$geo %in% countr_tab2_1_2 & \n", " data_2$year==2017 &\n", " data_2$purpose==\"TOTAL\"&\n", " data_2$duration==duration_vec2[i]&\n", " data_2$unit==\"THS\" &\n", " data_2$partner==\"DOM\")\n", "data_2_col2_2 <- data_2_col2_2[,c(5,7)]\n", "\n", "data_2_col2_3 <- data.frame( geo = \"UK\", values= NA)\n", " \n", "data_tab2_col2 <- rbind(data_2_col2_1,data_2_col2_2,data_2_col2_3)\n", "\n", "data_tab2_col2 <- data_tab2_col2[order(factor(data_tab2_col2$geo, levels= countr_0)),]\n", " \n", "data_tab2_col2$values <- round(data_tab2_col2$values,0)\n", "\n", "EU_27_prep_col3 <- data_tab2_col2$geo %in% EU_27\n", " \n", "tab_EU3_col3 <- sapply(data_tab2_col2[EU_27_prep_col3,2],sum)\n", "EU_tab2_sum1[,i] <- tab_EU3_col3\n", " \n", "data_tab2_part2[,i] <- round(data_tab2_col2[,2]/table2_vers1[,2]*100,1)\n", " \n", " }\n", "\n", "head(data_tab2_part2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Third step\n", "\n", "Continue with column six and seven, which are based on the same data like column four to five, the only difference is the variable \"partner\"." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "duration_vec2 <- c(\"N1-3\",\"N_GE4\")\n", "\n", "data_tab2_part3 <- as.data.frame(matrix(ncol = 2, nrow = 30))\n", "\n", "EU_tab2_sum2 <- as.data.frame(matrix(ncol = 2, nrow = 1))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
V1V2
<dbl><dbl>
126.151.4
2 6.012.5
3 6.215.2
411.025.8
5 9.431.1
615.516.5
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & V1 & V2\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 26.1 & 51.4\\\\\n", "\t2 & 6.0 & 12.5\\\\\n", "\t3 & 6.2 & 15.2\\\\\n", "\t4 & 11.0 & 25.8\\\\\n", "\t5 & 9.4 & 31.1\\\\\n", "\t6 & 15.5 & 16.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | V1 <dbl> | V2 <dbl> |\n", "|---|---|---|\n", "| 1 | 26.1 | 51.4 |\n", "| 2 | 6.0 | 12.5 |\n", "| 3 | 6.2 | 15.2 |\n", "| 4 | 11.0 | 25.8 |\n", "| 5 | 9.4 | 31.1 |\n", "| 6 | 15.5 | 16.5 |\n", "\n" ], "text/plain": [ " V1 V2 \n", "1 26.1 51.4\n", "2 6.0 12.5\n", "3 6.2 15.2\n", "4 11.0 25.8\n", "5 9.4 31.1\n", "6 15.5 16.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for (i in 1:2) {\n", "\n", "countr_tab2_1_1 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PT\",\"SI\",\"SK\",\"FI\",\"SE\",\"UK\",\"IS\",\"LI\",\"NO\",\"CH\",\"ME\",\"MK\",\"RS\",\"TR\",\"XK\")\n", "data_2_col3_1 <- subset(data_2, \n", " data_2$geo %in% countr_tab2_1_1 & \n", " data_2$year==2018 &\n", " data_2$purpose==\"TOTAL\"&\n", " data_2$duration==duration_vec2[i]&\n", " data_2$unit==\"THS\" &\n", " data_2$partner==\"OUT\") #the only difference to column four and five\n", "data_2_col3_1 <- data_2_col3_1[,c(5,7)]\n", "\n", "countr_tab2_1_2 <- c(\"PL\",\"RO\")\n", "data_2_col3_2 <- subset(data_2, \n", " data_2$geo %in% countr_tab2_1_2 & \n", " data_2$year==2017 &\n", " data_2$purpose==\"TOTAL\"&\n", " data_2$duration==duration_vec2[i]&\n", " data_2$unit==\"THS\" &\n", " data_2$partner==\"OUT\") #the only difference to column four and five\n", "data_2_col3_2 <- data_2_col3_2[,c(5,7)]\n", "\n", "data_2_col3_3 <- data.frame( geo = \"UK\", values= NA)\n", " \n", "data_tab2_col3 <- rbind(data_2_col3_1,data_2_col3_2,data_2_col3_3)\n", "\n", "data_tab2_col3 <- data_tab2_col3[order(factor(data_tab2_col3$geo, levels= countr_0)),]\n", " \n", "data_tab2_col3$values <- round(data_tab2_col3$values,0)\n", "\n", "EU_27_prep_col3 <- data_tab2_col3$geo %in% EU_27\n", "tab_EU3_col3 <- sapply(data_tab2_col3[EU_27_prep_col3,2],sum)\n", "EU_tab2_sum2[,i] <- tab_EU3_col3\n", " \n", "data_tab2_part3[,i] <- round(data_tab2_col3[,2]/table2_vers1[,2]*100,1)\n", " \n", " }\n", "\n", "head(data_tab2_part3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Match `table2_vers1` with the remaining columns (i.e. column 4-7; `data_tab2_part2`,`data_tab2_part3`) to `table2_vers2`" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 8
countr_names2V1V2V3V1V2V1V2
<fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1Belgium 16893 7058 983515.7 6.826.151.4
2Bulgaria 5090 2748 234348.033.5 6.012.5
3Czechia 34569 21380 1318955.723.0 6.215.2
4Denmark 18875 10380 849544.019.211.025.8
5Germany 26787913228913559140.019.5 9.431.1
6Estonia 6468 5109 135863.5 4.515.516.5
\n" ], "text/latex": [ "A data.frame: 6 × 8\n", "\\begin{tabular}{r|llllllll}\n", " & countr\\_names2 & V1 & V2 & V3 & V1 & V2 & V1 & V2\\\\\n", " & & & & & & & & \\\\\n", "\\hline\n", "\t1 & Belgium & 16893 & 7058 & 9835 & 15.7 & 6.8 & 26.1 & 51.4\\\\\n", "\t2 & Bulgaria & 5090 & 2748 & 2343 & 48.0 & 33.5 & 6.0 & 12.5\\\\\n", "\t3 & Czechia & 34569 & 21380 & 13189 & 55.7 & 23.0 & 6.2 & 15.2\\\\\n", "\t4 & Denmark & 18875 & 10380 & 8495 & 44.0 & 19.2 & 11.0 & 25.8\\\\\n", "\t5 & Germany & 267879 & 132289 & 135591 & 40.0 & 19.5 & 9.4 & 31.1\\\\\n", "\t6 & Estonia & 6468 & 5109 & 1358 & 63.5 & 4.5 & 15.5 & 16.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 8\n", "\n", "| | countr_names2 <fct> | V1 <dbl> | V2 <dbl> | V3 <dbl> | V1 <dbl> | V2 <dbl> | V1 <dbl> | V2 <dbl> |\n", "|---|---|---|---|---|---|---|---|---|\n", "| 1 | Belgium | 16893 | 7058 | 9835 | 15.7 | 6.8 | 26.1 | 51.4 |\n", "| 2 | Bulgaria | 5090 | 2748 | 2343 | 48.0 | 33.5 | 6.0 | 12.5 |\n", "| 3 | Czechia | 34569 | 21380 | 13189 | 55.7 | 23.0 | 6.2 | 15.2 |\n", "| 4 | Denmark | 18875 | 10380 | 8495 | 44.0 | 19.2 | 11.0 | 25.8 |\n", "| 5 | Germany | 267879 | 132289 | 135591 | 40.0 | 19.5 | 9.4 | 31.1 |\n", "| 6 | Estonia | 6468 | 5109 | 1358 | 63.5 | 4.5 | 15.5 | 16.5 |\n", "\n" ], "text/plain": [ " countr_names2 V1 V2 V3 V1 V2 V1 V2 \n", "1 Belgium 16893 7058 9835 15.7 6.8 26.1 51.4\n", "2 Bulgaria 5090 2748 2343 48.0 33.5 6.0 12.5\n", "3 Czechia 34569 21380 13189 55.7 23.0 6.2 15.2\n", "4 Denmark 18875 10380 8495 44.0 19.2 11.0 25.8\n", "5 Germany 267879 132289 135591 40.0 19.5 9.4 31.1\n", "6 Estonia 6468 5109 1358 63.5 4.5 15.5 16.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table2_vers2 <- cbind(table2_vers1, data_tab2_part2, data_tab2_part3)\n", "\n", "head(table2_vers2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fourth step\n", "\n", "Now we add the last column, which has another dataset `data_3`\n", "- load the dataset first\n", "- again we add a year-column" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "data_3 <- as_tibble(get_eurostat_data(id=\"tour_dem_totot\"))\n", "\n", "data_3$year <- as.integer(substr(data_3$time,1,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exclude the countries, which need data from 2016 and 2017 (`countr_tab2_2_1`). Build the subset `data_2_col4_1`" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "countr_tab2_2_1 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"IS\",\"LI\",\"NO\",\"CH\",\"ME\",\"RS\",\"TR\")\n", "data_2_col4_1 <- subset(data_3, \n", " data_3$geo %in% countr_tab2_2_1 & \n", " data_3$year==2018 &\n", " data_3$duration==\"N_GE1\"&\n", " data_3$unit==\"PC_POP\"&\n", " data_3$partner==\"WORLD\")\n", "data_2_col4_1 <- data_2_col4_1[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For United Kingdom (`countr_tab2_2_2`) we need data from 2016. Build the subset `data_2_col4_2`" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "countr_tab2_2_2 <- c(\"UK\")\n", "data_2_col4_2 <- subset(data_3, \n", " data_3$geo %in% countr_tab2_2_2 & \n", " data_3$year==2016 &\n", " data_3$duration==\"N_GE1\"&\n", " data_3$unit==\"PC_POP\"&\n", " data_3$partner==\"WORLD\")\n", "data_2_col4_2 <- data_2_col4_2[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Austria and Latvia (`countr_tab2_2_3`) we need data from 2017. Build the subset `data_2_col4_3`" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "countr_tab2_2_3 <- c(\"LV\",\"AT\")\n", "data_2_col4_3 <- subset(data_3, \n", " data_3$geo %in% countr_tab2_2_3 & \n", " data_3$year==2017 &\n", " data_3$duration==\"N_GE1\"&\n", " data_3$unit==\"PC_POP\"&\n", " data_3$partner==\"WORLD\")\n", "data_2_col4_3 <- data_2_col4_3[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- match the subsets to `data_tab2_part4`\n", "- bring the country names in the order the table shows (use `countr_0`)\n", "\n", "- here we can keep the country shortcuts, because we need them to calculate the EU-value later \n", "- by matching the last column to `table2_vers2`, we can delete the shortcut column, because we need the full names here: `table2_vers3`" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 9
countr_names2V1V2V3V1V2V1V2values
<fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1Belgium 16893 7058 983515.7 6.826.151.467.7
2Bulgaria 5090 2748 234348.033.5 6.012.533.8
3Czechia 34569 21380 1318955.723.0 6.215.278.1
4Denmark 18875 10380 849544.019.211.025.869.9
5Germany 26787913228913559140.019.5 9.431.175.3
6Estonia 6468 5109 135863.5 4.515.516.579.5
\n" ], "text/latex": [ "A data.frame: 6 × 9\n", "\\begin{tabular}{r|lllllllll}\n", " & countr\\_names2 & V1 & V2 & V3 & V1 & V2 & V1 & V2 & values\\\\\n", " & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & Belgium & 16893 & 7058 & 9835 & 15.7 & 6.8 & 26.1 & 51.4 & 67.7\\\\\n", "\t2 & Bulgaria & 5090 & 2748 & 2343 & 48.0 & 33.5 & 6.0 & 12.5 & 33.8\\\\\n", "\t3 & Czechia & 34569 & 21380 & 13189 & 55.7 & 23.0 & 6.2 & 15.2 & 78.1\\\\\n", "\t4 & Denmark & 18875 & 10380 & 8495 & 44.0 & 19.2 & 11.0 & 25.8 & 69.9\\\\\n", "\t5 & Germany & 267879 & 132289 & 135591 & 40.0 & 19.5 & 9.4 & 31.1 & 75.3\\\\\n", "\t6 & Estonia & 6468 & 5109 & 1358 & 63.5 & 4.5 & 15.5 & 16.5 & 79.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 9\n", "\n", "| | countr_names2 <fct> | V1 <dbl> | V2 <dbl> | V3 <dbl> | V1 <dbl> | V2 <dbl> | V1 <dbl> | V2 <dbl> | values <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | Belgium | 16893 | 7058 | 9835 | 15.7 | 6.8 | 26.1 | 51.4 | 67.7 |\n", "| 2 | Bulgaria | 5090 | 2748 | 2343 | 48.0 | 33.5 | 6.0 | 12.5 | 33.8 |\n", "| 3 | Czechia | 34569 | 21380 | 13189 | 55.7 | 23.0 | 6.2 | 15.2 | 78.1 |\n", "| 4 | Denmark | 18875 | 10380 | 8495 | 44.0 | 19.2 | 11.0 | 25.8 | 69.9 |\n", "| 5 | Germany | 267879 | 132289 | 135591 | 40.0 | 19.5 | 9.4 | 31.1 | 75.3 |\n", "| 6 | Estonia | 6468 | 5109 | 1358 | 63.5 | 4.5 | 15.5 | 16.5 | 79.5 |\n", "\n" ], "text/plain": [ " countr_names2 V1 V2 V3 V1 V2 V1 V2 values\n", "1 Belgium 16893 7058 9835 15.7 6.8 26.1 51.4 67.7 \n", "2 Bulgaria 5090 2748 2343 48.0 33.5 6.0 12.5 33.8 \n", "3 Czechia 34569 21380 13189 55.7 23.0 6.2 15.2 78.1 \n", "4 Denmark 18875 10380 8495 44.0 19.2 11.0 25.8 69.9 \n", "5 Germany 267879 132289 135591 40.0 19.5 9.4 31.1 75.3 \n", "6 Estonia 6468 5109 1358 63.5 4.5 15.5 16.5 79.5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab2_part4 <- rbind(data_2_col4_1, data_2_col4_2, data_2_col4_3)\n", "\n", "data_tab2_part4 <- data_tab2_part4[order(factor(data_tab2_part4$geo, levels= countr_0)),]\n", "\n", "data_tab2_part4$values <- round(data_tab2_part4$values, 1)\n", "\n", "table2_vers3 <- cbind(table2_vers2, data_tab2_part4[,2])\n", "\n", "head(table2_vers3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fifth step\n", "\n", "Calculate the EU value\n", "- we need one time again the table `table2_vers3` with shortcuts instead of full names to exclude the not-EU countries by using the `EU_27` vector.\n", "- remember that by calculating the last column to `table2_vers3` we keeped the shortcuts for the countries: \"data_tab2_part4[,1]\" --> this are the shortcuts\n", "- add this to `table2_vers3`" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "tab_prep3 <- cbind(data_tab2_part4[,1], table2_vers3[,2:9])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- which countries of `tab_prep3` are within `EU_27`: `EU_27_prep3`\n", "- by using the sapply-command we can calculate the sum of the column one to three of the table `table2_vers3`: `tab_EU3_1`" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "EU_27_prep3 <- tab_prep3$geo %in% EU_27\n", "\n", "tab_EU3_1 <- sapply(table2_vers3[EU_27_prep3,2:4],sum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- for column four to seven we have to calculate the ratio\n", "- we already calculated the sum above: `EU_tab2_sum1` and `EU_tab2_sum2`\n", "- we have to devide this to the sum of \"all trips\": \"tab_EU3_1[1]\"\n", "- for the last column we have to take the average, because there is no suitable data in the dataset" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "tab_EU3_2 <- round(EU_tab2_sum1/tab_EU3_1[1]*100,1)\n", "tab_EU3_3 <- round(EU_tab2_sum2/tab_EU3_1[1]*100,1)\n", "\n", "tab_EU3_4 <- round(mean(table2_vers3[EU_27_prep3,9]),1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Match all EU-values in `tab_EU3`\n", "- at this stage we get the warning message, that one NA for \"EU-27\" get generated: we solve this problem two steps later\n", "- put the summarized values to the calculated table from above: `table2_final`\n", "- give the columns the correct names" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in eval(expr, envir, enclos):\n", "“NAs introduced by coercion”\n" ] } ], "source": [ "tab_EU3 <- as.numeric(c(\"EU-27\", tab_EU3_1, tab_EU3_2, tab_EU3_3, tab_EU3_4))\n", "\n", "table2_final <- rbind(tab_EU3,table2_vers3)\n", "\n", "names(table2_final) <- c(\"\",\n", " \"All trips\", \n", " \"Short trips (1-3 nights)\",\n", " \"Long trips (4+ nights)\",\n", " \"Short domestic trips (1-3 nights)\",\n", " \"Long domestic trips (4+ nights)\",\n", " \"Short outbound trips (1-3 nights)\",\n", " \"Long outbound trips (4+ nights)\",\n", " \"Share of the population (aged 15+) taking part in tourism trips for personal purposes (%)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- we have to convert the class, because we have to add \"EU-27\" and this only works by converting it from a factor first, then doing the change and re-convert it back" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 31 × 9 of type chr
All tripsShort trips (1-3 nights)Long trips (4+ nights)Short domestic trips (1-3 nights)Long domestic trips (4+ nights)Short outbound trips (1-3 nights)Long outbound trips (4+ nights)Share of the population (aged 15+) taking part in tourism trips for personal purposes (%)
EU-27 113062261956251106346.024.5 8.820.765.1
Belgium 16893 7058 983515.7 6.826.151.467.7
Bulgaria 5090 2748 234348.033.5 6.012.533.8
Czechia 34569 21380 1318955.723.0 6.215.278.1
Denmark 18875 10380 849544.019.211.025.869.9
Germany 26787913228913559140.019.5 9.431.175.3
Estonia 6468 5109 135863.5 4.515.516.579.5
Ireland 15941 10110 583145.9 8.017.528.674.7
Greece 6565 1834 473125.061.7 2.910.442.8
Spain 156370110284 4608666.922.6 3.6 6.967.4
France 220225 9840312182237.338.5 7.416.974.3
Croatia 5255 2754 250131.928.920.518.646.5
Italy 68186 35285 3290145.133.9 6.714.444.9
Cyprus 3001 1717 128444.7 7.512.535.369.5
Latvia 3952 2952 100058.6 6.716.018.657.7
Lithuania 4885 2925 196045.1 9.014.831.157.3
Luxembourg 2555 1167 1388 1.5 0.344.254.081.1
Hungary 19230 12506 672446.215.818.919.257.6
Malta 874 391 48323.2 4.921.650.365.5
Netherlands 44551 21955 2259637.315.912.034.985.0
Austria 24391 13395 1099736.814.418.130.776.6
Poland 57910 30364 2754648.331.1 4.116.561.9
Portugal 18298 12846 545266.321.8 3.9 8.044.8
Romania 17902 11291 661262.331.7 0.8 5.227.0
Slovenia 5191 3203 198832.1 6.529.631.869.3
Slovakia 12423 7497 492645.616.914.822.873.2
Finland 38602 27837 1076560.415.011.712.991.0
Sweden 54541 31882 2265947.218.311.323.384.9
United KingdomNA NA NA NA NA NA NA 64.1
Norway 27084 17813 927053.616.512.217.792.5
Switzerland 21946 9605 1234119.610.824.245.589.8
\n" ], "text/latex": [ "A matrix: 31 × 9 of type chr\n", "\\begin{tabular}{lllllllll}\n", " & All trips & Short trips (1-3 nights) & Long trips (4+ nights) & Short domestic trips (1-3 nights) & Long domestic trips (4+ nights) & Short outbound trips (1-3 nights) & Long outbound trips (4+ nights) & Share of the population (aged 15+) taking part in tourism trips for personal purposes (\\%)\\\\\n", "\\hline\n", "\t EU-27 & 1130622 & 619562 & 511063 & 46.0 & 24.5 & 8.8 & 20.7 & 65.1\\\\\n", "\t Belgium & 16893 & 7058 & 9835 & 15.7 & 6.8 & 26.1 & 51.4 & 67.7\\\\\n", "\t Bulgaria & 5090 & 2748 & 2343 & 48.0 & 33.5 & 6.0 & 12.5 & 33.8\\\\\n", "\t Czechia & 34569 & 21380 & 13189 & 55.7 & 23.0 & 6.2 & 15.2 & 78.1\\\\\n", "\t Denmark & 18875 & 10380 & 8495 & 44.0 & 19.2 & 11.0 & 25.8 & 69.9\\\\\n", "\t Germany & 267879 & 132289 & 135591 & 40.0 & 19.5 & 9.4 & 31.1 & 75.3\\\\\n", "\t Estonia & 6468 & 5109 & 1358 & 63.5 & 4.5 & 15.5 & 16.5 & 79.5\\\\\n", "\t Ireland & 15941 & 10110 & 5831 & 45.9 & 8.0 & 17.5 & 28.6 & 74.7\\\\\n", "\t Greece & 6565 & 1834 & 4731 & 25.0 & 61.7 & 2.9 & 10.4 & 42.8\\\\\n", "\t Spain & 156370 & 110284 & 46086 & 66.9 & 22.6 & 3.6 & 6.9 & 67.4\\\\\n", "\t France & 220225 & 98403 & 121822 & 37.3 & 38.5 & 7.4 & 16.9 & 74.3\\\\\n", "\t Croatia & 5255 & 2754 & 2501 & 31.9 & 28.9 & 20.5 & 18.6 & 46.5\\\\\n", "\t Italy & 68186 & 35285 & 32901 & 45.1 & 33.9 & 6.7 & 14.4 & 44.9\\\\\n", "\t Cyprus & 3001 & 1717 & 1284 & 44.7 & 7.5 & 12.5 & 35.3 & 69.5\\\\\n", "\t Latvia & 3952 & 2952 & 1000 & 58.6 & 6.7 & 16.0 & 18.6 & 57.7\\\\\n", "\t Lithuania & 4885 & 2925 & 1960 & 45.1 & 9.0 & 14.8 & 31.1 & 57.3\\\\\n", "\t Luxembourg & 2555 & 1167 & 1388 & 1.5 & 0.3 & 44.2 & 54.0 & 81.1\\\\\n", "\t Hungary & 19230 & 12506 & 6724 & 46.2 & 15.8 & 18.9 & 19.2 & 57.6\\\\\n", "\t Malta & 874 & 391 & 483 & 23.2 & 4.9 & 21.6 & 50.3 & 65.5\\\\\n", "\t Netherlands & 44551 & 21955 & 22596 & 37.3 & 15.9 & 12.0 & 34.9 & 85.0\\\\\n", "\t Austria & 24391 & 13395 & 10997 & 36.8 & 14.4 & 18.1 & 30.7 & 76.6\\\\\n", "\t Poland & 57910 & 30364 & 27546 & 48.3 & 31.1 & 4.1 & 16.5 & 61.9\\\\\n", "\t Portugal & 18298 & 12846 & 5452 & 66.3 & 21.8 & 3.9 & 8.0 & 44.8\\\\\n", "\t Romania & 17902 & 11291 & 6612 & 62.3 & 31.7 & 0.8 & 5.2 & 27.0\\\\\n", "\t Slovenia & 5191 & 3203 & 1988 & 32.1 & 6.5 & 29.6 & 31.8 & 69.3\\\\\n", "\t Slovakia & 12423 & 7497 & 4926 & 45.6 & 16.9 & 14.8 & 22.8 & 73.2\\\\\n", "\t Finland & 38602 & 27837 & 10765 & 60.4 & 15.0 & 11.7 & 12.9 & 91.0\\\\\n", "\t Sweden & 54541 & 31882 & 22659 & 47.2 & 18.3 & 11.3 & 23.3 & 84.9\\\\\n", "\t United Kingdom & NA & NA & NA & NA & NA & NA & NA & 64.1\\\\\n", "\t Norway & 27084 & 17813 & 9270 & 53.6 & 16.5 & 12.2 & 17.7 & 92.5\\\\\n", "\t Switzerland & 21946 & 9605 & 12341 & 19.6 & 10.8 & 24.2 & 45.5 & 89.8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 31 × 9 of type chr\n", "\n", "| | All trips | Short trips (1-3 nights) | Long trips (4+ nights) | Short domestic trips (1-3 nights) | Long domestic trips (4+ nights) | Short outbound trips (1-3 nights) | Long outbound trips (4+ nights) | Share of the population (aged 15+) taking part in tourism trips for personal purposes (%) |\n", "|---|---|---|---|---|---|---|---|---|\n", "| EU-27 | 1130622 | 619562 | 511063 | 46.0 | 24.5 | 8.8 | 20.7 | 65.1 |\n", "| Belgium | 16893 | 7058 | 9835 | 15.7 | 6.8 | 26.1 | 51.4 | 67.7 |\n", "| Bulgaria | 5090 | 2748 | 2343 | 48.0 | 33.5 | 6.0 | 12.5 | 33.8 |\n", "| Czechia | 34569 | 21380 | 13189 | 55.7 | 23.0 | 6.2 | 15.2 | 78.1 |\n", "| Denmark | 18875 | 10380 | 8495 | 44.0 | 19.2 | 11.0 | 25.8 | 69.9 |\n", "| Germany | 267879 | 132289 | 135591 | 40.0 | 19.5 | 9.4 | 31.1 | 75.3 |\n", "| Estonia | 6468 | 5109 | 1358 | 63.5 | 4.5 | 15.5 | 16.5 | 79.5 |\n", "| Ireland | 15941 | 10110 | 5831 | 45.9 | 8.0 | 17.5 | 28.6 | 74.7 |\n", "| Greece | 6565 | 1834 | 4731 | 25.0 | 61.7 | 2.9 | 10.4 | 42.8 |\n", "| Spain | 156370 | 110284 | 46086 | 66.9 | 22.6 | 3.6 | 6.9 | 67.4 |\n", "| France | 220225 | 98403 | 121822 | 37.3 | 38.5 | 7.4 | 16.9 | 74.3 |\n", "| Croatia | 5255 | 2754 | 2501 | 31.9 | 28.9 | 20.5 | 18.6 | 46.5 |\n", "| Italy | 68186 | 35285 | 32901 | 45.1 | 33.9 | 6.7 | 14.4 | 44.9 |\n", "| Cyprus | 3001 | 1717 | 1284 | 44.7 | 7.5 | 12.5 | 35.3 | 69.5 |\n", "| Latvia | 3952 | 2952 | 1000 | 58.6 | 6.7 | 16.0 | 18.6 | 57.7 |\n", "| Lithuania | 4885 | 2925 | 1960 | 45.1 | 9.0 | 14.8 | 31.1 | 57.3 |\n", "| Luxembourg | 2555 | 1167 | 1388 | 1.5 | 0.3 | 44.2 | 54.0 | 81.1 |\n", "| Hungary | 19230 | 12506 | 6724 | 46.2 | 15.8 | 18.9 | 19.2 | 57.6 |\n", "| Malta | 874 | 391 | 483 | 23.2 | 4.9 | 21.6 | 50.3 | 65.5 |\n", "| Netherlands | 44551 | 21955 | 22596 | 37.3 | 15.9 | 12.0 | 34.9 | 85.0 |\n", "| Austria | 24391 | 13395 | 10997 | 36.8 | 14.4 | 18.1 | 30.7 | 76.6 |\n", "| Poland | 57910 | 30364 | 27546 | 48.3 | 31.1 | 4.1 | 16.5 | 61.9 |\n", "| Portugal | 18298 | 12846 | 5452 | 66.3 | 21.8 | 3.9 | 8.0 | 44.8 |\n", "| Romania | 17902 | 11291 | 6612 | 62.3 | 31.7 | 0.8 | 5.2 | 27.0 |\n", "| Slovenia | 5191 | 3203 | 1988 | 32.1 | 6.5 | 29.6 | 31.8 | 69.3 |\n", "| Slovakia | 12423 | 7497 | 4926 | 45.6 | 16.9 | 14.8 | 22.8 | 73.2 |\n", "| Finland | 38602 | 27837 | 10765 | 60.4 | 15.0 | 11.7 | 12.9 | 91.0 |\n", "| Sweden | 54541 | 31882 | 22659 | 47.2 | 18.3 | 11.3 | 23.3 | 84.9 |\n", "| United Kingdom | NA | NA | NA | NA | NA | NA | NA | 64.1 |\n", "| Norway | 27084 | 17813 | 9270 | 53.6 | 16.5 | 12.2 | 17.7 | 92.5 |\n", "| Switzerland | 21946 | 9605 | 12341 | 19.6 | 10.8 | 24.2 | 45.5 | 89.8 |\n", "\n" ], "text/plain": [ " All trips Short trips (1-3 nights) Long trips (4+ nights)\n", " [1,] EU-27 1130622 619562 511063 \n", " [2,] Belgium 16893 7058 9835 \n", " [3,] Bulgaria 5090 2748 2343 \n", " [4,] Czechia 34569 21380 13189 \n", " [5,] Denmark 18875 10380 8495 \n", " [6,] Germany 267879 132289 135591 \n", " [7,] Estonia 6468 5109 1358 \n", " [8,] Ireland 15941 10110 5831 \n", " [9,] Greece 6565 1834 4731 \n", "[10,] Spain 156370 110284 46086 \n", "[11,] France 220225 98403 121822 \n", "[12,] Croatia 5255 2754 2501 \n", "[13,] Italy 68186 35285 32901 \n", "[14,] Cyprus 3001 1717 1284 \n", "[15,] Latvia 3952 2952 1000 \n", "[16,] Lithuania 4885 2925 1960 \n", "[17,] Luxembourg 2555 1167 1388 \n", "[18,] Hungary 19230 12506 6724 \n", "[19,] Malta 874 391 483 \n", "[20,] Netherlands 44551 21955 22596 \n", "[21,] Austria 24391 13395 10997 \n", "[22,] Poland 57910 30364 27546 \n", "[23,] Portugal 18298 12846 5452 \n", "[24,] Romania 17902 11291 6612 \n", "[25,] Slovenia 5191 3203 1988 \n", "[26,] Slovakia 12423 7497 4926 \n", "[27,] Finland 38602 27837 10765 \n", "[28,] Sweden 54541 31882 22659 \n", "[29,] United Kingdom NA NA NA \n", "[30,] Norway 27084 17813 9270 \n", "[31,] Switzerland 21946 9605 12341 \n", " Short domestic trips (1-3 nights) Long domestic trips (4+ nights)\n", " [1,] 46.0 24.5 \n", " [2,] 15.7 6.8 \n", " [3,] 48.0 33.5 \n", " [4,] 55.7 23.0 \n", " [5,] 44.0 19.2 \n", " [6,] 40.0 19.5 \n", " [7,] 63.5 4.5 \n", " [8,] 45.9 8.0 \n", " [9,] 25.0 61.7 \n", "[10,] 66.9 22.6 \n", "[11,] 37.3 38.5 \n", "[12,] 31.9 28.9 \n", "[13,] 45.1 33.9 \n", "[14,] 44.7 7.5 \n", "[15,] 58.6 6.7 \n", "[16,] 45.1 9.0 \n", "[17,] 1.5 0.3 \n", "[18,] 46.2 15.8 \n", "[19,] 23.2 4.9 \n", "[20,] 37.3 15.9 \n", "[21,] 36.8 14.4 \n", "[22,] 48.3 31.1 \n", "[23,] 66.3 21.8 \n", "[24,] 62.3 31.7 \n", "[25,] 32.1 6.5 \n", "[26,] 45.6 16.9 \n", "[27,] 60.4 15.0 \n", "[28,] 47.2 18.3 \n", "[29,] NA NA \n", "[30,] 53.6 16.5 \n", "[31,] 19.6 10.8 \n", " Short outbound trips (1-3 nights) Long outbound trips (4+ nights)\n", " [1,] 8.8 20.7 \n", " [2,] 26.1 51.4 \n", " [3,] 6.0 12.5 \n", " [4,] 6.2 15.2 \n", " [5,] 11.0 25.8 \n", " [6,] 9.4 31.1 \n", " [7,] 15.5 16.5 \n", " [8,] 17.5 28.6 \n", " [9,] 2.9 10.4 \n", "[10,] 3.6 6.9 \n", "[11,] 7.4 16.9 \n", "[12,] 20.5 18.6 \n", "[13,] 6.7 14.4 \n", "[14,] 12.5 35.3 \n", "[15,] 16.0 18.6 \n", "[16,] 14.8 31.1 \n", "[17,] 44.2 54.0 \n", "[18,] 18.9 19.2 \n", "[19,] 21.6 50.3 \n", "[20,] 12.0 34.9 \n", "[21,] 18.1 30.7 \n", "[22,] 4.1 16.5 \n", "[23,] 3.9 8.0 \n", "[24,] 0.8 5.2 \n", "[25,] 29.6 31.8 \n", "[26,] 14.8 22.8 \n", "[27,] 11.7 12.9 \n", "[28,] 11.3 23.3 \n", "[29,] NA NA \n", "[30,] 12.2 17.7 \n", "[31,] 24.2 45.5 \n", " Share of the population (aged 15+) taking part in tourism trips for personal purposes (%)\n", " [1,] 65.1 \n", " [2,] 67.7 \n", " [3,] 33.8 \n", " [4,] 78.1 \n", " [5,] 69.9 \n", " [6,] 75.3 \n", " [7,] 79.5 \n", " [8,] 74.7 \n", " [9,] 42.8 \n", "[10,] 67.4 \n", "[11,] 74.3 \n", "[12,] 46.5 \n", "[13,] 44.9 \n", "[14,] 69.5 \n", "[15,] 57.7 \n", "[16,] 57.3 \n", "[17,] 81.1 \n", "[18,] 57.6 \n", "[19,] 65.5 \n", "[20,] 85.0 \n", "[21,] 76.6 \n", "[22,] 61.9 \n", "[23,] 44.8 \n", "[24,] 27.0 \n", "[25,] 69.3 \n", "[26,] 73.2 \n", "[27,] 91.0 \n", "[28,] 84.9 \n", "[29,] 64.1 \n", "[30,] 92.5 \n", "[31,] 89.8 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table2_final[,1] <- as.character(table2_final[,1])\n", "table2_final[1,1] <- \"EU-27\"\n", "table2_final[,1] <- factor(table2_final[,1])\n", "\n", "as.matrix(table2_final)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tourism participation: More than nine out of ten residents of Finland participated in tourism\n", "\n", "### Figure 5\n", "\n", "#### First step\n", "\n", "We calculated the needed data already for `table2_final` (last column)\n", "- but Albania and North Mazedonia are missing and we have to do the same analysis like above for the both countries " ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "countr_tab2_2_4 <- c(\"AL\",\"MK\")\n", "data_2_col4_4 <- subset(data_3, \n", " data_3$geo %in% countr_tab2_2_4 & \n", " data_3$year==2018 &\n", " data_3$duration==\"N_GE1\"&\n", " data_3$unit==\"PC_POP\"&\n", " data_3$partner==\"WORLD\")\n", "data_2_col4_4 <- data_2_col4_4[,c(4,6)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Bring the column from the table above in the order, the graph shows. We create therefore a new country vector `countr_fig5`\n", "- Match the EU-value \"tab_EU3[9]\", the ordered column from above `data_tab2_part4.1` and the two additional countries `data_2_col4_4` in `data_tab2_part5`" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in `[<-.factor`(`*tmp*`, ri, value = 65.1):\n", "“invalid factor level, NA generated”\n" ] } ], "source": [ "countr_fig5 <- c(\"EU-27\",\"FI\", \"NL\",\"SE\",\"LU\",\"EE\",\"CZ\",\"AT\",\"DE\",\"IE\",\"FR\",\"SK\",\"DK\",\"CY\",\"SI\",\"BE\",\"ES\",\"MT\",\"PL\",\"LV\",\n", " \"HU\",\"LT\",\"HR\",\"IT\",\"PT\",\"EL\",\"BG\",\"RO\", \"UK\", \"NO\",\"CH\",\"AL\",\"MK\")\n", "data_tab2_part4.1 <- data_tab2_part4[order(factor(data_tab2_part4$geo, levels= countr_fig5)),]\n", "\n", "data_tab2_part5 <- rbind(tab_EU3[9], data_tab2_part4.1, data_2_col4_4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have to convert the class, because we have to add \"EU-27\" and this only works by converting it from a factor first, then doing the change and re-convert it back." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 2
geovalues
<fct><dbl>
EU-2765.1
FI 91.0
NL 85.0
SE 84.9
LU 81.1
EE 79.5
\n" ], "text/latex": [ "A tibble: 6 × 2\n", "\\begin{tabular}{ll}\n", " geo & values\\\\\n", " & \\\\\n", "\\hline\n", "\t EU-27 & 65.1\\\\\n", "\t FI & 91.0\\\\\n", "\t NL & 85.0\\\\\n", "\t SE & 84.9\\\\\n", "\t LU & 81.1\\\\\n", "\t EE & 79.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 2\n", "\n", "| geo <fct> | values <dbl> |\n", "|---|---|\n", "| EU-27 | 65.1 |\n", "| FI | 91.0 |\n", "| NL | 85.0 |\n", "| SE | 84.9 |\n", "| LU | 81.1 |\n", "| EE | 79.5 |\n", "\n" ], "text/plain": [ " geo values\n", "1 EU-27 65.1 \n", "2 FI 91.0 \n", "3 NL 85.0 \n", "4 SE 84.9 \n", "5 LU 81.1 \n", "6 EE 79.5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab2_part5[,1] <- as.character(data_tab2_part5[,1])\n", "data_tab2_part5[1,1] <- \"EU-27\"\n", "data_tab2_part5[,1] <- factor(countr_fig5)\n", "\n", "head(data_tab2_part5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Bring the countries in the order the graph shows. You can do this due to modify the levels of the variable \"geo\"." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "data_tab2_part5$geo <- factor(data_tab2_part5$geo,\n", " levels = c(\"EU-27\",\"FI\", \"NL\",\"SE\",\"LU\",\"EE\",\"CZ\",\"AT\",\"DE\",\"IE\",\"FR\",\"SK\",\"DK\",\"CY\",\"SI\",\"BE\",\n", " \"ES\",\"MT\",\"PL\",\"LV\",\"HU\",\"LT\",\"HR\",\"IT\",\"PT\",\"EL\",\"BG\",\"RO\", \"UK\", \"NO\",\"CH\",\"AL\",\"MK\"))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the graph via `ggplot`" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "image/png": 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65du9bU1Iiq1dbWmpubi+dk7u7uxsbG/P+1fft2Irp//z4TfJcuXcS/zgUCgZmZ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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "ggplot(data_tab2_part5, aes(x=geo,y=values)) +\n", " geom_bar(stat = \"identity\", position=\"dodge\", width=0.5, fill=\"steelblue3\") +\n", " scale_y_continuous(limits = c(0, 100), breaks = seq(0, 100, by = 10)) +\n", " ggtitle(\"Share of population participating in tourism, 2018\",\n", " subtitle=\"(% of population aged 15 years or more)\") +\n", " coord_flip()+\n", " scale_x_discrete(limits = rev(levels(data_tab2_part5$geo)),\n", " labels = c(\"North Macedonia\", \"Albania\", \"Switzerland\", \"Norway\", \"United Kingdom\", \n", " \"Romania\", \"Bulgaria\", \"Greece\", \"Portugal\", \"Italy\", \"Croatia\", \"Lithuania\", \n", " \"Hungary\", \"Latvia\", \"Poland\", \"Malta\", \"Spain\", \"Belgium\", \"Slovenia\", \"Cyprus\", \n", " \"Denmark\", \"Slovakia\", \"France\", \"Ireland\", \"Germany\", \"Austria\", \"Czechia\", \n", " \"Estonia\", \"Luxembourg\", \"Sweden\", \"Netherlands\", \"Finland\", \"EU-27\")) +\n", " ylab(\" \") +\n", " xlab(\" \") +\n", " theme(panel.grid.major.x = element_line(color=\"grey\"),\n", " panel.background = element_rect(fill = NA),\n", " axis.line = element_line(colour = \"white\"),\n", " axis.ticks = element_line(colour= \"white\")) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nights spent abroad by EU-27 residents: Luxembourg leads in nights per inhabitant\n", "\n", "### Figure 6\n", "\n", "#### First step\n", "\n", "- load the data `data_fig6`\n", "- again we add a year-column" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "data_fig6 <- as_tibble(get_eurostat_data(id=\"tour_dem_tntot\"))\n", "\n", "data_fig6$year <- as.integer(substr(data_fig6$time,1,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a new contry vector `countr_fig6` and exclude the countries, which need data from 2017. Build the subset `data_fig6_subs`" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "countr_fig6 <- countr <- c(\"ES\",\"IT\",\"FR\",\"EL\",\"AT\",\"DE\",\"HR\",\"PT\",\"NL\",\"CZ\",\"BE\",\"BG\",\"IE\",\"CY\",\"SE\",\"HU\",\"DK\",\"SI\",\n", " \"MT\",\"FI\",\"SK\",\"EE\",\"LV\",\"LT\",\"LU\",\"UK\",\"CH\",\"NO\",\"IS\",\"LI\",\"TR\",\"ME\",\"RS\",\"MK\",\"XK\")\n", "data_fig6_subs <- subset(data_fig6, \n", " data_fig6$geo %in% countr_fig6 & \n", " data_fig6$year==2018 &\n", " data_fig6$purpose==\"TOTAL\" &\n", " data_fig6$duration==\"N_GE1\"&\n", " data_fig6$unit==\"NR\"&\n", " data_fig6$partner==\"OUT\")\n", "data_fig6_subs <- data_fig6_subs[,c(5,7)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Poland (`countr_fig6_2`) we need data from 2017. Build the subset `data_fig6_subs2`" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "countr_fig6_2 <- c(\"PL\",\"RO\")\n", "data_fig6_subs2 <- subset(data_fig6, \n", " data_fig6$geo %in% countr_fig6_2 & \n", " data_fig6$year==2017 &\n", " data_fig6$purpose==\"TOTAL\" &\n", " data_fig6$duration==\"N_GE1\"&\n", " data_fig6$unit==\"NR\"&\n", " data_fig6$partner==\"OUT\")\n", "data_fig6_subs2 <- data_fig6_subs2[,c(5,7)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Match the two calculated subsets in `data_fig6_subs`" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 2
