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Geocomputation with R

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Introduction

This repository hosts the code underlying Geocomputation with R, a book by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow. If you find the contents useful, please cite it as follows:

Lovelace, Robin, Jakub Nowosad and Jannes Muenchow (2019). Geocomputation with R. The R Series. CRC Press.

The first version of the book has been published by CRC Press in the R Series and can be viewed online at bookdown.org. Read the latest version at geocompr.robinlovelace.net.

Note: we are actively working on the Second Edition

Since commencing work on the Second Edition in September 2021 much has changed, including:

  • Replacement of raster with terra in Chapters 1 to 7 (see commits related to this update here)
  • Update of Chapter 7 to include mention alternative ways or reading-in OSM data in #656
  • Refactor build settings so the book builds on Docker images in the geocompr/docker repo
  • Improve the experience of using the book in Binder (ideal for trying out the code before installing or updating the necessary R packages), as documented in issue #691 (thanks to yuvipanda)
  • Improved communication of binary spatial predicates in Chapter 4 (see #675)
  • New section on the links between subsetting and clipping (see #698) in Chapter 5
  • New section on the dimensionally extended 9 intersection model (DE-9IM)
  • New chapter on raster-vector interactions split out from Chapter 5
  • New section on the sfheaders package
  • New section in Chapter 2 on spherical geometry engines and the s2 package
  • Replacement of code based on the old mlr package with code based on the new mlr3 package, as described in a huge pull request and popular tweet

See https://github.com/Robinlovelace/geocompr/compare/1.9main for a continuously updated summary of the changes to date. At the time of writing (April 2022) there have been more than 10k lines of code/prose added, lots of refactoring!

Contributions at this stage are very welcome.

Contributing

We encourage contributions on any part of the book, including:

  • improvements to the text, e.g.clarifying unclear sentences, fixing typos (see guidance from Yihui Xie);
  • changes to the code, e.g.to do things in a more efficient way;
  • suggestions on content (see the projects issue tracker);
  • improvements to and alternative approaches in the Geocompr solutions booklet hosted at geocompr.github.io/solutions (see a blog post on how to update solutions in files such as _01-ex.Rmd here)

See our-style.md for the books style.

Many thanks to all contributors to the book so far via GitHub (this list will update automatically): prosoitos, florisvdh, katygregg, rsbivand, iod-ine, KiranmayiV, defuneste, zmbc, erstearns, FlorentBedecarratsNM, dcooley, marcosci, MikeJohnPage, eyesofbambi, nickbearman, tyluRp, giocomai, KHwong12, LaurieLBaker, MarHer90, mdsumner, pat-s, darrellcarvalho, e-clin, gisma, ateucher, annakrystalli, kant, gavinsimpson, Himanshuteli, yutannihilation, jimr1603, jbixon13, olyerickson, yvkschaefer, katiejolly, kwhkim, layik, mpaulacaldas, mtennekes, mvl22, ganes1410, richfitz, wdearden, yihui, chihinl, cshancock, ec-nebi, gregor-d, jasongrahn, p-kono, pokyah, schuetzingit, sdesabbata, tim-salabim, tszberkowitz.

During the project we aim to contribute upstream to the packages that make geocomputation with R possible. This impact is recorded in our-impact.csv.

Reproducing the book

To ease reproducibility, we created the geocompkg package. Installing it from GitHub will install all the R packages needed build the book (you will need a computer with necessary system dependencies and the remotes package installed):

install.packages("remotes")
# To reproduce the first Part (chapters 1 to 8):
remotes::install_github("geocompr/geocompkg")

# During development work on the 2nd edition you may also need dev versions of
# other packages to build the book, e.g.:
remotes::install_github("mtennekes/tmap")

Running the commands above should install the packages needed to run the first part of the book (chapters 1 to 8). To install and build the book in its entirety, run the following command (which installs additional Suggests packages, this may take some time to run!):

# To reproduce all chapters (install lots of packages, may take some time!)
remotes::install_github("geocompr/geocompkg", dependencies = TRUE)

You need a recent version of the GDAL, GEOS, PROJ and udunits libraries installed for this to work on Mac and Linux. See the sf packages README for information on that.

Once the dependencies have been installed you should be able to build and view a local version the book with:

bookdown::render_book("index.Rmd") # to build the book
browseURL("_book/index.html") # to view it

Geocompr in binder

For many people the quickest way to get started with Geocomputation with R is in your web browser via Binder. To see an interactive RStudio Server instance click on the following button, which will open mybinder.org with an R installation that has all the dependencies needed to reproduce the book:

Launch Rstudio Binder

You can also have a play with the repo in RStudio Cloud by clicking on this link (requires log-in):

Launch Rstudio Cloud

Geocomputation with R in a Docker container

To ease reproducibility we have made Docker images available, at geocompr/geocompr on DockerHub. These images allow you to explore Geocomputation with R in a virtual machine that has up-to-date dependencies.

After you have installed docker and set-it up on your computer you can start RStudio Server without a password (see the Rocker project for info on how to add a password and other security steps for public-facing servers):

docker run -p 8787:8787 -e DISABLE_AUTH=TRUE geocompr/geocompr

If it worked you should be able to open-up RStudio server by opening a browser and navigating to http://localhost:8787/ resulting in an up-to-date version of R and RStudio running in a container.

Start a plain R session running:

docker run -it geocompr/geocompr R

If you see something like this after following the steps above, congratulations: it worked! See github.com/rocker-org for more info.

