`R` Spatial Analysis Notes

Spatial Analysis in `R`

sp - defines the set of base classes for spatial data in R. Most useful for creating, converting, merging, transforming (e.g. projection), and plotting (see spplot) Spatial* objects.
rgdal - wrapper for GDAL. Most useful for reading in vector data (e.g. ESRI Shapefiles, GeoJSON, KML) with the function readOGR and reading raster data (e.g. NetCDF, GeoTiff) with the function readGDAL.
rgeos - wrapper for the GEOS library. Most useful for common spatial operations such as calculating distances between objects, areas, intersections, buffers, centroids, etc. See help(package = "rgeos") for a complete list of functions.
raster - provides a set of object classes and functions specific to working with raster data. Most useful for loading, transforming, and analyzing raster data. A particularly useful function getData retrieves commonly used spatial data such as administrative boundaries or world climate data.

"Applied Spatial Data Analysis with R" [Book Website][Springer Link] by Roger S. Bivand, Edzer Pebesma, and Virgilio Gómez-Rubio. Perhaps the best and most comprehensive introduction to Spatial* objects in R by the authors of the sp package.
CRAN Task View: Analysis of Spatial Data - lists available R packages by spatial application.
Stack Overflow Geographic Information Systems - Invaluable Q&A community and knowledge base. This is often the best place to look for solutions to a specific but common task.
Visual Raster Cheatsheet by Etienne B. Racine

Making Maps in R by Kevin Johnson
Mapping in R using the ggplot2 package by Zev Ross
Introduction to Spatial Data and ggplot2 by James Chesire
Using R — Working with Geospatial Data (and ggplot2) by Bethany Yollin
Making Maps with R by Kim Gilbert
Create Maps With R Geospatial Classes and Graphics Tools - UCSB National Center for Ecological Analysis and Synthesis
Spatial maps and geocoding in R by Aedin Culhane
choroplethr by Ari Lamstein (choropleth mapping)
- In addition to the GitHub documentation, Lamstein has many tutorial and examples in his blog

The very basics of R - UCLA Statistics
R Tutorial by Kelly Black
CRAN R Reference Card (excellent coverage of commonly used base R functions)
RStudio Cheatsheets (RStudio IDE, visualization, data cleaning, etc.) by RStudio
A section on R Basics from "R Graphics Cookbok" by Winston Chang

dplyr introduction (powerful tools for processing, cleaning, and analyzing tabular data) by Hadley Wickham
- dplyr databases vignette
stargazer cheatsheet (publication quality formatting for regression results) by Jake Russ
knitr resources (dynamic document compilation / reproducible research) by Yihui Xie.
packrat (dependency management / reproducibility tool) by RStudio.
Advanced R by Hadley Wickham
R Packages (package development) by Hadley Wickham