paezha / spatial-analysis-r

Open Educational Resource for teaching spatial data analysis and statistics with R

An Introduction to Spatial Data Analysis and Statistics: A Course in R

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Introduction

This repository hosts the code underlying the book An Introduction to Spatial Data Analysis and Statistics: A Course in R, by Antonio Paez:

Paez A (2021). An Introduction to Spatial Data Analysis and Statistics: A Course in R. McMaster Invisible Press. ISBN: 978-1-7778515-0-7

The book is free to read online at https://paezha.github.io/spatial-analysis-r.

Resources for Students and Instructors

Presentation slides

I have created a set of presentation slides in mentimeter for each substantive chapter in the book. I use these as mini-lectures in a flipped classroom format in my course, but they can be used as a template for creating longer presentations or lectures.

Chapter	Mentimeter Slides	Static pdf file
Chapter 3	Introduction to Mapping	pdf
Chapter 5	Mapping in R Continued	pdf
Chapter 7	Maps as Processes	pdf
Chapter 9	Point Pattern Analysis I	pdf
Chapter 11	Point Pattern Analysis II	pdf
Chapter 13	Point Pattern Analysis III	pdf
Chapter 15	Point Pattern Analysis IV	pdf
Chapter 17	Point Pattern Analysis V	pdf
Chapter 19	Area Data I	pdf
Chapter 21	Area Data II	pdf
Chapter 23	Area Data III	pdf
Chapter 25	Area Data IV	pdf
Chapter 27	Area Data V	pdf
Chapter 29	Area Data VI	pdf
Chapter 31	Spatially Continuous Data I	pdf
Chapter 33	Spatially Continuous Data II	pdf
Chapter 35	Spatially Continuous Data III	pdf
Chapter 37	Spatially Continuous Data IV	pdf

Template repository for projects

I created a template repository to help students get started with the use of GitHub and R Markdown for collaborative work on term projects. In my own course adoption of this workflow is completely optional. Not every student/group has the inclination/time to adopt this approach, but those who do produce very professional-looking reports and learn valuable skills.

Examples of projects

There will be some examples of projects in this repository.

Contributing

An advantage of an Open Educational Resource compared to traditional publishing (besides it being free!) is that it is a live, ongoing project, for as long as anyone cares for it. If you are using this resource, I would encourage you to contribute to improve it, by:

• suggesting improvements to the text, e.g. clarifying unclear sentences, fixing typos (see guidance from Yihui Xie);
• proposing changes to the code, e.g. to do things in a more efficient way; and
• making requests to develop content (see the project’s issue tracker).

Many thanks to all contributors to the book so far via GitHub (this list will update automatically): Robinlovelace.

Reproducing the book

TODO <!-- 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):

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

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

The book in binder

I think I got the binder to work.

The book in a Docker container

TODO <!-- 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

TODO

Citations

TODO

spatial-analysis-r's Issues

`rm(list = ls())` is not as good practice as it seems

I'm enjoying your book, thanks for it! However, everytime I read that you recommend doing rm(list = ls()), this comes to my mind: https://rstats.wtf/source-and-blank-slates#rm-list-ls

Cannot export the book to PDF nor EPUB

First of all, thank you for making this beautiful resource public!

Just reporting that at the moment the button to download to PDF or EPUB are not working (they both point to a non existing page).

PDF: https://paezha.github.io/spatial-analysis-r/spatial-analysis-R.pdf
EPUB: https://paezha.github.io/spatial-analysis-r/spatial-analysis-R.epub

`spdep` works with `sf` objects

In chapter 21, you explain that an sf object must be converted to sp before calculating poly2nb(). Since last versions (I don't know exactly which) it is not longer necessary. Indeed, sf objects are the recommended way to interact with the spdep package.

https://r-spatial.github.io/spdep/news/index.html#version-12-7-2022-10-01

minor typo section 3.6

if (!require("leaflet")) install.packages('magrittr')
should be
if (!require("magrittr")) install.packages('magrittr')
(i presume)

Link to cheatsheet does not work

update link on line 348, link no longer works.
"Check the ggplot2 Cheat Sheet "

Cheatsheets are available here: https://www.rstudio.com/resources/cheatsheets/