Currently, hundreds of R packages are related to spatial data analysis. They range from ecology and earth observation, hydrology and soil science, to transportation and demography. These packages support various stages of analysis, including data preparation, visualization, modeling, or communicating the results.

Introduction The coronavirus pandemic is a global phenomenon that will affect the lives of the majority of the world’s population for years to come.

This post explains how to quickly get key R packages for geographic research installed on Ubuntu, a popular Linux distribution. A recent thread on the r-spatial GitHub organization alludes to many considerations when choosing a Linux set-up for work with geographic data, ranging from the choice of Linux distribution (distro) to the use of binary vs or compiled versions (binaries are faster to install). This post touches on some of these things.

Inset maps enable multiple places to be shown in the same geographic data visualisation, as described in the Inset maps section (8.2.7) of our open source book Geocomputation with R. The topic of inset maps has gained attention and recently Enrico Spinielli asked inset maps could be created for data in unusual coordinate. R’s flexibility allows inset maps to be created in various ways, using different approaches and packages.

This vignette builds on the making maps chapter of the Geocomputation with R book. Its goal is to demonstrate all possible map styles available in the tmap package.

This vignette builds on the making maps chapter of the Geocomputation with R book. Its goal is to demonstrate how to set and modify grids and graticules in the tmap package.

Introduction This workbook outlines key concepts and functions related to map projections — also referred to as coordinate reference systems (CRSs) — and transformation of geographic data from one projection to another. It is based on the open source book Geocomputation with R , and Chapter 6 in particular.