• Spatial Data and Analysis in R
  • 1 Introduction
    • 1.1 Workshop agenda
    • 1.2 Workshop Packages and Data
    • 1.3 Downloading content via Github
    • 1.4 Using RMarkdown and RStudio
    • 1.5 Overview
    • 1.6 R Basics Review
    • 1.7 Spatial Data in R
      • 1.7.1 Exercise
      • 1.7.2 Solution
      • 1.7.3 R Spatial Package Landscape
      • 1.7.4 Exercise
      • 1.7.5 Solution
      • 1.7.6 Primary R spatial packages
    • 1.8 Quick examples
      • 1.8.1 Geocoding example with tmaptools using open street map
      • 1.8.2 Exercise
      • 1.8.3 Solution
      • 1.8.4 Choropleth map
      • 1.8.5 Interactive mapping
  • 2 Reading in Spatial Data
    • 2.1 Reading in vector data
      • 2.1.1 Shapefiles
      • 2.1.2 Geodatabases
      • 2.1.3 Geopackages
      • 2.1.4 Open spatial data sources
      • 2.1.5 Spatial data from R packages
      • 2.1.6 Read in OpenStreetMap data
    • 2.2 Raster data
      • 2.2.1 raster package
      • 2.2.2 terra package
      • 2.2.3 stars package
    • 2.3 Convert flat files to spatial
  • 3 Coordinate reference systems
    • 3.1 Lesson Objectives
    • 3.2 What makes up a CRS?
    • 3.3 The ellipsoid and geoid
    • 3.4 Why you need to know about CRS working with spatial data in R:
    • 3.5 Changes to CRS recently in R in sf
    • 3.6 Projected coordinate systems
    • 3.7 Exercise
    • 3.8 Solution
  • 4 Vector data with sf
    • 4.1 sf Objects
    • 4.2 sf Methods
    • 4.3 Exploring sf
    • 4.4 Converting other object types to sf
    • 4.5 Units in sf
  • 5 Raster data
    • 5.1 Raster data with terra
      • 5.1.1 Exercise
      • 5.1.2 Solution
      • 5.1.3 Manipulating raster objects
      • 5.1.4 Reading existing rasters on disk
      • 5.1.5 Multiband rasters
      • 5.1.6 Plotting rasters and vectors with tmap
    • 5.2 Raster data with stars
      • 5.2.1 Read a satellite image
    • 5.3 Raster Operations
      • 5.3.1 Projecting
      • 5.3.2 Cropping
      • 5.3.3 Map Algebra
  • 6 Geoprocessing
    • 6.1 Lesson Goals
    • 6.2 Spatial Subsetting
    • 6.3 Spatial Join
    • 6.4 Dissolve
    • 6.5 Spatial Overlap
      • 6.5.1 Next we’ll create a dummy spatial polygon file to compare area with using the rmapshaper package to simplify the border of the PDX metropolitan polygon
      • 6.5.2 Then we can overlay polygons in ggplot to see how similar they are, showing the original census PDX metropolitan area in green, and new simplified polygon in red
      • 6.5.3 Now that we have an original and simplified polygon to compare, the process we want to use is:
      • 6.5.4 Exercise
      • 6.5.5 Solution
      • 6.5.6 Exercise
    • 6.6 Deriving data for a sites or a watershed
      • 6.6.1 Extract using sites
      • 6.6.2 River reaches and basin
      • 6.6.3 StreamCat data for watershed
    • 6.7 Extract
  • 7 Visualizing Spatial Data
    • 7.1 Lesson Goals
    • 7.2 tmap
      • 7.2.1 Exercise
      • 7.2.2 Solution
    • 7.3 leaflet
    • 7.4 mapview
      • 7.4.1 Exercise
      • 7.4.2 Solution
    • 7.5 Adding Web Map services in mapview
    • 7.6 ggplot2
      • 7.6.1 Exercise
      • 7.6.2 Solution
  • 8 Extensions to work with GIS software in R
    • 8.1 RQGIS
    • 8.2 R-ArcGIS bridge
    • 8.3 Accessing Python toolbox using reticulate
      • 8.3.1 Access Python objects directly from R
      • 8.3.2 And share spatial results from Python
    • 8.4 R Whitebox Tools
    • 8.5 rgee
  • 9 References
    • 9.1 R Spatial Resources
    • 9.2 R Vector Processing / Simple Features Resources
    • 9.3 R Raster Resources
    • 9.4 R Mapping Resources
    • 9.5 General R Resources
  • Published with bookdown

Spatial Data and Analysis in R

Chapter 9 References

9.1 R Spatial Resources

  • R Spatial - Spatial Data Science with R
  • Geocomputation with R
  • R Spatial Task View
  • Modern Geospatial Data Analysis with R by Zev Ross
  • SIGR2021 Summer School
  • Spatial Data Science - Pebesma and Bivand
  • Spatial Data Science Course- Prof. Adam Wilson
  • Introduction to Mapping and Spatial Analysis with R
  • R Spatial Workshop for EPA R User Group
  • Intro to GIS and Spatial Analysis by Manuel Gimond
  • FOSS4G2019 R for Geospatial Processing
  • An Introduction to Spatial Analysis and Mapping in R
  • Earth Analytics Spatial Data in R

9.2 R Vector Processing / Simple Features Resources

  • Simple Features for R
  • Spatial Data in R: New Directions
  • sp-sf Migration
  • An Exploration of Simple Features for R
  • Simple Features: Building Spatial Data Pipelines in R
  • Tidy spatial data in R: using dplyr, tidyr, and ggplot2 with sf

9.3 R Raster Resources

  • terra
  • Spatial Data Science with R and terra
  • stars - spatiotemporal arrays
  • Wageningen University Intro to Raster
  • Wageningen University Advanced Raster Analysis
  • The Visual Raster Cheat Sheet GitHub Repo
  • Rastervis

9.4 R Mapping Resources

  • mapview
  • Leaflet for R
  • tmap
  • Zev Ross Creating beautiful demographic maps in R with the tidycensus and tmap packages
  • Geocomputation with R: Making maps with R
  • Nico Hahn: Making Maps with R R

9.5 General R Resources

  • Google R Style Guide
  • Advanced R by Hadley Wickham