Geocomputation with R is a book on geographic data analysis, visualization, and modeling. The First Edition was first published as a physical book in 2019 and we have reached 500k people through via the website at https://geocompr.robinlovelace.net/ and the physical book since then.

TL;DR: Please help us by filling out the survey at https://forms.gle/nq9RmbxJyZXQgc948. It’s been almost 3 years since the first edition of Geocomputation with R was published back in 2019. It’s been an amazing journey for this open source book on geographic data analysis, visualization and modeling since then. We have reached almost 500k people via the website at https://geocompr.robinlovelace.net/ and the physical book.

The R programming language has, over the past two decades, evolved substantial spatial data analysis capabilities, and is now one of the most powerful environments for undertaking geographic research using a reproducible command line interface. Currently, dedicated R packages allow to read spatial data and apply a plethora of different kinds of spatial methods in a reproducible fashion.

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.