R Tools for Eurostat Open Data

This rOpenGov R package provides tools to access Eurostat database, which you can also browse on-line for the data sets and documentation. For contact information and source code, see the package website.

Installation

Release version (CRAN):

install.packages("eurostat")

Development version (Github):

library(devtools)
install_github("ropengov/eurostat")

Overall, the eurostat package includes the following functions:

Finding data

Function get_eurostat_toc() downloads a table of contents of eurostat datasets. The values in column ‘code’ should be used to download a selected dataset.

# Load the package
library(eurostat)
library(rvest)

# Get Eurostat data listing
toc <- get_eurostat_toc()

# Check the first items
library(knitr)
kable(head(toc))
title code type last update of data last table structure change data start data end values
Database by themes data folder NA NA NA NA NA
General and regional statistics general folder NA NA NA NA NA
European and national indicators for short-term analysis euroind folder NA NA NA NA NA
Business and consumer surveys (source: DG ECFIN) ei_bcs folder NA NA NA NA NA
Consumer surveys (source: DG ECFIN) ei_bcs_cs folder NA NA NA NA NA
Consumers - monthly data ei_bsco_m dataset 30.03.2017 30.03.2017 1980M01 2017M03 NA

With search_eurostat() you can search the table of contents for particular patterns, e.g. all datasets related to passenger transport. The kable function to produces nice markdown output. Note that with the type argument of this function you could restrict the search to for instance datasets or tables.

# info about passengers
kable(head(search_eurostat("passenger transport")))
title code type last update of data last table structure change data start data end values
Volume of passenger transport relative to GDP tran_hv_pstra dataset 03.08.2016 03.08.2016 2000 2014 NA
Modal split of passenger transport tran_hv_psmod dataset 03.08.2016 02.08.2016 1990 2014 NA
Railway transport - Total annual passenger transport (1 000 pass., million pkm) rail_pa_total dataset 07.04.2017 08.11.2016 2004 2015 NA
International railway passenger transport from the reporting country to the country of disembarkation (1 000 passengers) rail_pa_intgong dataset 07.04.2017 21.09.2016 2002 2015 NA
International railway passenger transport from the country of embarkation to the reporting country (1 000 passengers) rail_pa_intcmng dataset 07.04.2017 26.05.2016 2002 2015 NA
Air passenger transport by reporting country avia_paoc dataset 19.12.2016 19.12.2016 1993 2016Q3 NA

Codes for the dataset can be searched also from the Eurostat database. The Eurostat database gives codes in the Data Navigation Tree after every dataset in parenthesis.

Downloading data

The package supports two of the Eurostats download methods: the bulk download facility and the Web Services’ JSON API. The bulk download facility is the fastest method to download whole datasets. It is also often the only way as the JSON API has limitation of maximum 50 sub-indicators at a time and whole datasets usually exceeds that. To download only a small section of the dataset the JSON API is faster, as it allows to make a data selection before downloading.

A user does not usually have to bother with methods, as both are used via main function get_eurostat(). If only the table id is given, the whole table is downloaded from the bulk download facility. If also filters are defined the JSON API is used.

Here an example of indicator ‘Modal split of passenger transport’. This is the percentage share of each mode of transport in total inland transport, expressed in passenger-kilometres (pkm) based on transport by passenger cars, buses and coaches, and trains. All data should be based on movements on national territory, regardless of the nationality of the vehicle. However, the data collection is not harmonized at the EU level.

Pick and print the id of the data set to download:

# For the original data, see
# http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tsdtr210
id <- search_eurostat("Modal split of passenger transport", 
                         type = "table")$code[1]
print(id)

[1] “tsdtr210”

Get the whole corresponding table. As the table is annual data, it is more convient to use a numeric time variable than use the default date format:

dat <- get_eurostat(id, time_format = "num")

Investigate the structure of the downloaded data set:

str(dat)
## Classes 'tbl_df', 'tbl' and 'data.frame':    2326 obs. of  5 variables:
##  $ unit   : Factor w/ 1 level "PC": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vehicle: Factor w/ 3 levels "BUS_TOT","CAR",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ geo    : Factor w/ 35 levels "AT","BE","CH",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ time   : num  1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
##  $ values : num  11 10.6 3.7 9.1 11.3 32.4 14.9 13.5 6 24.8 ...
kable(head(dat))
unit vehicle geo time values
PC BUS_TOT AT 1990 11.0
PC BUS_TOT BE 1990 10.6
PC BUS_TOT CH 1990 3.7
PC BUS_TOT DE 1990 9.1
PC BUS_TOT DK 1990 11.3
PC BUS_TOT EL 1990 32.4

