Using Spatial Data with R

Claudia A Engel

Last updated: February 11, 2019

Prerequisites and Preparations

To get the most out of this workshop you should have:

• a basic knowledge of R and/or be familiar with the topics covered in the Introduction to R.
• have a recent version of R and RStudio installed.

Recommended:

• Create a new RStudio project R-spatial in a new folder R-spatial.

• Create a new folder under R-spatial and call it data.

• If you have your working directory set to R-spatial which contains a folder called data you can copy, paste, and run the following lines in R:

download.file("http://bit.ly/R-spatial-data", "R-spatial-data.zip")
unzip("R-spatial-data.zip", exdir = "data")

You can also download the data manually here R-spatial-data.zip and extract them.

• Open up a new R Script file R-spatial.R for the code you’ll create during the workshop.

• Install and load the following libraries:
  • sf
  • sp
  • rgdal
  • raster
  • rgeos
  • dplyr
• For the mapping section install and load these additional libraries:
  • classInt
  • RColorBrewer
  • ggplot2
  • ggmap
  • tmap
  • leaflet(On Mac installing binary version is ok)

References

Bivand, RS., Pebesma, E., Gómez-Rubio, V. (2013): Applied Spatial Data Analysis with R

Brunsdon, C. and Comber, L. (2015): An Introduction to R for Spatial Analysis and Mapping

Lovelace, R., Nowosad, J., Muenchow. J. (2019): Geocomputation with R

Spatial Data Analysis and Modeling with R

CRAN Task View: Analysis of Spatial Data

Engel, C. (2019). R for Geospatial Analysis and Mapping. The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2019 Edition), John P. Wilson (Ed.). DOI:10.22224/gistbok/2019.1.3.

For a quick introduction to all things geo check out map school.

Acknowledgements

Some of the materials for this tutorial are adapted from http://datacarpentry.org