If you want to call QGIS from R, you can use the qgis tag, by running the following command for example (which also shows how to set a password and use a different port on localhost):

docker run -d -p 8799:8787 -e USERID=$UID -e PASSWORD=strongpass -v $(pwd):/home/rstudio/geocompr robinlovelace/geocompr:qgis

From this point to build the book you can open projects in the geocompr directory from the project box in the top-right hand corner, and knit index.Rmd with the little knit button above the the RStudio script panel (Ctl+Shift+B should do the same job).

See the geocompr/docker repo for details, including how to share volumes between your computer and the Docker image, for using geographic R packages on your own data and for information on available tags.

Reproducing this README

To reduce the books dependencies, scripts to be run infrequently to generate input for the book are run on creation of this README.

The additional packages required for this can be installed as follows:

source("code/extra-pkgs.R")

With these additional dependencies installed, you should be able to run the following scripts, which create content for the book, that weve removed from the main book build to reduce package dependencies and the books build time:

source("code/01-cranlogs.R")
source("code/sf-revdep.R")
source("code/09-urban-animation.R")
source("code/09-map-pkgs.R")

Note: the .Rproj file is configured to build a website not a single page. To reproduce this README use the following command:

rmarkdown::render("README.Rmd", output_format = "github_document", output_file = "README.md")

Citations

To cite packages used in this book we use code from Efficient R Programming:

# geocompkg:::generate_citations()

This generates .bib and .csv files containing the packages. The current of packages used can be read-in as follows:

pkg_df = readr::read_csv("extdata/package_list.csv")

Other citations are stored online using Zotero.

If you would like to add to the references, please use Zotero, join the open group add your citation to the open geocompr library.

We use the following citation key format:

[auth:lower]_[veryshorttitle:lower]_[year]

This can be set from inside Zotero desktop with the Better Bibtex plugin installed (see github.com/retorquere/zotero-better-bibtex) by selecting the following menu options (with the shortcut Alt+E followed by N), and as illustrated in the figure below:

Edit > Preferences > Better Bibtex

Zotero settings: these are useful if you want to add references.

We use Zotero because it is a powerful open source reference manager that integrates well with the citr package. As described in the GitHub repo Robinlovelace/rmarkdown-citr-demo.

References

# remotes::install_github("gadenbuie/regexplain")
# regexplain::regexplain_file("extdata/package_list.csv")
pattern = " \\[[^\\}]*\\]" # perl=TRUE
pkg_df$Title = gsub(pattern = pattern, replacement = "", x = pkg_df$Title, perl = TRUE)
knitr::kable(pkg_df)
Name Title version
bookdown Authoring Books and Technical Documents with R Markdown 0.7
cartogram Create Cartograms with R 0.1.0
dismo Species Distribution Modeling 1.1.4
geosphere Spherical Trigonometry 1.5.7
ggmap Spatial Visualization with ggplot2 2.6.1
ggplot2 Create Elegant Data Visualisations Using the Grammar of Graphics 3.0.0.9000
gstat Spatial and Spatio-Temporal Geostatistical Modelling, Prediction 1.1.6
historydata Datasets for Historians 0.2.9001
htmlwidgets HTML Widgets for R 1.2
kableExtra Construct Complex Table with kable and Pipe Syntax 0.9.0
kernlab Kernel-Based Machine Learning Lab 0.9.26
knitr A General-Purpose Package for Dynamic Report Generation in R 1.20
latticeExtra Extra Graphical Utilities Based on Lattice 0.6.28
leaflet Create Interactive Web Maps with the JavaScript Leaflet 2.0.1
link2GI Linking Geographic Information Systems, Remote Sensing and Other 0.3.0
lwgeom Bindings to Selected liblwgeom Functions for Simple Features 0.1.4
mapview Interactive Viewing of Spatial Data in R 2.4.0
microbenchmark Accurate Timing Functions 1.4.4
mlr Machine Learning in R 2.12.1
osmdata Import OpenStreetMap Data as Simple Features or Spatial 0.0.7
pROC Display and Analyze ROC Curves 1.12.1
ranger A Fast Implementation of Random Forests 0.10.1
raster Geographic Data Analysis and Modeling 2.6.7
rcartocolor CARTOColors Palettes 0.0.22
rgdal Bindings for the Geospatial Data Abstraction Library 1.3.3
rgeos Interface to Geometry Engine - Open Source (GEOS) 0.3.28
rgrass7 Interface Between GRASS 7 Geographical Information System and R 0.1.10
rmapshaper Client for mapshaper for Geospatial Operations 0.4.0
rmarkdown Dynamic Documents for R 1.10
rnaturalearth World Map Data from Natural Earth 0.2.0
rnaturalearthdata World Vector Map Data from Natural Earth Used in rnaturalearth 0.1.0
RPostgreSQL R Interface to the PostgreSQL Database System 0.6.2
RQGIS Integrating R with QGIS 1.0.3
RSAGA SAGA Geoprocessing and Terrain Analysis 1.1.0
sf Simple Features for R 0.6.3
sp Classes and Methods for Spatial Data 1.3.1
spData Datasets for Spatial Analysis 0.2.9.0
spDataLarge Large datasets for spatial analysis 0.2.7.0
stplanr Sustainable Transport Planning 0.2.4.9000
tabularaster Tidy Tools for Raster Data 0.5.0
tidyverse Easily Install and Load the Tidyverse 1.2.1
tmap Thematic Maps 2.0.1
tmaptools Thematic Map Tools 2.0.1
tree Classification and Regression Trees 1.0.39
vegan Community Ecology Package 2.5.2
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