The R Journal: accepted article

This article will be copy edited and may be changed before publication.

Retrieval and Analysis of Eurostat Open Data with the eurostat Package
Leo Lahti, Janne Huovari, Markus Kainu and Przemysław Biecek

Abstract The increasing availability of open statistical data resources is providing novel opportunities for research and citizen science. Efficient algorithmic tools are needed to realize the full potential of the new information resources. We introduce the eurostat R package that provides a collection of custom tools for the Eurostat open data service, including functions to query, download, manipulate, and visualize these data sets in a smooth, automated and reproducible manner. The online documentation provides detailed examples on the analysis of these spatio-temporal data collections. This work provides substantial improvements over the previously available tools, and has been extensively tested by an active user community. The eurostat R package contributes to the growing open source ecosystem dedicated to reproducible research in computational social science and digital humanities.

Received: 2016-09-15; online 2017-05-10
CRAN packages: eurostat, eurostat, eurostat, FAOSTAT, WDI, pxweb, osmar, eurostat, smarterpoland, eurostat, eurostat, rsdmx, datamart, quandl, pdfetch, rsdmx, eurostat, classInt, httr, jsonlite, readr, sp, stringi, eurostat, eurostat, tibble, eurostat, plotrix, grid, maptools, rgdal, rgeos, scales, stringr, eurostat, eurostat, countrycode, eurostat , CRAN Task Views implied by cited CRAN packages: Spatial, WebTechnologies, Graphics, NaturalLanguageProcessing, SpatioTemporal, TimeSeries


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-019,
  author = {Leo Lahti and Janne Huovari and Markus Kainu and Przemysław
          Biecek},
  title = {{Retrieval and Analysis of Eurostat Open Data with the
          eurostat Package}},
  year = {2017},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2017/RJ-2017-019/index.html}
}