Installation

Release version:

install.packages("eurostat")

Development version:

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

Overall, the eurostat package includes the following functions:

clean_eurostat_cache    Clean Eurostat Cache
dic_order               Order of Variable Levels from Eurostat
                        Dictionary.
eu_countries            Countries and Country Codes
eurostat-package        R Tools for Eurostat open data
eurotime2date           Date Conversion from Eurostat Time Format
eurotime2num            Conversion of Eurostat Time Format to Numeric
get_eurostat            Read Eurostat Data
get_eurostat_dic        Download Eurostat Dictionary
get_eurostat_json       Get Data from Eurostat API in JSON
get_eurostat_raw        Download Data from Eurostat Database
get_eurostat_toc        Download Table of Contents of Eurostat Data
                        Sets
harmonize_country_code
                        Harmonize Country Code
label_eurostat          Get Eurostat Codes
search_eurostat         Grep Datasets Titles from Eurostat

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))

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")))

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:

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)

## '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))

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)

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

datl <- label_eurostat(dat)
kable(head(datl))

label_eurostat_vars(names(datl))

levels(datl$vehicle)

Selecting and modifying data

data(efta_countries)
kable(efta_countries)

dat_eu12 <- subset(datl, geo == "European Union (28 countries)" & time == 2012)
kable(dat_eu12, row.names = FALSE)

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)

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)

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 on passenger transport distributions with 2012 data for all countries with data.

library(tidyr)

transports <- spread(subset(dat, time == 2012, select = c(geo, vehicle, values)), vehicle, values)

transports <- na.omit(transports)

# triangle plot
library(plotrix)
triax.plot(transports[, -1], show.grid = TRUE, 
           label.points = TRUE, point.labels = transports$geo, 
           pch = 19)

citation("eurostat")

## 
## Kindly cite the eurostat R package as follows:
## 
##   (C) Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek
##   2014-2016. eurostat R package URL:
##   https://github.com/rOpenGov/eurostat
## 
## A BibTeX entry for LaTeX users is
## 
##   @Misc{,
##     title = {eurostat R package},
##     author = {Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek},
##     year = {2014-2016},
##     url = {https://github.com/rOpenGov/eurostat},
##   }

Acknowledgements

We are grateful to all contributors and Eurostat open data portal! This rOpenGov R package is based on earlier CRAN packages statfi and smarterpoland. The datamart and reurostat packages seem to develop related Eurostat tools but at the time of writing this tutorial this package seems to be in an experimental stage. The quandl package may also provides access to some versions of eurostat data sets.

Session info

This tutorial was created with

sessionInfo()

## R version 3.3.1 (2016-06-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] plotrix_3.6-2   ggplot2_2.1.0   tidyr_0.5.1     rvest_0.3.2    
## [5] xml2_1.0.0      eurostat_1.2.24 knitr_1.13     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.5        magrittr_1.5       munsell_0.4.3     
##  [4] colorspace_1.2-6   R6_2.1.2           plyr_1.8.4        
##  [7] stringr_1.0.0      httr_1.2.1         highr_0.6         
## [10] tools_3.3.1        grid_3.3.1         gtable_0.2.0      
## [13] htmltools_0.3.5    yaml_2.1.13        digest_0.6.9      
## [16] assertthat_0.1     tibble_1.1         formatR_1.4       
## [19] curl_0.9.7         evaluate_0.9       rmarkdown_0.9.6.14
## [22] labeling_0.3       stringi_1.1.1      scales_0.4.0      
## [25] jsonlite_1.0