geovalues
<fct><dbl>
AT 74626464
BE104276180
BG 6397986
CH118774558
CY 11955548
CZ 46994452
\n" ], "text/latex": [ "A tibble: 6 × 2\n", "\\begin{tabular}{ll}\n", " geo & values\\\\\n", " & \\\\\n", "\\hline\n", "\t AT & 74626464\\\\\n", "\t BE & 104276180\\\\\n", "\t BG & 6397986\\\\\n", "\t CH & 118774558\\\\\n", "\t CY & 11955548\\\\\n", "\t CZ & 46994452\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 2\n", "\n", "| geo <fct> | values <dbl> |\n", "|---|---|\n", "| AT | 74626464 |\n", "| BE | 104276180 |\n", "| BG | 6397986 |\n", "| CH | 118774558 |\n", "| CY | 11955548 |\n", "| CZ | 46994452 |\n", "\n" ], "text/plain": [ " geo values \n", "1 AT 74626464\n", "2 BE 104276180\n", "3 BG 6397986\n", "4 CH 118774558\n", "5 CY 11955548\n", "6 CZ 46994452" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_fig6_subs <- rbind(data_fig6_subs,data_fig6_subs2)\n", "\n", "head(data_fig6_subs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Calculate the EU-27 value by summing up\n", "- create a vector, which indicates due to FALSE and TRUE at the exact position, which countries of `data_fig6_subs` are within `EU_27`: `EU_27_prep4`\n", "- by using the sapply-command we can calculate the sum: `tab_EU4`" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "EU_27_prep4 <- data_fig6_subs$geo %in% EU_27\n", "\n", "tab_EU4 <- sapply(as.data.frame(data_fig6_subs[EU_27_prep4,2]),sum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add a column with the ratio we need for the pie chart\n", "- build the ratio `tab_ratio2`\n", "- just keep the EU-countries\n", "- sort the values decreasing\n", "- keep the top 9 countries (`tab_ratio_top9_2`) and summing up the others to \"Rest of EU-27\" (`tab_ratio_other_2_sum`) by using again the sapply command\n", "- add the sum to the top 9: `tab_ratio_new_2`" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in `[<-.factor`(`*tmp*`, ri, value = 15.3142164698757):\n", "“invalid factor level, NA generated”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 10 × 2
geovalues
<fct><dbl>
7DE35.407430
13FR15.544253
22NL 7.500622
25SE 5.559875
11ES 4.806601
28PL 4.630022
17IT 4.284631
2BE 4.052285
1AT 2.900065
10NA15.314216
\n" ], "text/latex": [ "A data.frame: 10 × 2\n", "\\begin{tabular}{r|ll}\n", " & geo & values\\\\\n", " & & \\\\\n", "\\hline\n", "\t7 & DE & 35.407430\\\\\n", "\t13 & FR & 15.544253\\\\\n", "\t22 & NL & 7.500622\\\\\n", "\t25 & SE & 5.559875\\\\\n", "\t11 & ES & 4.806601\\\\\n", "\t28 & PL & 4.630022\\\\\n", "\t17 & IT & 4.284631\\\\\n", "\t2 & BE & 4.052285\\\\\n", "\t1 & AT & 2.900065\\\\\n", "\t10 & NA & 15.314216\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 10 × 2\n", "\n", "| | geo <fct> | values <dbl> |\n", "|---|---|---|\n", "| 7 | DE | 35.407430 |\n", "| 13 | FR | 15.544253 |\n", "| 22 | NL | 7.500622 |\n", "| 25 | SE | 5.559875 |\n", "| 11 | ES | 4.806601 |\n", "| 28 | PL | 4.630022 |\n", "| 17 | IT | 4.284631 |\n", "| 2 | BE | 4.052285 |\n", "| 1 | AT | 2.900065 |\n", "| 10 | NA | 15.314216 |\n", "\n" ], "text/plain": [ " geo values \n", "7 DE 35.407430\n", "13 FR 15.544253\n", "22 NL 7.500622\n", "25 SE 5.559875\n", "11 ES 4.806601\n", "28 PL 4.630022\n", "17 IT 4.284631\n", "2 BE 4.052285\n", "1 AT 2.900065\n", "10 NA 15.314216" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tab_ratio2 <- cbind(data_fig6_subs,data_fig6_subs[2]/tab_EU4*100)\n", "\n", "tab_ratio2 <- tab_ratio2[EU_27_prep4,]\n", "\n", "tab_ratio2 <- tab_ratio2[order(tab_ratio2[,3], decreasing=T),]\n", "\n", "tab_ratio_top9_2 <- tab_ratio2[1:9,]\n", "tab_ratio_other_2 <- tab_ratio2[10:27,]\n", "tab_ratio_other_2_sum <- sapply(as.data.frame(tab_ratio_other_2[,3]),sum)\n", "\n", "tab_ratio_new_2 <- rbind(tab_ratio_top9_2[,c(1,3)], tab_ratio_other_2_sum)\n", "tab_ratio_new_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the labels we need for the pie chart \n", "- round the values and put a \"%\" behind it: `label_pie2_1`\n", "- create a vector with the needed country names: `label_pie2_2`\n", "- combine the both created label parts: `label_pie2_3`" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "label_pie2_1 <- paste(round(tab_ratio_new_2$values,0), \"%\")\n", "\n", "label_pie2_2 <- c(\"Germany\",\"France\",\"Netherlands\",\"Sweden\",\"Spain\",\"Poland\",\"Italy\",\"Belgium\",\n", " \"Austria\",\"Rest of EU-27\")\n", "\n", "label_pie2_3 <- paste(label_pie2_2,\"\\n\",label_pie2_1) #we can use a \"/n\" to get a line break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Third step\n", "\n", "Create variables for the position of the labels in the pie chart. Additionally change the colors in the vector `colors`, if you want.\n", " \n", "Plot the pie chart" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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zduEFGfPn2e/VdycrJomY0bN0rsSISIXF1dS9u4TNeuXSOiyMjI0haIjY0lomHD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68IudCA93Tk6Ol5dXeno60YkQQuxt0gwA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\nW48Kiyn5ISU9oDtplPKYyiukhzt37vzVV18NGTJkyJAhLVu2ZLbG2ggPD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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "tab_ratio_new_2 <- tab_ratio_new_2 %>% \n", " mutate(end = 2 * pi * cumsum(values)/sum(values),\n", " start = lag(end, default = 0),\n", " middle = 0.5 * (start + end),\n", " hjust = ifelse(middle > pi, 1, 0),\n", " vjust = ifelse(middle < pi/2 | middle > 3 * pi/2, 0, 1))\n", "\n", "# colors <- c(\"#ffd700\",\"skyblue1\",\"lightblue1\",\"tomato2\",\"tan1\",\"peachpuff1\",\"dodgerblue3\",\"blue\",\"tan\")\n", "\n", "ggplot(tab_ratio_new_2) + \n", " geom_arc_bar(aes(x0 = 0, y0 = 0, r0 = 0, r = 1,\n", " start = start, end = end, fill = geo)) +\n", " geom_text(aes(x = 1.05 * sin(middle), y = 1.05 * cos(middle), label = label_pie2_3,\n", " hjust = hjust, vjust = vjust)) +\n", " # scale_fill_manual(values=colors) +\n", " coord_fixed() +\n", " scale_x_continuous(limits = c(-1.5, 1.4), #Adjust so labels are not cut off\n", " name = \"\", breaks = NULL, labels = NULL) +\n", " scale_y_continuous(limits = c(-1.15, 1.05), #Adjust so labels are not cut off\n", " name = \"\", breaks = NULL, labels = NULL) +\n", " ylab(\" \") +\n", " xlab(\" \") +\n", " theme_classic() + \n", " theme(legend.position = \"none\",\n", " axis.line = element_blank(),\n", " axis.text = element_blank(),\n", " axis.ticks = element_blank(),\n", " panel.grid = element_blank()) +\n", " ggtitle(\"Share of nights spent during outbound holidays of Europeans, by country of residence of the tourist, 2018\",\n", " subtitle=\"(% of nights spent abroad by residents of the EU-27)\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure 7\n", "\n", "#### First step\n", "\n", "We need the number of population by people aged over 15 years.\n", "- load the data: `data_fig7`\n", "- add again a year-column\n", "- create a new country vector: `countr_fig7`" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "scrolled": true }, "outputs": [], "source": [ "data_fig7 <- as_tibble(get_eurostat_data(id=\"demo_pjanbroad\"))\n", "\n", "data_fig7$year <- as.integer(substr(data_fig7$time,1,4))\n", "\n", "countr_fig7 <- c(\"LU\",\"SE\",\"CY\",\"NL\",\"IE\",\"DE\",\"FI\",\"EE\",\"BE\",\"AT\",\"DK\",\"SI\",\"MT\",\"FR\",\"LT\",\"SK\",\n", " \"CZ\",\"LV\",\"HU\",\"PL\",\"HR\",\"ES\",\"IT\",\"PT\",\"BG\",\"EL\",\"RO\",\"CH\",\"NO\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build the subset `data_fig7_subs`\n", "- because we have per country several data points, we have to aggregate the data per country" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "data_fig7_subs <- subset(data_fig7, \n", " data_fig7$geo %in% countr_fig7 & \n", " data_fig7$year==2018 &\n", " data_fig7$age %in% c(\"Y15-64\",\"Y_GE65\") &\n", " data_fig7$sex==\"T\")\n", "data_fig7_subs <- data_fig7_subs[,c(4,6)]\n", "\n", "data_fig7_subs <- aggregate(data_fig7_subs$values, by = list(geo = data_fig7_subs$geo), FUN = sum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bring the countries in the order the graphik shows (we have to do this for both needed datasets, i.e. `data_fig7_subs` and `data_fig6_subs`).\n", "- data_fig7_subs_2; data_fig6_subs_fig7 --> compare the countries: are they in the same order? then we can match\n", "- match the two data frames and calculate the ratio in `data_fig7_final`" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "data_fig6_subs_fig7 <- data_fig6_subs[order(factor(data_fig6_subs$geo, levels= countr_fig7)),]\n", "data_fig7_subs_2 <- data_fig7_subs[order(factor(data_fig7_subs$geo, levels= countr_fig7)),]" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "data_fig7_final <- cbind(data_fig6_subs_fig7,data_fig7_subs_2[,2])\n", "data_fig7_final$ratio <- round(data_fig7_final[,2]/data_fig7_final[,3],1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "Calculate the EU-27 value by summing up\n", "- create a vector, which indicates due to FALSE and TRUE at the exact position, which countries of `data_fig7_final` are within `EU_27`: `EU_27_prep5`\n", "- by using the sapply-command we can calculate the sum: `tab_EU5` \n", "- calculate the ratio `tab_EU5_ratio` and match them to `tab_EU5_final` (at this stage we get the warning message, that one NA for \"Eu-27\" get generated: we solve this problem two steps later)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "EU_27_prep5 <- data_fig7_final$geo %in% EU_27\n", "\n", "tab_EU5 <- sapply(as.data.frame(data_fig7_final[EU_27_prep5,2:3]),sum)\n", "\n", "tab_EU5_ratio <- round(tab_EU5[1]/tab_EU5[2],1)\n", "\n", "tab_EU5_final <- c(\"EU-27\",tab_EU5,tab_EU5_ratio)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- put the calculates EU values to the table above: `data_fig7_final`\n", "- we have to convert the class, because we have to add \"EU-27\" and this only works by converting it from a factor first, then doing the change and re-convert it back" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in `[<-.factor`(`*tmp*`, ri, value = \"EU-27\"):\n", "“invalid factor level, NA generated”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