Or you can get only a part of the dataset by defining filters argument. It should be named list, where names corresponds to variable names (lower case) and values are vectors of codes corresponding desidered series (upper case). For time variable, in addition to a time, also a sinceTimePeriod and a lastTimePeriod can be used.

dat2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), lastTimePeriod=1), time_format = "num")
kable(dat2)

Replacing codes with labels

By default variables are returned as Eurostat codes, but to get human-readable labels instead, use a type = "label" argument.

datl2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), 
                                         lastTimePeriod = 1), 
                      type = "label", time_format = "num")
kable(head(datl2))

Eurostat codes in the downloaded data set can be replaced with human-readable labels from the Eurostat dictionaries with the label_eurostat() function.

datl <- label_eurostat(dat)
kable(head(datl))
unit vehicle geo time values
Percentage Motor coaches, buses and trolley buses Austria 1990 11.0
Percentage Motor coaches, buses and trolley buses Belgium 1990 10.6
Percentage Motor coaches, buses and trolley buses Switzerland 1990 3.7
Percentage Motor coaches, buses and trolley buses Germany (until 1990 former territory of the FRG) 1990 9.1
Percentage Motor coaches, buses and trolley buses Denmark 1990 11.3
Percentage Motor coaches, buses and trolley buses Greece 1990 32.4

The label_eurostat() allows conversion of individual variable vectors or variable names as well.

label_eurostat_vars(names(datl))

Vehicle information has 3 levels. You can check them now with:

levels(datl$vehicle)

Selecting and modifying data

EFTA, Eurozone, EU and EU candidate countries

To facilitate smooth visualization of standard European geographic areas, the package provides ready-made lists of the country codes used in the eurostat database for EFTA (efta_countries), Euro area (ea_countries), EU (eu_countries) and EU candidate countries (eu_candidate_countries). These can be used to select specific groups of countries for closer investigation. For conversions with other standard country coding systems, see the countrycode R package. To retrieve the country code list for EFTA, for instance, use:

data(efta_countries)
kable(efta_countries)
code name
IS Iceland
LI Liechtenstein
NO Norway
CH Switzerland

EU data from 2012 in all vehicles:

dat_eu12 <- subset(datl, geo == "European Union (28 countries)" & time == 2012)
kable(dat_eu12, row.names = FALSE)
unit vehicle geo time values
Percentage Motor coaches, buses and trolley buses European Union (28 countries) 2012 9.3
Percentage Passenger cars European Union (28 countries) 2012 83.0
Percentage Trains European Union (28 countries) 2012 7.7

EU data from 2000 - 2012 with vehicle types as variables:

Reshaping the data is best done with spread() in tidyr.

library("tidyr")
dat_eu_0012 <- subset(dat, geo == "EU28" & time %in% 2000:2012)
dat_eu_0012_wide <- spread(dat_eu_0012, vehicle, values)
kable(subset(dat_eu_0012_wide, select = -geo), row.names = FALSE)
unit time BUS_TOT CAR TRN
PC 2000 10.4 82.4 7.2
PC 2001 10.2 82.7 7.1
PC 2002 9.9 83.3 6.8
PC 2003 9.9 83.5 6.7
PC 2004 9.8 83.4 6.8
PC 2005 9.9 83.2 6.9
PC 2006 9.7 83.2 7.1
PC 2007 9.8 83.1 7.2
PC 2008 9.7 83.1 7.3
PC 2009 9.2 83.7 7.1
PC 2010 9.2 83.6 7.2
PC 2011 9.2 83.4 7.3
PC 2012 9.3 83.0 7.7

Train passengers for selected EU countries in 2000 - 2012

dat_trains <- subset(datl, geo %in% c("Austria", "Belgium", "Finland", "Sweden")
                     & time %in% 2000:2012 
                     & vehicle == "Trains")

dat_trains_wide <- spread(dat_trains, geo, values) 
kable(subset(dat_trains_wide, select = -vehicle), row.names = FALSE)
unit time Austria Belgium Finland Sweden
Percentage 2000 9.7 6.3 5.1 7.5
Percentage 2001 9.7 6.4 4.8 7.9
Percentage 2002 9.7 6.5 4.8 7.8
Percentage 2003 9.5 6.5 4.7 7.7
Percentage 2004 9.4 7.1 4.7 7.5
Percentage 2005 9.8 6.6 4.8 7.7
Percentage 2006 10.0 6.9 4.8 8.3
Percentage 2007 10.0 7.1 5.0 8.7
Percentage 2008 11.1 7.5 5.4 9.4
Percentage 2009 11.1 7.5 5.1 9.5
Percentage 2010 11.0 7.7 5.2 9.4
Percentage 2011 11.3 7.7 5.0 8.8
Percentage 2012 11.8 7.8 5.3 9.1

