Geocomputation with R
Lovelace, Robin, Jakub Nowosad and Jannes Muenchow (2019). Geocomputation with R. The R Series. CRC Press.
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; and
- suggestions on content (see the project’s issue tracker).
See our-style.md for the book’s style.
Many thanks to all contributors to the book so far via GitHub (this list will update automatically): prosoitos, florisvdh, katygregg, rsbivand, KiranmayiV, erstearns, zmbc, eyesofbambi, nickbearman, tyluRp, giocomai, LaurieLBaker, mdsumner, pat-s, gisma, ateucher, annakrystalli, DarrellCarvalho, kant, gavinsimpson, Henrik-P, Himanshuteli, yutannihilation, jbixon13, yvkschaefer, katiejolly, KHwong12, layik, mtennekes, mvl22, ganes1410, richfitz, SymbolixAU, wdearden, yihui, chihinl, cshancock, 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
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 a computer with necessary system
dependencies and the
remotes package installed):
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 package’s 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:
You can also have a play with the repo in RStudio Cloud by clicking on this link (requires log-in):
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
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 book’s 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:
With these additional dependencies installed, you should be able to run the following scripts, which create content for the book, that we’ve removed from the main book build to reduce package dependencies and the book’s build time:
source("code/cranlogs.R") source("code/sf-revdep.R") source("code/08-urban-animation.R") source("code/08-map-pkgs.R")
.Rproj file is configured to build a website not a single
page. To reproduce this
use the following command:
rmarkdown::render("README.Rmd", output_format = "github_document", output_file = "README.md")
To cite packages used in this book we use code from Efficient R Programming:
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.
We use the following citation key format:
This can be set from inside Zotero desktop with the Better Bibtex plugin
by selecting the following menu options (with the shortcut
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.
# 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)
|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|
|ggmap||Spatial Visualization with ggplot2||2.6.1|
|ggplot2||Create Elegant Data Visualisations Using the Grammar of Graphics||184.108.40.20600|
|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|
|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|
|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|
|tmaptools||Thematic Map Tools||2.0.1|
|tree||Classification and Regression Trees||1.0.39|
|vegan||Community Ecology Package||2.5.2|