geovaluesdata_fig7_subs_2[, 2]ratio
<fct><chr><chr><chr>
1EU-2725732684713782010856.8
2LU 15819287 504883 31.3
3SE 143070504 8325565 17.2
4CY 11955548 723947 16.5
5NL 193011133 14418460 13.4
6IE 50197497 3823944 13.1
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & geo & values & data\\_fig7\\_subs\\_2{[}, 2{]} & ratio\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & EU-27 & 2573268471 & 378201085 & 6.8 \\\\\n", "\t2 & LU & 15819287 & 504883 & 31.3\\\\\n", "\t3 & SE & 143070504 & 8325565 & 17.2\\\\\n", "\t4 & CY & 11955548 & 723947 & 16.5\\\\\n", "\t5 & NL & 193011133 & 14418460 & 13.4\\\\\n", "\t6 & IE & 50197497 & 3823944 & 13.1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | geo <fct> | values <chr> | data_fig7_subs_2[, 2] <chr> | ratio <chr> |\n", "|---|---|---|---|---|\n", "| 1 | EU-27 | 2573268471 | 378201085 | 6.8 |\n", "| 2 | LU | 15819287 | 504883 | 31.3 |\n", "| 3 | SE | 143070504 | 8325565 | 17.2 |\n", "| 4 | CY | 11955548 | 723947 | 16.5 |\n", "| 5 | NL | 193011133 | 14418460 | 13.4 |\n", "| 6 | IE | 50197497 | 3823944 | 13.1 |\n", "\n" ], "text/plain": [ " geo values data_fig7_subs_2[, 2] ratio\n", "1 EU-27 2573268471 378201085 6.8 \n", "2 LU 15819287 504883 31.3 \n", "3 SE 143070504 8325565 17.2 \n", "4 CY 11955548 723947 16.5 \n", "5 NL 193011133 14418460 13.4 \n", "6 IE 50197497 3823944 13.1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_fig7_final <- rbind(tab_EU5_final,data_fig7_final)\n", "\n", "data_fig7_final[,1] <- as.character(data_fig7_final[,1])\n", "data_fig7_final[1,1] <- \"EU-27\"\n", "data_fig7_final[,1] <- factor(data_fig7_final[,1])\n", "\n", "head(data_fig7_final)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Third step\n", "\n", "- bring the country names in the order the graphik shows you can do this due to modify the levels of the variable `\"geo\"`\n", "- to achieve that the y-axes in the chart is later in an ascending order, we have to convert the ratio-axes to numeric\n", "\n", "Plot the bar chart" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "data_fig7_final$geo <- factor(data_fig7_final$geo,\n", " levels = c(\"EU-27\",\"LU\",\"SE\",\"CY\",\"NL\",\"IE\",\"DE\",\"FI\",\"EE\",\"BE\",\"AT\",\"DK\",\"SI\",\"MT\",\"FR\",\"LT\",\n", " \"SK\",\"CZ\",\"LV\",\"HU\",\"PL\",\"HR\",\"ES\",\"IT\",\"PT\",\"BG\",\"EL\",\"RO\",\"CH\",\"NO\"))\n", "\n", "data_fig7_final$ratio <- as.numeric(as.character(data_fig7_final$ratio))" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "image/png": 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kz6JZOFXOV2wQSTtVPjaJTVqqkRkxk1zniTyV6lPsOmtgICAvT19clRpero6OgM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AGgLBDgAAAEBDINgBAAAAaAgEOwAAAAAN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\ni7BmIvHTZkVHRyuVylmzZul6KIqaNWvWhQsX0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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "ggplot(data_fig7_final, aes(x=geo,y=ratio)) +\n", " geom_bar(stat = \"identity\", position=\"dodge\", width=0.5, fill=\"steelblue3\") +\n", " theme_classic() +\n", " ggtitle(\"Country of origin for outbound tourism trips, 2018\",\n", " subtitle = \"(average nights spent abroad per inhabitant aged 15 years or more)\") +\n", " coord_flip()+\n", " scale_x_discrete(limits = rev(levels(data_fig7_final$geo)),\n", " labels = c(\"Norway\", \"Switzerland\", \"Romania\", \"Greece\", \"Bulgaria\", \"Portugal\", \"Italy\", \n", " \"Spain\", \"Croatia\", \"Poland\", \"Hungary\", \"Latvia\", \"Czechia\", \"Slovakia\", \n", " \"Lithuania\", \"France\", \"Malta\", \"Slovenia\", \"Denmark\", \"Austria\",\n", " \"Belgium\", \"Estonia\", \"Finland\", \"Germany\", \"Ireland\", \"Netherlands\",\n", " \"Cyprus\", \"Sweden\", \"Luxembourg\",\"EU-27\")) +\n", " ylab(\" \") +\n", " xlab(\" \") +\n", " theme(axis.title.x=element_blank(), #remove labels at x-axes\n", " axis.text.x=element_blank(),\n", " axis.ticks.x=element_blank()) +\n", " geom_text(aes(label=ratio)) #add the ratio values after the bars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tourism expenditure: highest spending by German residents\n", "\n", "### Table 3\n", "\n", "#### First step\n", "\n", "- load the needed data `data_3` and add the year-column\n", "- create a country-vector (in the order the table shows): `countr_tab3_1_1`" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“'memory.limit()' is Windows-specific”\n" ] }, { "data": { "text/html": [ "Inf" ], "text/latex": [ "Inf" ], "text/markdown": [ "Inf" ], "text/plain": [ "[1] Inf" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "0.9.8\tid%3Dbop_c6_a\tfilters%3Dc(\"CRE\", \"DEB\", \"BAL\", \"MIO_EUR\", \"SD\", \"S1\", \"WRL_REST\")\tdate_filter%3Dc(2013, 2018)\tverbose%3DTRUE\tname%3DFALSE\n", "\n", "Trying to download the DSD from: http://ec.europa.eu/eurostat/SDMX/diss-web/rest/datastructure/ESTAT/DSD_bop_c6_a\n", "\n", "Trying to extract the DSD from: /tmp/RtmpF5luDK/file1211abd98fa\n", "\n", "The DSD of the bop_c6_a dataset was cached in memory ('bop_c6_a.dsd' in '.restatapi_env').\n", "\n", "\n", "date_filter: c20132018\n", "\n", "filters: c20132018; is numeric: FALSE; call parents: 26\n", "\n", "length df: 3 -*- df: c, 2013, 2018\n", "\n", "2013, 2018 date filter length: 2, nchar date_filter: 4,4\n", "\n", "date_filter: 2013, 2018 nrow dft: 2\n", "\n", ".MIO_EUR.SD.S1.S1.CRE+DEB+BAL.WRL_REST.-?startPeriod=2013-01-01&endPeriod=2013-12-31?startPeriod=2018-01-01&endPeriod=2018-12-31\n", "\n", "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/bop_c6_a/.MIO_EUR.SD.S1.S1.CRE+DEB+BAL.WRL_REST./?startPeriod=2013-01-01&endPeriod=2013-12-31\n", "\n", "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/bop_c6_a/.MIO_EUR.SD.S1.S1.CRE+DEB+BAL.WRL_REST./?startPeriod=2013-01-01&endPeriod=2013-12-31\n", "code: - severity:\n", "\n", "\n", "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/bop_c6_a/.MIO_EUR.SD.S1.S1.CRE+DEB+BAL.WRL_REST./?startPeriod=2018-01-01&endPeriod=2018-12-31\n", "\n", "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/bop_c6_a/.MIO_EUR.SD.S1.S1.CRE+DEB+BAL.WRL_REST./?startPeriod=2018-01-01&endPeriod=2018-12-31\n", "code: - severity:\n", "\n", "\n", "restat - nrow:228;ncol:10;colnames:CURRENCY/PARTNER/BOP_ITEM/STK_FLOW/SECTOR10/SECTPART/GEO/FREQ/obsTime/obsValue\n", "\n" ] } ], "source": [ "memory.limit(size=9999999999)\n", "\n", "data_3 <- as_tibble(get_eurostat_data(id=\"bop_c6_a\",filters=c(\"CRE\",\"DEB\",\"BAL\",\"MIO_EUR\",\"SD\",\"S1\",\"WRL_REST\"),date_filter=c(2013,2018),verbose=TRUE,name=FALSE))\n", "data_3$year <- as.integer(substr(data_3$time,1,4))" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "countr_tab3_1_1 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"UK\",\"IS\",\"NO\",\"CH\",\"ME\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Second step\n", "\n", "We create first the columns for 2018 and afterwards the same for 2013\n", "- due to we want to use a loop for getting the data for the three columns, we have to create a vector with the essential difference between the three columns (here: the variable \"stk_flow\"): `cred_deb_bal`\n", "- create an empty data frame, which we can fill while running the loop: `data_tab3_part1`\n", " - we have 4 columns in the needed dataset: one country-column and three data-columns\n", " - and we have data for 38 countries and therefore 38 rows \n", " \n", "Build the subset `data_3_col1` within the loop\n", "- the first column with the countries is the same for each of the three calculated datasets, therefore we can overwrite the country column in each intermediate step (we get of course a warning message)\n", "- we assign the second column of each intermediate step to the three remaining columns of the empty data frame `data_tab3_part1` from above" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "cred_deb_bal <- c(\"CRE\",\"DEB\",\"BAL\")\n", "\n", "data_tab3_part1 <- as.data.frame(matrix(ncol = 4, nrow = 38))" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "for (i in 1:3) {\n", " \n", "data_3_col1 <- subset(data_3, \n", " data_3$geo %in% countr_tab3_1_1 & \n", " data_3$year == 2018 &\n", " data_3$currency ==\"MIO_EUR\"&\n", " data_3$bop_item== \"SD\" &\n", " data_3$stk_flow == cred_deb_bal[i] &\n", " data_3$partner == \"WRL_REST\")\n", "data_3_col1 <- data_3_col1[,c(7,9)]\n", "\n", "data_tab3_part1[,1] <- data_3_col1[,1]\n", "\n", "data_tab3_part1[,1+i] <- data_3_col1[,2]\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bring the countries in the order the graphik shows.\n", "\n", "Finally we have three of the seven columns filled with data." ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
V1V2V3V4
<fct><dbl><dbl><dbl>
4BE 7528.015654.0 -8126.0
5BG 3808.2 1584.4 2223.8
8CZ 6305.3 5055.5 1249.8
10DK 7718.8 8926.6 -1207.7
9DE36390.080933.0-44543.0
11EE 1509.6 1244.8 264.8
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & V1 & V2 & V3 & V4\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t4 & BE & 7528.0 & 15654.0 & -8126.0\\\\\n", "\t5 & BG & 3808.2 & 1584.4 & 2223.8\\\\\n", "\t8 & CZ & 6305.3 & 5055.5 & 1249.8\\\\\n", "\t10 & DK & 7718.8 & 8926.6 & -1207.7\\\\\n", "\t9 & DE & 36390.0 & 80933.0 & -44543.0\\\\\n", "\t11 & EE & 1509.6 & 1244.8 & 264.8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | V1 <fct> | V2 <dbl> | V3 <dbl> | V4 <dbl> |\n", "|---|---|---|---|---|\n", "| 4 | BE | 7528.0 | 15654.0 | -8126.0 |\n", "| 5 | BG | 3808.2 | 1584.4 | 2223.8 |\n", "| 8 | CZ | 6305.3 | 5055.5 | 1249.8 |\n", "| 10 | DK | 7718.8 | 8926.6 | -1207.7 |\n", "| 9 | DE | 36390.0 | 80933.0 | -44543.0 |\n", "| 11 | EE | 1509.6 | 1244.8 | 264.8 |\n", "\n" ], "text/plain": [ " V1 V2 V3 V4 \n", "4 BE 7528.0 15654.0 -8126.0\n", "5 BG 3808.2 1584.4 2223.8\n", "8 CZ 6305.3 5055.5 1249.8\n", "10 DK 7718.8 8926.6 -1207.7\n", "9 DE 36390.0 80933.0 -44543.0\n", "11 EE 1509.6 1244.8 264.8" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab3_part1 <- data_tab3_part1[order(factor(data_tab3_part1$V1, levels= countr_tab3_1_1)),]\n", "\n", "head(data_tab3_part1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Third step\n", "\n", "Now we create the 2013-columns and repeat the loop from above.\n", "- due to we don't need the \"balance column\" we create at the beginning a vector with only 2 elements: `cred_deb_bal2`\n", "- Albania, Portugal and Norway have no data for 2013, therefore we just need 35 rows in `data_tab3_part2` and assign NA after the loop in `data_3_col2_1`\n", "- create a country-vector (in the order the table shows), exclude the countries with NA: `countr_tab3_1_2`\n", "\n", "Build the subset `data_3_col2` within the loop and match the loop's result afterwards with the NAs of the three countries (`data_tab3_part2`)." ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "cred_deb_bal2 <- c(\"CRE\",\"DEB\")\n", "\n", "data_tab3_part2 <- as.data.frame(matrix(ncol = 3, nrow = 35))\n", "\n", "countr_tab3_1_2 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PL\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"UK\",\"IS\",\"CH\",\"ME\",\"MK\",\"RS\",\"TR\",\"BA\",\"XK\")" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "for (i in 1:2) {\n", " \n", "data_3_col2 <- subset(data_3, \n", " data_3$geo %in% countr_tab3_1_2 & \n", " data_3$year == 2013 &\n", " data_3$currency ==\"MIO_EUR\"&\n", " data_3$bop_item== \"SD\" &\n", " data_3$stk_flow == cred_deb_bal2[i] &\n", " data_3$partner == \"WRL_REST\")\n", "data_3_col2 <- data_3_col2[,c(7,9)]\n", "\n", "data_tab3_part2[,1] <- data_3_col2[,1]\n", "\n", "data_tab3_part2[,1+i] <- data_3_col2[,2]\n", "\n", "}" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "data_3_col2_1 <- data.frame( V1=c(\"AL\",\"PT\",\"NO\"),\n", " V2=c(NA,NA,NA), \n", " V3=c(NA,NA,NA))\n", "\n", "data_tab3_part2 <- rbind(data_tab3_part2,data_3_col2_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bring the countries in the order the graphik shows.\n", "\n", "Finally we have two further of the seven columns filled with data." ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 3
V1V2V3
<fct><dbl><dbl>
3BE10074.016692.0
4BG 2890.8 840.0
7CZ 5303.1 3493.8
9DK 5385.1 7584.1
8DE31081.068793.0
10EE 1255.8 802.8
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & V1 & V2 & V3\\\\\n", " & & & \\\\\n", "\\hline\n", "\t3 & BE & 10074.0 & 16692.0\\\\\n", "\t4 & BG & 2890.8 & 840.0\\\\\n", "\t7 & CZ & 5303.1 & 3493.8\\\\\n", "\t9 & DK & 5385.1 & 7584.1\\\\\n", "\t8 & DE & 31081.0 & 68793.0\\\\\n", "\t10 & EE & 1255.8 & 802.8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| | V1 <fct> | V2 <dbl> | V3 <dbl> |\n", "|---|---|---|---|\n", "| 3 | BE | 10074.0 | 16692.0 |\n", "| 4 | BG | 2890.8 | 840.0 |\n", "| 7 | CZ | 5303.1 | 3493.8 |\n", "| 9 | DK | 5385.1 | 7584.1 |\n", "| 8 | DE | 31081.0 | 68793.0 |\n", "| 10 | EE | 1255.8 | 802.8 |\n", "\n" ], "text/plain": [ " V1 V2 V3 \n", "3 BE 10074.0 16692.0\n", "4 BG 2890.8 840.0\n", "7 CZ 5303.1 3493.8\n", "9 DK 5385.1 7584.1\n", "8 DE 31081.0 68793.0\n", "10 EE 1255.8 802.8" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab3_part2 <- data_tab3_part2[order(factor(data_tab3_part2$V1, levels= countr_tab3_1_1)),]\n", "\n", "head(data_tab3_part2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fourth step\n", "\n", "Calculate the \"relative to GDP\"-column.\n", "- therefore first we have to load the dataset to receive the GDPs per country: `data_4`\n", "- create a country-vector (in the order the table shows), exclude the countries with NA: `countr_tab3_2_1`\n", "- Norway has no data, therefore we assign NA: `data_4_col3_2`\n", "\n", "Build the subset `data_4_col3_1` and match the result afterwards with the NA of Norway to `data_tab3_part3`" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "data_4 <- as_tibble(get_eurostat_data(id=\"nama_10_gdp\"))\n", "data_4$year <- as.integer(substr(data_4$time,1,4))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "countr_tab3_2_1 <- c(\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\n", " \"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"UK\",\"IS\",\"CH\",\"ME\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\")\n", " \n", "data_4_col3_1 <- subset(data_4, \n", " data_4$geo %in% countr_tab3_2_1 & \n", " data_4$year == 2018 &\n", " data_4$na_item == \"B1GQ\" &\n", " data_4$unit == \"CP_MEUR\")\n", "data_4_col3_1 <- data_4_col3_1[,c(3,5)]\n", "\n", "data_4_col3_2 <- data.frame( geo= \"NO\", values= NA)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "data_tab3_part3 <- rbind(data_4_col3_1,data_4_col3_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bring the countries in the order the graphik shows." ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 2
geovalues
<fct><dbl>
BE 460419.4
BG 56111.8
CZ 210927.8
DK 302361.1
DE3356410.0
EE 25937.6
\n" ], "text/latex": [ "A tibble: 6 × 2\n", "\\begin{tabular}{ll}\n", " geo & values\\\\\n", " & \\\\\n", "\\hline\n", "\t BE & 460419.4\\\\\n", "\t BG & 56111.8\\\\\n", "\t CZ & 210927.8\\\\\n", "\t DK & 302361.1\\\\\n", "\t DE & 3356410.0\\\\\n", "\t EE & 25937.6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 2\n", "\n", "| geo <fct> | values <dbl> |\n", "|---|---|\n", "| BE | 460419.4 |\n", "| BG | 56111.8 |\n", "| CZ | 210927.8 |\n", "| DK | 302361.1 |\n", "| DE | 3356410.0 |\n", "| EE | 25937.6 |\n", "\n" ], "text/plain": [ " geo values \n", "1 BE 460419.4\n", "2 BG 56111.8\n", "3 CZ 210927.8\n", "4 DK 302361.1\n", "5 DE 3356410.0\n", "6 EE 25937.6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_tab3_part3 <- data_tab3_part3[order(factor(data_tab3_part3$geo, levels= countr_tab3_1_1)),]\n", "\n", "head(data_tab3_part3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have to calculate the ratio.\n", "- therefore we have to devide the 2018 data from above through the GDP-column calculated just now (`col4_1`,`col4_2`)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 1
values
<dbl>
11.635031
26.786808
32.989317
42.552842
51.084194
65.820122
\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & values\\\\\n", " & \\\\\n", "\\hline\n", "\t1 & 1.635031\\\\\n", "\t2 & 6.786808\\\\\n", "\t3 & 2.989317\\\\\n", "\t4 & 2.552842\\\\\n", "\t5 & 1.084194\\\\\n", "\t6 & 5.820122\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| | values <dbl> |\n", "|---|---|\n", "| 1 | 1.635031 |\n", "| 2 | 6.786808 |\n", "| 3 | 2.989317 |\n", "| 4 | 2.552842 |\n", "| 5 | 1.084194 |\n", "| 6 | 5.820122 |\n", "\n" ], "text/plain": [ " values \n", "1 1.635031\n", "2 6.786808\n", "3 2.989317\n", "4 2.552842\n", "5 1.084194\n", "6 5.820122" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 1
values
<dbl>
13.399944
22.823649
32.396792
42.952298
52.411297
64.799210
\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & values\\\\\n", " & \\\\\n", "\\hline\n", "\t1 & 3.399944\\\\\n", "\t2 & 2.823649\\\\\n", "\t3 & 2.396792\\\\\n", "\t4 & 2.952298\\\\\n", "\t5 & 2.411297\\\\\n", "\t6 & 4.799210\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| | values <dbl> |\n", "|---|---|\n", "| 1 | 3.399944 |\n", "| 2 | 2.823649 |\n", "| 3 | 2.396792 |\n", "| 4 | 2.952298 |\n", "| 5 | 2.411297 |\n", "| 6 | 4.799210 |\n", "\n" ], "text/plain": [ " values \n", "1 3.399944\n", "2 2.823649\n", "3 2.396792\n", "4 2.952298\n", "5 2.411297\n", "6 4.799210" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "col4_1 <- data_tab3_part1[,2]/data_tab3_part3[,2]*100\n", "col4_2 <- data_tab3_part1[,3]/data_tab3_part3[,2]*100\n", "\n", "head(col4_1)\n", "head(col4_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fifth step\n", "\n", "- create column with the names of the countries, this will be the first column of the complete table 3: `countr_names2`\n", "- finally put the calculated columns all together and round them correspondingly: `table3`\n", "- give the columns the correct names" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "countr_names2 <- c(\"Belgium\",\"Bulgaria\",\"Czechia\",\"Denmark\",\"Germany\",\"Estonia\", \"Ireland\", \"Greece\", \"Spain\", \"France\", \n", " \"Croatia\", \"Italy\", \"Cyprus\", \"Latvia\", \"Lithuania\", \"Luxembourg\", \"Hungary\", \"Malta\", \"Netherlands\", \n", " \"Austria\", \"Poland\", \"Portugal\", \"Romania\", \"Slovenia\", \"Slovakia\", \"Finland\", \"Sweden\",\n", " \"United Kingdom\",\"Iceland\",\"Norway\", \"Switzerland\",\"Montenegro\",\"North Macedonia\",\"Albania\",\n", " \"Serbia\",\"Turkey\",\"Bosnia and Herzegovina\",\"Kosovo\")" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "table3 <- cbind(countr_names2,\n", " round(data_tab3_part2[,2],0),\n", " round(data_tab3_part1[,2],0),\n", " round(col4_1,1),\n", " round(data_tab3_part2[,3],0),\n", " round(data_tab3_part1[,3],0),\n", " round(col4_2,1),\n", " round(data_tab3_part1[,4],0))" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 8
Receipts (million EUR) 2013Receipts (million EUR) 2018Relative to GDP 2018 (%)Expenditure (million EUR) 2013Expenditure (million EUR) 2018Relative to GDP 2018 (%)Balance (million EUR) 2018
<fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1Belgium 10074 75281.616692156543.4 -8126
2Bulgaria 2891 38086.8 840 15842.8 2224
3Czechia 5303 63053.0 3494 50562.4 1250
4Denmark 5385 77192.6 7584 89273.0 -1208
5Germany 31081363901.168793809332.4-44543
6Estonia 1256 15105.8 803 12454.8 265