Visualization

Visualizing train passenger data with ggplot2:

library(ggplot2)
p <- ggplot(dat_trains, aes(x = time, y = values, colour = geo)) 
p <- p + geom_line()
print(p)

Triangle plot

Triangle plot is handy for visualizing data sets with three variables.

library(tidyr)
library(plotrix)
library(eurostat)
library(dplyr)
library(tidyr)

# All sources of renewable energy are to be grouped into three sets
 dict <- c("Solid biofuels (excluding charcoal)" = "Biofuels",
 "Biogasoline" = "Biofuels",
 "Other liquid biofuels" = "Biofuels",
 "Biodiesels" = "Biofuels",
 "Biogas" = "Biofuels",
 "Hydro power" = "Hydro power",
 "Tide, Wave and Ocean" = "Hydro power",
 "Solar thermal" = "Wind, solar, waste and Other",
 "Geothermal Energy" = "Wind, solar, waste and Other",
 "Solar photovoltaic" = "Wind, solar, waste and Other",
 "Municipal waste (renewable)" = "Wind, solar, waste and Other",
 "Wind power" = "Wind, solar, waste and Other",
 "Bio jet kerosene" = "Wind, solar, waste and Other")
# Some cleaning of the data is required
 energy3 <- get_eurostat("ten00081") %>%
 label_eurostat(dat) %>%
 filter(time == "2013-01-01",
 product != "Renewable energies") %>%
 mutate(nproduct = dict[as.character(product)], # just three categories
 geo = gsub(geo, pattern=" \\(.*", replacement="")) %>%
 select(nproduct, geo, values) %>%
 group_by(nproduct, geo) %>%
 summarise(svalue = sum(values)) %>%
 group_by(geo) %>%
 mutate(tvalue = sum(svalue),
 svalue = svalue/sum(svalue)) %>%
 filter(tvalue > 1000) %>% # only large countries
 spread(nproduct, svalue)
 
# Triangle plot
 par(cex=0.75, mar=c(0,0,0,0))
 positions <- plotrix::triax.plot(as.matrix(energy3[, c(3,5,4)]),
                     show.grid = TRUE,
                     label.points= FALSE, point.labels = energy3$geo,
                     col.axis="gray50", col.grid="gray90",
                     pch = 19, cex.axis=0.8, cex.ticks=0.7, col="grey50")

 # Larger labels
 ind <- which(energy3$geo %in%  c("Norway", "Iceland","Denmark","Estonia", "Turkey", "Italy", "Finland"))
 df <- data.frame(positions$xypos, geo = energy3$geo)
 points(df$x[ind], df$y[ind], cex=2, col="red", pch=19)
 text(df$x[ind], df$y[ind], df$geo[ind], adj = c(0.5,-1), cex=1.5)

Maps

Disposable income of private households by NUTS 2 regions at 1:60mln resolution using ggplot2

library(eurostat)
library(dplyr)
library(ggplot2)
# Data from Eurostat
eurostat::get_eurostat("tgs00026", time_format = "raw") %>% 
  # subset to have only a single row per geo
  dplyr::filter(time == 2010, nchar(as.character(geo)) == 4) %>% 
  # categorise
  dplyr::mutate(cat = cut_to_classes(values, n = 5)) %>% 
  # merge with geodata
  merge_eurostat_geodata(data=.,geocolumn="geo",resolution = "60", output_class = "df", all_regions = TRUE) %>% 
  # plot map
  ggplot(data=., aes(x=long,y=lat,group=group)) +
  geom_polygon(aes(fill=cat),color="white", size=.1) +
  scale_fill_brewer(palette ="Oranges")
## Reading cache file /tmp/RtmpQTJy2h/eurostat/tgs00026_raw_code_TF.rds
## Table  tgs00026  read from cache file:  /tmp/RtmpQTJy2h/eurostat/tgs00026_raw_code_TF.rds
## 
##       COPYRIGHT NOTICE
## 
##       When data downloaded from this page 
##       <http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units>
##       is used in any printed or electronic publication, 
##       in addition to any other provisions 
##       applicable to the whole Eurostat website, 
##       data source will have to be acknowledged 
##       in the legend of the map and 
##       in the introductory page of the publication 
##       with the following copyright notice:
## 
##       - EN: (C) EuroGeographics for the administrative boundaries
##       - FR: (C) EuroGeographics pour les limites administratives
##       - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen
## 
##       For publications in languages other than 
##       English, French or German, 
##       the translation of the copyright notice 
##       in the language of the publication shall be used.
## 
##       If you intend to use the data commercially, 
##       please contact EuroGeographics for 
##       information regarding their licence agreements.
## 
## Reading cache file /tmp/RtmpQTJy2h/eurostat/df60.RData
## data_frame at resolution 1: 60  read from cache file:  /tmp/RtmpQTJy2h/eurostat/df60.RData

