Eurostat R tools

This R package provides tools to access Eurostat database as part of the rOpenGov project.

For contact information and source code, see the github page

Available tools

• Installation
• Finding data
• Downloading data
  
• Replacing codes with labels
  
• Selecting and modifying data
  
• Visualization
  
  • Triangle plot
    
• Citing the package
  
• Acknowledgements
  
• Session info
  

Installation

Release version:

install.packages("eurostat")

Development version:

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

Overall, the eurostat package includes the following functions:

library(eurostat)
eurostat.functions <- sort(ls("package:eurostat"))
kable(as.data.frame(eurostat.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))

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':    2520 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","BG",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ time   : num  2013 2013 2013 2013 2013 ...
##  $ values : num  9.8 15.2 16.2 5.1 18.5 17.9 5.8 9.8 14.4 17.8 ...

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)

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 can be replaced also after downloadind with human-readable labels using a function label_eurostat(). It replaces the eurostat codes based on definitions from Eurostat dictionaries.

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

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

label_eurostat_vars(names(datl))

## [1] "Unit of measure"                                                                     
## [2] "Vehicles"                                                                            
## [3] "Geopolitical entity (reporting)"                                                     
## [4] "Period of time (a=annual, q=quarterly, m=monthly, d=daily, c=cumulated from January)"

Vehicle information has 3 levels. They are:

levels(datl$vehicle)

## [1] "Motor coaches, buses and trolley buses"
## [2] "Passenger cars"                        
## [3] "Trains"

Selecting and modifying data

EFTA, Eurozone, EU and EU candidate countries

To facilititate fast plotting 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 (candidate_countries). This helps 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)

EU data from 2012 in all vehicles:

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

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)

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)

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)

For further examples, see also the blog post on the eurostat R package.

Citing the package

Citing the Data Kindly cite Eurostat.

Citing the R tools This work can be freely used, modified and distributed under the BSD-2-clause (modified FreeBSD) license:

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},
##     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 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 15.10
## 
## 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_3.6-1   ggplot2_2.0.0   tidyr_0.4.1     rvest_0.3.1    
## [5] xml2_0.1.2      eurostat_1.2.21 knitr_1.12.3   
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.3      magrittr_1.5     munsell_0.4.3    colorspace_1.2-6
##  [5] R6_2.1.2         plyr_1.8.3       stringr_1.0.0    httr_1.1.0      
##  [9] highr_0.5.1      dplyr_0.4.3      tools_3.2.2      parallel_3.2.2  
## [13] grid_3.2.2       gtable_0.1.2     DBI_0.3.1        digest_0.6.9    
## [17] assertthat_0.1   formatR_1.2.1    curl_0.9.5       evaluate_0.8    
## [21] labeling_0.3     stringi_1.0-1    scales_0.3.0     jsonlite_0.9.19