\n" ], "text/latex": [ "A data.frame: 6 × 8\n", "\\begin{tabular}{r|llllllll}\n", " & & Receipts (million EUR) 2013 & Receipts (million EUR) 2018 & Relative to GDP 2018 (\\%) & Expenditure (million EUR) 2013 & Expenditure (million EUR) 2018 & Relative to GDP 2018 (\\%) & Balance (million EUR) 2018\\\\\n", " & & & & & & & & \\\\\n", "\\hline\n", "\t1 & Belgium & 10074 & 7528 & 1.6 & 16692 & 15654 & 3.4 & -8126\\\\\n", "\t2 & Bulgaria & 2891 & 3808 & 6.8 & 840 & 1584 & 2.8 & 2224\\\\\n", "\t3 & Czechia & 5303 & 6305 & 3.0 & 3494 & 5056 & 2.4 & 1250\\\\\n", "\t4 & Denmark & 5385 & 7719 & 2.6 & 7584 & 8927 & 3.0 & -1208\\\\\n", "\t5 & Germany & 31081 & 36390 & 1.1 & 68793 & 80933 & 2.4 & -44543\\\\\n", "\t6 & Estonia & 1256 & 1510 & 5.8 & 803 & 1245 & 4.8 & 265\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 8\n", "\n", "| | <fct> | Receipts (million EUR) 2013 <dbl> | Receipts (million EUR) 2018 <dbl> | Relative to GDP 2018 (%) <dbl> | Expenditure (million EUR) 2013 <dbl> | Expenditure (million EUR) 2018 <dbl> | Relative to GDP 2018 (%) <dbl> | Balance (million EUR) 2018 <dbl> |\n", "|---|---|---|---|---|---|---|---|---|\n", "| 1 | Belgium | 10074 | 7528 | 1.6 | 16692 | 15654 | 3.4 | -8126 |\n", "| 2 | Bulgaria | 2891 | 3808 | 6.8 | 840 | 1584 | 2.8 | 2224 |\n", "| 3 | Czechia | 5303 | 6305 | 3.0 | 3494 | 5056 | 2.4 | 1250 |\n", "| 4 | Denmark | 5385 | 7719 | 2.6 | 7584 | 8927 | 3.0 | -1208 |\n", "| 5 | Germany | 31081 | 36390 | 1.1 | 68793 | 80933 | 2.4 | -44543 |\n", "| 6 | Estonia | 1256 | 1510 | 5.8 | 803 | 1245 | 4.8 | 265 |\n", "\n" ], "text/plain": [ " Receipts (million EUR) 2013 Receipts (million EUR) 2018\n", "1 Belgium 10074 7528 \n", "2 Bulgaria 2891 3808 \n", "3 Czechia 5303 6305 \n", "4 Denmark 5385 7719 \n", "5 Germany 31081 36390 \n", "6 Estonia 1256 1510 \n", " Relative to GDP 2018 (%) Expenditure (million EUR) 2013\n", "1 1.6 16692 \n", "2 6.8 840 \n", "3 3.0 3494 \n", "4 2.6 7584 \n", "5 1.1 68793 \n", "6 5.8 803 \n", " Expenditure (million EUR) 2018 Relative to GDP 2018 (%)\n", "1 15654 3.4 \n", "2 1584 2.8 \n", "3 5056 2.4 \n", "4 8927 3.0 \n", "5 80933 2.4 \n", "6 1245 4.8 \n", " Balance (million EUR) 2018\n", "1 -8126 \n", "2 2224 \n", "3 1250 \n", "4 -1208 \n", "5 -44543 \n", "6 265 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(table3) <- c(\"\",\n", " \"Receipts (million EUR) 2013\", \n", " \"Receipts (million EUR) 2018\",\n", " \"Relative to GDP 2018 (%)\",\n", " \"Expenditure (million EUR) 2013\",\n", " \"Expenditure (million EUR) 2018\",\n", " \"Relative to GDP 2018 (%)\",\n", " \"Balance (million EUR) 2018\")\n", "head(table3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sixth step\n", "\n", "We need additionally the values for the EU\n", "- load the needed data `data_5` and add the year-column" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No data retrieved for the given filter(s), because the results are too big to download immediately through the REST API. The whole dataset is downloaded through the raw download and the filters are applied locally.\n", "\n" ] } ], "source": [ "data_5 <- as_tibble(get_eurostat_data(id=\"bop_eu6_q\",select_freq=\"A\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create first the columns for 2018 and afterwards the same for 2013\n", "- we will do exactly the same like for the several countries above" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "cred_deb_bal <- c(\"CRE\",\"DEB\",\"BAL\")\n", "\n", "data_tab3_part4 <- as.data.frame(matrix(ncol = 3, nrow = 1))" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A data.frame: 1 × 3
V1V2V3
<dbl><dbl><dbl>
156587109167.347419.7
\n" ], "text/latex": [ "A data.frame: 1 × 3\n", "\\begin{tabular}{lll}\n", " V1 & V2 & V3\\\\\n", " & & \\\\\n", "\\hline\n", "\t 156587 & 109167.3 & 47419.7\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 1 × 3\n", "\n", "| V1 <dbl> | V2 <dbl> | V3 <dbl> |\n", "|---|---|---|\n", "| 156587 | 109167.3 | 47419.7 |\n", "\n" ], "text/plain": [ " V1 V2 V3 \n", "1 156587 109167.3 47419.7" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for (i in 1:3) {\n", " \n", "data_5_col1 <- subset(data_5, \n", " data_5$geo == \"EU27_2020\" & \n", " data_5$time == 2018 &\n", " data_5$currency ==\"MIO_EUR\"&\n", " data_5$bop_item== \"SD\" &\n", " data_5$stk_flow == cred_deb_bal[i] &\n", " data_5$partner == \"EXT_EU27_2020\")\n", "data_5_col1 <- data_5_col1[,10]\n", "\n", "data_tab3_part4[,i] <- data_5_col1\n", "\n", "}\n", "\n", "data_tab3_part4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create the 2013-columns and repeat the loop from above." ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "cred_deb_bal2 <- c(\"CRE\",\"DEB\")\n", "\n", "data_tab3_part5 <- as.data.frame(matrix(ncol = 2, nrow = 1))" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A data.frame: 1 × 2
V1V2
<dbl><dbl>
126010.786737.8
\n" ], "text/latex": [ "A data.frame: 1 × 2\n", "\\begin{tabular}{ll}\n", " V1 & V2\\\\\n", " & \\\\\n", "\\hline\n", "\t 126010.7 & 86737.8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 1 × 2\n", "\n", "| V1 <dbl> | V2 <dbl> |\n", "|---|---|\n", "| 126010.7 | 86737.8 |\n", "\n" ], "text/plain": [ " V1 V2 \n", "1 126010.7 86737.8" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for (i in 1:2) {\n", " \n", "data_5_col2 <- subset(data_5, \n", " data_5$geo == \"EU27_2020\" & \n", " data_5$time == 2013 &\n", " data_5$currency ==\"MIO_EUR\"&\n", " data_5$bop_item== \"SD\" &\n", " data_5$stk_flow == cred_deb_bal[i] &\n", " data_5$partner == \"EXT_EU27_2020\")\n", "data_5_col2 <- data_5_col2[,10]\n", "\n", "data_tab3_part5[,i] <- data_5_col2\n", "\n", "}\n", "\n", "data_tab3_part5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the \"relative to GDP\"-column\n", "- we can use the dataset `data_4` to receive the GDPs per country\n", "- now we have to calculate the ratio: therefore we have to devide the 2018 data from above (`data_tab3_part4`) through the GDP-column calculated just now (`data_5_col3`): `data_5_col4_1`, `data_5_col4_2`" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "data_5_col3 <- subset(data_4, \n", " data_4$geo == \"EU27_2020\" & \n", " data_4$year == 2018 &\n", " data_4$na_item == \"B1GQ\"&\n", " data_4$unit == \"CP_MEUR\")\n", "data_5_col3 <- data_5_col3[,5]" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A data.frame: 1 × 1
values
<dbl>
1.158366
\n" ], "text/latex": [ "A data.frame: 1 × 1\n", "\\begin{tabular}{l}\n", " values\\\\\n", " \\\\\n", "\\hline\n", "\t 1.158366\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 1 × 1\n", "\n", "| values <dbl> |\n", "|---|\n", "| 1.158366 |\n", "\n" ], "text/plain": [ " values \n", "1 1.158366" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A data.frame: 1 × 1
values
<dbl>
0.8075745
\n" ], "text/latex": [ "A data.frame: 1 × 1\n", "\\begin{tabular}{l}\n", " values\\\\\n", " \\\\\n", "\\hline\n", "\t 0.8075745\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 1 × 1\n", "\n", "| values <dbl> |\n", "|---|\n", "| 0.8075745 |\n", "\n" ], "text/plain": [ " values \n", "1 0.8075745" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_5_col4_1 <- data_tab3_part4[,1]/data_5_col3*100\n", "data_5_col4_2 <- data_tab3_part4[,2]/data_5_col3*100\n", "data_5_col4_1\n", "data_5_col4_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally put the calculated columns all together and round them correspondingly: `table3_EU`\n", "\n", "Give the columns the correct names" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A data.frame: 1 × 8
Receipts (million EUR) 2013Receipts (million EUR) 2018Relative to GDP 2018 (%)Expenditure (million EUR) 2013Expenditure (million EUR) 2018Relative to GDP 2018 (%)Balance (million EUR) 2018
<fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
EU-271260111565871.2867381091670.847420
\n" ], "text/latex": [ "A data.frame: 1 × 8\n", "\\begin{tabular}{llllllll}\n", " & Receipts (million EUR) 2013 & Receipts (million EUR) 2018 & Relative to GDP 2018 (\\%) & Expenditure (million EUR) 2013 & Expenditure (million EUR) 2018 & Relative to GDP 2018 (\\%) & Balance (million EUR) 2018\\\\\n", " & & & & & & & \\\\\n", "\\hline\n", "\t EU-27 & 126011 & 156587 & 1.2 & 86738 & 109167 & 0.8 & 47420\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 1 × 8\n", "\n", "| <fct> | Receipts (million EUR) 2013 <dbl> | Receipts (million EUR) 2018 <dbl> | Relative to GDP 2018 (%) <dbl> | Expenditure (million EUR) 2013 <dbl> | Expenditure (million EUR) 2018 <dbl> | Relative to GDP 2018 (%) <dbl> | Balance (million EUR) 2018 <dbl> |\n", "|---|---|---|---|---|---|---|---|\n", "| EU-27 | 126011 | 156587 | 1.2 | 86738 | 109167 | 0.8 | 47420 |\n", "\n" ], "text/plain": [ " Receipts (million EUR) 2013 Receipts (million EUR) 2018\n", "1 EU-27 126011 156587 \n", " Relative to GDP 2018 (%) Expenditure (million EUR) 2013\n", "1 1.2 86738 \n", " Expenditure (million EUR) 2018 Relative to GDP 2018 (%)\n", "1 109167 0.8 \n", " Balance (million EUR) 2018\n", "1 47420 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "countr_names3 <- c(\"EU-27\")\n", "\n", "table3_EU <- cbind(countr_names3,\n", " round(data_tab3_part5[,1],0),\n", " round(data_tab3_part4[,1],0),\n", " round(data_5_col4_1,1),\n", " round(data_tab3_part5[,2],0),\n", " round(data_tab3_part4[,2],0),\n", " round(data_5_col4_2,1),\n", " round(data_tab3_part4[,3],0))\n", "\n", "names(table3_EU) <- c(\"\",\n", " \"Receipts (million EUR) 2013\", \n", " \"Receipts (million EUR) 2018\",\n", " \"Relative to GDP 2018 (%)\",\n", " \"Expenditure (million EUR) 2013\",\n", " \"Expenditure (million EUR) 2018\",\n", " \"Relative to GDP 2018 (%)\",\n", " \"Balance (million EUR) 2018\")\n", "\n", "table3_EU" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Seventh step\n", "\n", "Add the EU values (`table3_EU`) to `table3`: `table3_final`" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 39 × 8 of type chr
Receipts (million EUR) 2013Receipts (million EUR) 2018Relative to GDP 2018 (%)Expenditure (million EUR) 2013Expenditure (million EUR) 2018Relative to GDP 2018 (%)Balance (million EUR) 2018
EU-27 126011156587 1.286738109167 0.8 47420
Belgium 10074 7528 1.616692 15654 3.4 -8126
Bulgaria 2891 3808 6.8 840 1584 2.8 2224
Czechia 5303 6305 3.0 3494 5056 2.4 1250
Denmark 5385 7719 2.6 7584 8927 3.0 -1208
Germany 31081 36390 1.168793 80933 2.4-44543
Estonia 1256 1510 5.8 803 1245 4.8 265
Ireland 3370 5760 1.8 4669 6399 2.0 -639
Greece 12152 16086 9.0 1835 2191 1.2 13895
Spain 51589 69168 5.712359 22396 1.9 46772
France 53103 55912 2.431787 41398 1.8 14514
Croatia 6136 948918.3 679 1434 2.8 8054
Italy 33063 41712 2.420309 25484 1.4 16228
Cyprus 2211 294013.7 944 1330 6.2 1610
Latvia 651 897 3.1 538 660 2.3 237
Lithuania 1035 1274 2.8 805 1186 2.6 89
Luxembourg 5387 5175 8.6 1896 2813 4.7 2362
Hungary 4043 5850 4.3 1437 2239 1.6 3612
Malta 1057 157412.6 289 440 3.5 1133
Netherlands 10343 15057 1.915589 17680 2.3 -2623
Austria 15237 19559 5.1 7738 10020 2.6 9539
Poland 8405 11759 2.4 6483 8042 1.6 3717
Portugal NA 17053 8.3NA 4585 2.2 12468
Romania 1392 2876 1.4 1508 4522 2.2 -1646
Slovenia 2094 2704 5.9 1068 1482 3.2 1221
Slovakia 1998 2710 3.0 1782 2225 2.5 484
Finland 3044 3104 1.3 3989 5151 2.2 -2047
Sweden 6908 8464 1.810661 13297 2.8 -4833
