In international comparison, using nationally aggregated indicators often have many disadvantages, which result from the very different levels of homogeneity, but also from the often very limited observation numbers in a cross-sectional analysis. When comparing European countries, a few missing cases can limit the cross-section of countries to around 20 cases which disallows the use of many analytical methods. Working with sub-national statistics has many advantages: the similarity of the aggregation level and high number of observations can allow more precise control of model parameters and errors, and the number of observations grows from 20 to 200-300.
The change from national to sub-national level comes with a huge data processing price: internal administrative boundaries, their names, codes codes change very frequently.
Yet the change from national to sub-national level comes with a huge data processing price. While national boundaries are relatively stable, with only a handful of changes in each recent decade. The change of national boundaries requires a more-or-less global consensus. But states are free to change their internal administrative boundaries, and they do it with large frequency. This means that the names, identification codes and boundary definitions of sub-national regions change very frequently. Joining data from different sources and different years can be very difficult.
Our regions R package helps the data processing, validation and imputation of sub-national, regional datasets and their coding.
There are numerous advantages of switching from a national level of the analysis to a sub-national level comes with a huge price in data processing, validation and imputation, and the regions package aims to help this process.
This package is an offspring of the eurostat package on rOpenGov. It started as a tool to validate and re-code regional Eurostat statistics, but it aims to be a general solution for all sub-national statistics. It will be developed parallel with other rOpenGov packages.
You can install the development version from GitHub with:
devtools::install_github("rOpenGov/regions")
or the released version from CRAN:
install.packages("regions")
You can review the complete package documentation on regions.dataobservaotry.eu. If you find any problems with the code, please raise an issue on Github. Pull requests are welcome if you agree with the Contributor Code of Conduct
If you use regions
in your work, please cite the package.