Geospatial Data & Visualization

Reading

Our geospatial module is meant only as an introduction to geospatial data, an already-rich area in R that is in mid-Renaissance right now, just as tabular data was in the past few years since the introduction of dplyr and the rest of tidyverse.

GeoSpatial Textbook: Geocomputation with R (Lovelace, Nowosad, Muenchow)

Basic Visualization tools

Also read over the following vignettes:

Watch

UPDATED

  • DataCamp now has a more modern course from Zev Ross, Spatial Analysis in R with sf and raster that more closely aligns with the tools we will be using in this module (matching the Geocomputation reading above).

  • Charlotte Wickham’s DataCamp course, Working with Geospatial Data doesn’t cover the new/emerging spatial suite we will focus on, but still very relevant. Chapter 3, introducing the raster side of things, is (for the moment) the same one we will use. However, the sf package replaces the vector manipulation and mapping functions of sp, though many concepts carry over. Least relevant are the older plotting strategies covered there, such as ggmap, where we will rely on newer tmap, ggplot::geom_sf, and mapview packages.

References

  • I highly recommend browsing the excellent vignettes of the sf package. Vignettes are provided with most well-developed R packages and often provide the best and most up-to-date introduction to the package.

  • Follow the r-spatial blog & website for the latest news from the r-spatial community. (Speaking of which, you can also follow the Tidyverse website/blog for updates from there.) Most of the package developers involved in these projects are also active in the #rstats corner of Twitter.