United Kingdom 35080 42387 1.845556 60006 2.5-17619
Iceland 818 266412.0 642 1437 6.5 1227
Norway NA 5029NA NA 14472NA -9443
Switzerland 12562 15232 2.412819 16147 2.6 -914
Montenegro 666 100121.5 37 58 1.2 943
North Macedonia 201 325 3.0 98 220 2.0 105
Albania NA 185614.5NA 142611.1 430
Serbia 792 1317 3.1 841 1396 3.3 -79
Turkey 21089 21482 3.3 3623 3888 0.6 17594
Bosnia and Herzegovina 516 933 5.5 101 227 1.3 706
Kosovo 648 122818.3 135 302 4.5 926
\n" ], "text/latex": [ "A matrix: 39 × 8 of type chr\n", "\\begin{tabular}{llllllll}\n", " & Receipts (million EUR) 2013 & Receipts (million EUR) 2018 & Relative to GDP 2018 (\\%) & Expenditure (million EUR) 2013 & Expenditure (million EUR) 2018 & Relative to GDP 2018 (\\%) & Balance (million EUR) 2018\\\\\n", "\\hline\n", "\t EU-27 & 126011 & 156587 & 1.2 & 86738 & 109167 & 0.8 & 47420\\\\\n", "\t Belgium & 10074 & 7528 & 1.6 & 16692 & 15654 & 3.4 & -8126\\\\\n", "\t Bulgaria & 2891 & 3808 & 6.8 & 840 & 1584 & 2.8 & 2224\\\\\n", "\t Czechia & 5303 & 6305 & 3.0 & 3494 & 5056 & 2.4 & 1250\\\\\n", "\t Denmark & 5385 & 7719 & 2.6 & 7584 & 8927 & 3.0 & -1208\\\\\n", "\t Germany & 31081 & 36390 & 1.1 & 68793 & 80933 & 2.4 & -44543\\\\\n", "\t Estonia & 1256 & 1510 & 5.8 & 803 & 1245 & 4.8 & 265\\\\\n", "\t Ireland & 3370 & 5760 & 1.8 & 4669 & 6399 & 2.0 & -639\\\\\n", "\t Greece & 12152 & 16086 & 9.0 & 1835 & 2191 & 1.2 & 13895\\\\\n", "\t Spain & 51589 & 69168 & 5.7 & 12359 & 22396 & 1.9 & 46772\\\\\n", "\t France & 53103 & 55912 & 2.4 & 31787 & 41398 & 1.8 & 14514\\\\\n", "\t Croatia & 6136 & 9489 & 18.3 & 679 & 1434 & 2.8 & 8054\\\\\n", "\t Italy & 33063 & 41712 & 2.4 & 20309 & 25484 & 1.4 & 16228\\\\\n", "\t Cyprus & 2211 & 2940 & 13.7 & 944 & 1330 & 6.2 & 1610\\\\\n", "\t Latvia & 651 & 897 & 3.1 & 538 & 660 & 2.3 & 237\\\\\n", "\t Lithuania & 1035 & 1274 & 2.8 & 805 & 1186 & 2.6 & 89\\\\\n", "\t Luxembourg & 5387 & 5175 & 8.6 & 1896 & 2813 & 4.7 & 2362\\\\\n", "\t Hungary & 4043 & 5850 & 4.3 & 1437 & 2239 & 1.6 & 3612\\\\\n", "\t Malta & 1057 & 1574 & 12.6 & 289 & 440 & 3.5 & 1133\\\\\n", "\t Netherlands & 10343 & 15057 & 1.9 & 15589 & 17680 & 2.3 & -2623\\\\\n", "\t Austria & 15237 & 19559 & 5.1 & 7738 & 10020 & 2.6 & 9539\\\\\n", "\t Poland & 8405 & 11759 & 2.4 & 6483 & 8042 & 1.6 & 3717\\\\\n", "\t Portugal & NA & 17053 & 8.3 & NA & 4585 & 2.2 & 12468\\\\\n", "\t Romania & 1392 & 2876 & 1.4 & 1508 & 4522 & 2.2 & -1646\\\\\n", "\t Slovenia & 2094 & 2704 & 5.9 & 1068 & 1482 & 3.2 & 1221\\\\\n", "\t Slovakia & 1998 & 2710 & 3.0 & 1782 & 2225 & 2.5 & 484\\\\\n", "\t Finland & 3044 & 3104 & 1.3 & 3989 & 5151 & 2.2 & -2047\\\\\n", "\t Sweden & 6908 & 8464 & 1.8 & 10661 & 13297 & 2.8 & -4833\\\\\n", "\t United Kingdom & 35080 & 42387 & 1.8 & 45556 & 60006 & 2.5 & -17619\\\\\n", "\t Iceland & 818 & 2664 & 12.0 & 642 & 1437 & 6.5 & 1227\\\\\n", "\t Norway & NA & 5029 & NA & NA & 14472 & NA & -9443\\\\\n", "\t Switzerland & 12562 & 15232 & 2.4 & 12819 & 16147 & 2.6 & -914\\\\\n", "\t Montenegro & 666 & 1001 & 21.5 & 37 & 58 & 1.2 & 943\\\\\n", "\t North Macedonia & 201 & 325 & 3.0 & 98 & 220 & 2.0 & 105\\\\\n", "\t Albania & NA & 1856 & 14.5 & NA & 1426 & 11.1 & 430\\\\\n", "\t Serbia & 792 & 1317 & 3.1 & 841 & 1396 & 3.3 & -79\\\\\n", "\t Turkey & 21089 & 21482 & 3.3 & 3623 & 3888 & 0.6 & 17594\\\\\n", "\t Bosnia and Herzegovina & 516 & 933 & 5.5 & 101 & 227 & 1.3 & 706\\\\\n", "\t Kosovo & 648 & 1228 & 18.3 & 135 & 302 & 4.5 & 926\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 39 × 8 of type chr\n", "\n", "| | Receipts (million EUR) 2013 | Receipts (million EUR) 2018 | Relative to GDP 2018 (%) | Expenditure (million EUR) 2013 | Expenditure (million EUR) 2018 | Relative to GDP 2018 (%) | Balance (million EUR) 2018 |\n", "|---|---|---|---|---|---|---|---|\n", "| EU-27 | 126011 | 156587 | 1.2 | 86738 | 109167 | 0.8 | 47420 |\n", "| Belgium | 10074 | 7528 | 1.6 | 16692 | 15654 | 3.4 | -8126 |\n", "| Bulgaria | 2891 | 3808 | 6.8 | 840 | 1584 | 2.8 | 2224 |\n", "| Czechia | 5303 | 6305 | 3.0 | 3494 | 5056 | 2.4 | 1250 |\n", "| Denmark | 5385 | 7719 | 2.6 | 7584 | 8927 | 3.0 | -1208 |\n", "| Germany | 31081 | 36390 | 1.1 | 68793 | 80933 | 2.4 | -44543 |\n", "| Estonia | 1256 | 1510 | 5.8 | 803 | 1245 | 4.8 | 265 |\n", "| Ireland | 3370 | 5760 | 1.8 | 4669 | 6399 | 2.0 | -639 |\n", "| Greece | 12152 | 16086 | 9.0 | 1835 | 2191 | 1.2 | 13895 |\n", "| Spain | 51589 | 69168 | 5.7 | 12359 | 22396 | 1.9 | 46772 |\n", "| France | 53103 | 55912 | 2.4 | 31787 | 41398 | 1.8 | 14514 |\n", "| Croatia | 6136 | 9489 | 18.3 | 679 | 1434 | 2.8 | 8054 |\n", "| Italy | 33063 | 41712 | 2.4 | 20309 | 25484 | 1.4 | 16228 |\n", "| Cyprus | 2211 | 2940 | 13.7 | 944 | 1330 | 6.2 | 1610 |\n", "| Latvia | 651 | 897 | 3.1 | 538 | 660 | 2.3 | 237 |\n", "| Lithuania | 1035 | 1274 | 2.8 | 805 | 1186 | 2.6 | 89 |\n", "| Luxembourg | 5387 | 5175 | 8.6 | 1896 | 2813 | 4.7 | 2362 |\n", "| Hungary | 4043 | 5850 | 4.3 | 1437 | 2239 | 1.6 | 3612 |\n", "| Malta | 1057 | 1574 | 12.6 | 289 | 440 | 3.5 | 1133 |\n", "| Netherlands | 10343 | 15057 | 1.9 | 15589 | 17680 | 2.3 | -2623 |\n", "| Austria | 15237 | 19559 | 5.1 | 7738 | 10020 | 2.6 | 9539 |\n", "| Poland | 8405 | 11759 | 2.4 | 6483 | 8042 | 1.6 | 3717 |\n", "| Portugal | NA | 17053 | 8.3 | NA | 4585 | 2.2 | 12468 |\n", "| Romania | 1392 | 2876 | 1.4 | 1508 | 4522 | 2.2 | -1646 |\n", "| Slovenia | 2094 | 2704 | 5.9 | 1068 | 1482 | 3.2 | 1221 |\n", "| Slovakia | 1998 | 2710 | 3.0 | 1782 | 2225 | 2.5 | 484 |\n", "| Finland | 3044 | 3104 | 1.3 | 3989 | 5151 | 2.2 | -2047 |\n", "| Sweden | 6908 | 8464 | 1.8 | 10661 | 13297 | 2.8 | -4833 |\n", "| United Kingdom | 35080 | 42387 | 1.8 | 45556 | 60006 | 2.5 | -17619 |\n", "| Iceland | 818 | 2664 | 12.0 | 642 | 1437 | 6.5 | 1227 |\n", "| Norway | NA | 5029 | NA | NA | 14472 | NA | -9443 |\n", "| Switzerland | 12562 | 15232 | 2.4 | 12819 | 16147 | 2.6 | -914 |\n", "| Montenegro | 666 | 1001 | 21.5 | 37 | 58 | 1.2 | 943 |\n", "| North Macedonia | 201 | 325 | 3.0 | 98 | 220 | 2.0 | 105 |\n", "| Albania | NA | 1856 | 14.5 | NA | 1426 | 11.1 | 430 |\n", "| Serbia | 792 | 1317 | 3.1 | 841 | 1396 | 3.3 | -79 |\n", "| Turkey | 21089 | 21482 | 3.3 | 3623 | 3888 | 0.6 | 17594 |\n", "| Bosnia and Herzegovina | 516 | 933 | 5.5 | 101 | 227 | 1.3 | 706 |\n", "| Kosovo | 648 | 1228 | 18.3 | 135 | 302 | 4.5 | 926 |\n", "\n" ], "text/plain": [ " Receipts (million EUR) 2013\n", " [1,] EU-27 126011 \n", " [2,] Belgium 10074 \n", " [3,] Bulgaria 2891 \n", " [4,] Czechia 5303 \n", " [5,] Denmark 5385 \n", " [6,] Germany 31081 \n", " [7,] Estonia 1256 \n", " [8,] Ireland 3370 \n", " [9,] Greece 12152 \n", "[10,] Spain 51589 \n", "[11,] France 53103 \n", "[12,] Croatia 6136 \n", "[13,] Italy 33063 \n", "[14,] Cyprus 2211 \n", "[15,] Latvia 651 \n", "[16,] Lithuania 1035 \n", "[17,] Luxembourg 5387 \n", "[18,] Hungary 4043 \n", "[19,] Malta 1057 \n", "[20,] Netherlands 10343 \n", "[21,] Austria 15237 \n", "[22,] Poland 8405 \n", "[23,] Portugal NA \n", "[24,] Romania 1392 \n", "[25,] Slovenia 2094 \n", "[26,] Slovakia 1998 \n", "[27,] Finland 3044 \n", "[28,] Sweden 6908 \n", "[29,] United Kingdom 35080 \n", "[30,] Iceland 818 \n", "[31,] Norway NA \n", "[32,] Switzerland 12562 \n", "[33,] Montenegro 666 \n", "[34,] North Macedonia 201 \n", "[35,] Albania NA \n", "[36,] Serbia 792 \n", "[37,] Turkey 21089 \n", "[38,] Bosnia and Herzegovina 516 \n", "[39,] Kosovo 648 \n", " Receipts (million EUR) 2018 Relative to GDP 2018 (%)\n", " [1,] 156587 1.2 \n", " [2,] 7528 1.6 \n", " [3,] 3808 6.8 \n", " [4,] 6305 3.0 \n", " [5,] 7719 2.6 \n", " [6,] 36390 1.1 \n", " [7,] 1510 5.8 \n", " [8,] 5760 1.8 \n", " [9,] 16086 9.0 \n", "[10,] 69168 5.7 \n", "[11,] 55912 2.4 \n", "[12,] 9489 18.3 \n", "[13,] 41712 2.4 \n", "[14,] 2940 13.7 \n", "[15,] 897 3.1 \n", "[16,] 1274 2.8 \n", "[17,] 5175 8.6 \n", "[18,] 5850 4.3 \n", "[19,] 1574 12.6 \n", "[20,] 15057 1.9 \n", "[21,] 19559 5.1 \n", "[22,] 11759 2.4 \n", "[23,] 17053 8.3 \n", "[24,] 2876 1.4 \n", "[25,] 2704 5.9 \n", "[26,] 2710 3.0 \n", "[27,] 3104 1.3 \n", "[28,] 8464 1.8 \n", "[29,] 42387 1.8 \n", "[30,] 2664 12.0 \n", "[31,] 5029 NA \n", "[32,] 15232 2.4 \n", "[33,] 1001 21.5 \n", "[34,] 325 3.0 \n", "[35,] 1856 14.5 \n", "[36,] 1317 3.1 \n", "[37,] 21482 3.3 \n", "[38,] 933 5.5 \n", "[39,] 1228 18.3 \n", " Expenditure (million EUR) 2013 Expenditure (million EUR) 2018\n", " [1,] 86738 109167 \n", " [2,] 16692 15654 \n", " [3,] 840 1584 \n", " [4,] 3494 5056 \n", " [5,] 7584 8927 \n", " [6,] 68793 80933 \n", " [7,] 803 1245 \n", " [8,] 4669 6399 \n", " [9,] 1835 2191 \n", "[10,] 12359 22396 \n", "[11,] 31787 41398 \n", "[12,] 679 1434 \n", "[13,] 20309 25484 \n", "[14,] 944 1330 \n", "[15,] 538 660 \n", "[16,] 805 1186 \n", "[17,] 1896 2813 \n", "[18,] 1437 2239 \n", "[19,] 289 440 \n", "[20,] 15589 17680 \n", "[21,] 7738 10020 \n", "[22,] 6483 8042 \n", "[23,] NA 4585 \n", "[24,] 1508 4522 \n", "[25,] 1068 1482 \n", "[26,] 1782 2225 \n", "[27,] 3989 5151 \n", "[28,] 10661 13297 \n", "[29,] 45556 60006 \n", "[30,] 642 1437 \n", "[31,] NA 14472 \n", "[32,] 12819 16147 \n", "[33,] 37 58 \n", "[34,] 98 220 \n", "[35,] NA 1426 \n", "[36,] 841 1396 \n", "[37,] 3623 3888 \n", "[38,] 101 227 \n", "[39,] 135 302 \n", " Relative to GDP 2018 (%) Balance (million EUR) 2018\n", " [1,] 0.8 47420 \n", " [2,] 3.4 -8126 \n", " [3,] 2.8 2224 \n", " [4,] 2.4 1250 \n", " [5,] 3.0 -1208 \n", " [6,] 2.4 -44543 \n", " [7,] 4.8 265 \n", " [8,] 2.0 -639 \n", " [9,] 1.2 13895 \n", "[10,] 1.9 46772 \n", "[11,] 1.8 14514 \n", "[12,] 2.8 8054 \n", "[13,] 1.4 16228 \n", "[14,] 6.2 1610 \n", "[15,] 2.3 237 \n", "[16,] 2.6 89 \n", "[17,] 4.7 2362 \n", "[18,] 1.6 3612 \n", "[19,] 3.5 1133 \n", "[20,] 2.3 -2623 \n", "[21,] 2.6 9539 \n", "[22,] 1.6 3717 \n", "[23,] 2.2 12468 \n", "[24,] 2.2 -1646 \n", "[25,] 3.2 1221 \n", "[26,] 2.5 484 \n", "[27,] 2.2 -2047 \n", "[28,] 2.8 -4833 \n", "[29,] 2.5 -17619 \n", "[30,] 6.5 1227 \n", "[31,] NA -9443 \n", "[32,] 2.6 -914 \n", "[33,] 1.2 943 \n", "[34,] 2.0 105 \n", "[35,] 11.1 430 \n", "[36,] 3.3 -79 \n", "[37,] 0.6 17594 \n", "[38,] 1.3 706 \n", "[39,] 4.5 926 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table3_final <- rbind(table3_EU,table3)\n", "\n", "as.matrix(table3_final)" ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 4 }