2.2 So… why use R for mapping?

Given the number of tools dedicated to visualization and analyses of spatial data, it is important that users ask this question and take some time to balance pros and cons of using R for mapping. According to us, the choice strongly relies on:

  1. your ambition in terms of mapping;
  2. your skills in R.

If you aim at creating a good-looking map without analysis and you are not familiar with R, it does not make sense to use R only for mapping. But if you are familiar with R or plan on becoming familiar with it to perform and replicate spatial analyses in R, you can quickly get a good-looking map (a R plot basically) and then benefit from the plot system you already know. Also, when you need tricky spatial analysis, even if you are not familiar with R, you will doubtlessly may benefit from learning it.

Using R to create your maps and perform spatial analyses also means that you will write your data pipeline in a specific language and thus create scripts. Such scripts are easy to share and key element to make your analyses transparent and reproducible. Last but not least, the vast and active R community, which explains the incredible richness of packages, the abundance of documentation and tutorials available on line as well as the massive stack of answered questions on question and answer sites such as StackOverflow.

For a detailed list of packages, have a look at the CRAN task view “Spatial”. Note that there is a quick way to install all packages listed in the task view through the package:

install.packages("ctv")
ctv::install.views("Spatial")

Also, according to us, a good set of tutorials/documentation to start with spatial data in R is: