Geocomputation with R

Introduction

This repository hosts the code underlying Geocomputation with R, a book by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow:

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

This book has been published by CRC Press in the R Series. The online version of this book is free to read at https://geocompr.robinlovelace.net/.

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 project’s issue tracker).

Please 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): katygregg, erstearns, eyesofbambi, tyluRp, marcosci, giocomai, mdsumner, rsbivand, pat-s, gisma, ateucher, annakrystalli, gavinsimpson, Henrik-P, Himanshuteli, yutannihilation, jbixon13, katiejolly, layik, mvl22, nickbearman, ganes1410, richfitz, SymbolixAU, wdearden, yihui, chihinl.

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, this book is also a package. Installing it from GitHub will ensure all dependencies to build the book are available on your computer (you need devtools):

install.packages(devtools)
devtools::install_github("geocompr/geocompkg")

You need a recent version of the GDAL, GEOS, Proj.4 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

Running geocompr code in docker

To ease reproducibility we have set-up a docker image (robinlovelace/geocompr on docker hub) containing all the dependencies needed to reproduce the book. After you have installed docker and set-it up on your computer you should be able to reproduce the entire book with the following steps (resulting in output shown below):

# download repo with Windows Powershell or a Unix terminal
git clone https://github.com/Robinlovelace/geocompr.git 
# or download manually from here:
# https://github.com/Robinlovelace/geocompr/archive/master.zip
cd geocompr # navigate into the repo
# on linux and mac:
docker run -d -p 8788:8787 -v $(pwd):/home/rstudio/data -e USERID=$UID -e PASSWORD=pickASafePassWord --name rstudio robinlovelace/geocompr

# on windows:
docker run -d -p 8787:8787 -v ${pwd}:/home/rstudio/data -e PASSWORD=safepassword robinlovelace/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.

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

From this point to build the book you can open projects in the data 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).

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:

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 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")

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")

Book statistics

An indication of the book’s progress over time is illustrated below (to be updated roughly every week as the book progresses).

Book statistics: estimated number of pages per chapter over time.

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

knitr::kable(pkg_df)

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

kongdd/geocompr