dlab-berkeley/R-Geospatial-Fundamentals-Legacy

This is the repository for D-Lab's Geospatial Fundamentals in R with sf workshop.

R Geospatial Fundamentals

This is the repository for D-Lab's R Geospatial Fundamentals workshop.

Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. This workshop focuses on fundamental operations for reading, writing, manipulating and mapping vector data, which encodes location as points, lines and polygons.

--

Content outline

• Part I: Core concepts, vector data, and plotting
  • Basic geospatial concepts
  • Basic vector data
  • Geospatial data structures (the sf package)
  • Basic plotting (base::plot and the ggplot3 package)
  • Managing coordinate reference systems (CRS)
  • Advanced plotting (the tmap package)
  • Map overlays
• Part II: Spatial analysis
  • Spatial measurement queries
  • Spatial relationship queries
  • Buffer analysis
  • Spatial and non-spatial joins
  • Aggregation
  • Continued mapping practice
• Part III: Raster data
  • Raster concepts
  • Raster data structures (the raster package)
  • Mapping with raster and vector data
  • Spatial analysis of raster and vector data
  • Raster reclassification
  • Raster stacks and raster algebra

Getting started

Please follow the notes in participant-instructions.md.

Assumed participant background

We assume that participants have working familiarity with the R language, including the topics covered in our R-Fundamentals workshop materials (though participants without this have still reported useful learning about geospatial concepts).

If you would like a refresher on R, you could review that workshop's materials, or look to other online resources such as the Base R Cheat Sheet or the Quick R website.

Technology requirements

Please bring a laptop with the following:

• R version 3.5 or greater
• RStudio integrated development environment (IDE) is highly recommended but not required.
• The R packages ggplot2, sf, here, units, tmap, mapview, tidyverse and raster installed, minimally

Resources

• The Geocomputation with R textbook (Lovelace, Nowosad, and Muenchow, 2019) is an excellent resource for getting up and running.
• The R sf package webpage, especially the Articles tab of tutorials for getting started.
• The tmap getting started documentation is a great source of plotting support.
• The sf vignettes and sf cheatsheet are great resources for developing and debugging sf code.
• The raster vignettes should help you troubleshoot that package.