# R in Space - Useful resources

## The mapping landscape has changed!

Over the last decade it has become increasingly easy to create and edit maps. As explained by Mark Zastrow in “Science on the Map”, the mapping landscape has dramatically changed over the last decade. Scientists can now readily draw valuable spatial representations of their work, even with large data sets and perform powerful analyses using specific packages of programming languages such as R and Python. In order to give you a taste of the diversity of tool to manipulate, analyze and visualize geographic information, we listed a few software available:

### Specific packages of various programming languages

#### Python packages

And if you want to see MORE, look at the Awesome GIS and the Awesome Geospatial 🔥!

### Free GIS data

If you are looking for free GIS data, you should definitively start by carefully examine the resources listed on this page: https://freegisdata.rtwilson.com/. Note that in R the function getData() from the raster package is fantastic and some R packages are great data provider, for instance, osmdata (see below).

## 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;

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. To give you something more tangible regarding the abundance of R packages, we propose below a curated list of R packages you will doubtlessly find very helpful:

For a more 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: