Getting spatial in R

The following pages will introduce some basic concepts of using the R software for GIS operations. Nothing over the top but just a simple introduction to basic concepts like converting data to spatial objects, doing some spatial transformations, and data import and export of GIS data. R continues to enhance its GIS functionality, so it is likley the number of subpages will grow to try and illustrate these developments with digital soil science examples.

Notes on using R for GIS stuff

R is richly served with functionality to work with, analyse, manipulate and map spatial data. Many procedures one would carry out in a GIS software, can more-or-less be performed (and automated) relatively easy in R. The application of spatial data analysis in R is well documented in Bivand, Pebesma, and Gomez-Rubio (2008). There you will find a deep analysis with numerous examples of basic concepts right through to quite advanced analyses. Similarly Geocomputation with R (with proper citation: (Lovelace, Nowosad, and Muenchow 2019)) is a great resource and probably demonstrates more of the recent developments and applications for doing GIS work in R.

Naturally, in DSM, we constantly work with spatial data in one form or another e.g., points, polygons, rasters. We need to to such things as import, view, and export points to, in, and from a GIS. Similarly for polygons and rasters.

Many of the functions used for working with spatial data do not come with the base function suite installed with the R software. Thus we need to use specific functions from a range of different contributed R packages. Probably the most important and most frequently used are:

  • sp contains many functions for handling vector (polygon) data.
  • raster very rich source of functions for handling raster data.
  • rgdal function for projections and spatial data I/O.

These 3 packages are more than able to do a very large suite of tasks. Relatively newer packages like sf and stars are increasingly popular packages for geospatial analyses of vector and raster data respectively. Yet at the time of writing the author is less familiar with these packages and what relative advantages they have over their sp and raster counterparts. Currently most GIS functionality needed for DSM can ably be done with with those packages mentioned above, but note that applications involving more specialized GIS functionality, handling and manipulating spatial-temporal data, and working with data cubes, then perhaps the newer packages are going to provide the required solutions. I will keep an eye on these packages and determine whether a given DSM task can be better crafted with these packages when the opportunity arises.

With regard to either sp, raster, rgdal (or any R package for that matter), always consult the help files and online documentation regarding these packages, and you will quickly realize that there is deeper set of functionality that comes with the package that is not covered in these pages.

References

Bivand, R S, E J Pebesma, and V Gomez-Rubio. 2008. Applied Spatial Data Analysis with R. UseR! Series, Springer.

Lovelace, R, J Nowosad, and J Muenchow. 2019. Geocomputation with R. Chapman & Hall/CRC The R Series.