Disposable income of private households by NUTS 2 regions in Poland with labels at 1:1mln resolution using ggplot2

library(eurostat)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
# Downloading and manipulating the tabular data
df <- get_eurostat("tgs00026", time_format = "raw") %>% 
  # subsetting to year 2005 and NUTS-3 level
  dplyr::filter(time == 2005, nchar(as.character(geo)) == 4, grepl("PL",geo)) %>% 
  # label the single geo column
  mutate(label = label_eurostat(.)[["geo"]],
         cat = cut_to_classes(values)) %>% 
  # merge with geodata
  merge_eurostat_geodata(data=.,geocolumn="geo",resolution = "01", all_regions = FALSE, output_class="df")
## Reading cache file /tmp/RtmpQTJy2h/eurostat/tgs00026_raw_code_TF.rds
## Table  tgs00026  read from cache file:  /tmp/RtmpQTJy2h/eurostat/tgs00026_raw_code_TF.rds
## 
##       COPYRIGHT NOTICE
## 
##       When data downloaded from this page 
##       <http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units>
##       is used in any printed or electronic publication, 
##       in addition to any other provisions 
##       applicable to the whole Eurostat website, 
##       data source will have to be acknowledged 
##       in the legend of the map and 
##       in the introductory page of the publication 
##       with the following copyright notice:
## 
##       - EN: (C) EuroGeographics for the administrative boundaries
##       - FR: (C) EuroGeographics pour les limites administratives
##       - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen
## 
##       For publications in languages other than 
##       English, French or German, 
##       the translation of the copyright notice 
##       in the language of the publication shall be used.
## 
##       If you intend to use the data commercially, 
##       please contact EuroGeographics for 
##       information regarding their licence agreements.
## 
## data_frame at resolution 1: 01  cached at:  /tmp/RtmpQTJy2h/eurostat/df01.RData
# plot map
p <- ggplot(data=df, aes(long,lat,group=group))
p <- p + geom_polygon(aes(fill = cat),colour="white",size=.8)
p <- p + scale_fill_manual(values=brewer.pal(n = 5, name = "Oranges"))

p <- p + geom_label(data=df %>% group_by(label,values,cat) %>% summarise(long = mean(long),
                                                         lat = mean(lat)), 
                    aes(long, lat, label = paste(label,"\n",values,"€"), group=label,fill=cat), 
                    size=3.5, color="white", fontface="bold", lineheight=.8, show.legend=FALSE)
p <- p + labs(title = paste0("Disposable household incomes in 2005"))
p <- p + guides(fill = guide_legend(title = "EUR per Year",title.position = "top", title.hjust=0))
p

Disposable income of private households by NUTS 2 regions at 1:60mln resolution using spplot

library(sp)
library(eurostat)
library(dplyr)
dat <- get_eurostat("tgs00026", time_format = "raw") %>% 
  # subsetting to year 2005 and NUTS-3 level
  dplyr::filter(time == 2005, nchar(as.character(geo)) == 4) %>% 
  # classifying the values the variable
  dplyr::mutate(cat = cut_to_classes(values)) %>% 
  # merge Eurostat data with geodata from Cisco
  merge_eurostat_geodata(data=.,geocolumn="geo",resolution = "10", output_class ="spdf", all_regions=FALSE) 
## Reading cache file /tmp/RtmpQTJy2h/eurostat/tgs00026_raw_code_TF.rds
## Table  tgs00026  read from cache file:  /tmp/RtmpQTJy2h/eurostat/tgs00026_raw_code_TF.rds
## 
##       COPYRIGHT NOTICE
## 
##       When data downloaded from this page 
##       <http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units>
##       is used in any printed or electronic publication, 
##       in addition to any other provisions 
##       applicable to the whole Eurostat website, 
##       data source will have to be acknowledged 
##       in the legend of the map and 
##       in the introductory page of the publication 
##       with the following copyright notice:
## 
##       - EN: (C) EuroGeographics for the administrative boundaries
##       - FR: (C) EuroGeographics pour les limites administratives
##       - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen
## 
##       For publications in languages other than 
##       English, French or German, 
##       the translation of the copyright notice 
##       in the language of the publication shall be used.
## 
##       If you intend to use the data commercially, 
##       please contact EuroGeographics for 
##       information regarding their licence agreements.
## 
## SpatialPolygonDataFrame at resolution 1: 10  cached at:  /tmp/RtmpQTJy2h/eurostat/spdf10.RData
# plot map
sp::spplot(obj = dat, "cat", main = "Disposable household income",
       xlim=c(-22,34), ylim=c(35,70), 
           col.regions = c("dim grey", brewer.pal(n = 5, name = "Oranges")),
       col = "white", usePolypath = FALSE)

SDMX

Eurostat data is available also in the SDMX format. The eurostat R package does not provide custom tools for this but the generic rsdmx R package can be used to access data in that format when necessary:

library(rsdmx)

# Data set URL
url <- "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/cdh_e_fos/..PC.FOS1.BE/?startperiod=2005&endPeriod=2011 "

# Read the data from eurostat
d <- readSDMX(url)

# Convert to data frame and show the first entries
df <- as.data.frame(d)

kable(head(df))
UNIT Y_GRAD FOS07 GEO FREQ obsTime obsValue OBS_STATUS
PC TOTAL FOS1 BE A 2009 NA na
PC TOTAL FOS1 BE A 2006 NA na
PC Y_GE1990 FOS1 BE A 2009 43.75 NA
PC Y_GE1990 FOS1 BE A 2006 NA na

Further examples

For further examples, see the package homepage.

Version info

This tutorial was created with

sessionInfo()
## R version 3.3.2 (2016-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.2 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=de_BE.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=de_BE.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=de_BE.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rsdmx_0.5-8          sp_1.2-4             RColorBrewer_1.1-2  
##  [4] rvest_0.3.2          xml2_1.1.1           ggplot2_2.2.1       
##  [7] plotrix_3.6-3        dplyr_0.5.0          tidyr_0.6.1         
## [10] xtable_1.8-2         eurostat_3.1.100091  pkgdown_0.1.0.9000  
## [13] knitr_1.15.1         devtools_1.12.0.9000
## 
## loaded via a namespace (and not attached):
##  [1] purrr_0.2.2         lattice_0.20-34     tcltk_3.3.2        
##  [4] colorspace_1.3-2    testthat_1.0.2      htmltools_0.3.5    
##  [7] yaml_2.1.14         XML_3.98-1.5        pkgbuild_0.0.0.9000
## [10] e1071_1.6-8         withr_1.0.2         DBI_0.6            
## [13] plyr_1.8.4          stringr_1.2.0       munsell_0.4.3      
## [16] commonmark_1.2      gtable_0.2.0        mapproj_1.2-4      
## [19] evaluate_0.10       memoise_1.0.0       labeling_0.3       
## [22] highlight_0.4.7     Cairo_1.5-9         curl_2.3           
## [25] class_7.3-14        highr_0.6           Rcpp_0.12.10       
## [28] readr_1.1.0         backports_1.0.5     scales_0.4.1       
## [31] classInt_0.1-23     desc_1.1.0          pkgload_0.0.0.9000 
## [34] jsonlite_1.3        hms_0.3             digest_0.6.12      
## [37] stringi_1.1.5       ggrepel_0.6.9       grid_3.3.2         
## [40] rprojroot_1.2       bitops_1.0-6        rgdal_1.2-5        
## [43] tools_3.3.2         maps_3.1.1          magrittr_1.5       
## [46] RCurl_1.95-4.8      lazyeval_0.2.0      tibble_1.3.0       
## [49] crayon_1.3.2        whisker_0.4         assertthat_0.1     
## [52] rmarkdown_1.3.9004  roxygen2_6.0.1      httr_1.2.1         
## [55] R6_2.2.0