R for Spatial Data Science

The second part of this book explains how the concepts introduced in the first part are dealt with using R. Chapter 7 deals with basic handling of spatial data: reading, writing, subsetting, selecting by spatial predicates, geometry transformers like buffers or intersections, raster-vector and vector-raster conversion, handling of data cubes, spherical geometry, coordinate transformations and conversions. This is followed by Chapter 8 which is dedicated to plotting of spatial and spatiotemporal data with base plot, and packages ggplot2, tmap and mapview. The chapter deals with projection, colours, colour breaks, graticules, graphic elements on maps like legends, and interactive maps. Chapter 9 discusses approaches to handle large vector or raster datasets or data cubes, where “large” either means too large to fit in memory or too large to download.

The material covered in this part is not meant as a complete tutorial nor a manual of the packages covered, but rather as an explanation and illustration of a number of common workflows. More complete and detailed information is found in the package documentation, in particular in the package vignettes for packages sf and stars. Links to them are found on the CRAN landing pages of the packages.