Recent changes in R spatial and how to be ready for them

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Currently, hundreds of R packages are related to spatial data analysis. They range from ecology and earth observation, hydrology and soil science, to transportation and demography. These packages support various stages of analysis, including data preparation, visualization, modeling, or communicating the results. One common feature of most R spatial packages is that they are built upon some of the main representations of spatial data in R, available in key geographic R packages such as:

  • sf, which replaces sp
  • terra, which aims to replace raster
  • stars

Those packages are also not entirely independent. They are using external libraries, namely GEOS for spatial data operations, GDAL for reading and writing spatial data, and PROJ for conversions of spatial coordinates.

Therefore, R spatial packages are interwoven with each other and depend partially on external software developments. This has several positives, including the ability to use cutting-edge features and algorithms. On the other hand, it also makes R spatial packages vulnerable to changes in the upstream packages and libraries.

In the first part of the talk, we showcase several recent advances in R packages. It includes the largest recent change related to the developments in the PROJ library. We explain why the changes happened and how they impact R users. The second part focus on how to prepare for the changes, including computer set-up and running R spatial packages using Docker (briefly covered in a previous post and outlined in the new geocompr/docker repo). We outline important considerations when setting-up operating systems for geographic R packages. To reduce set-up times you can use geographic R packages Docker, a flexible and scalable technology containerization technology. Docker can run on modern computers and on your browser via services such as Binder, greatly reducing set-up times. Discussing these set-up options, and questions of compatibility between geographic R packages and paradigms such as the tidyverse and data.table, ensure that after the talk everyone can empower themselves with open source software for geographic data analysis in a powerful and flexible statistical programming environment.

You can find the slides for the talk at

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