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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
<!-- IMPORTANT: DO NOT KNIT WITH KNIT BUTTON. INSTEAD USE THIS:
rmarkdown::render('README.Rmd', output_format = 'github_document', output_file = 'README.md')
-->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
#out.width = "100%",
fig.path = "images/"
)
is_online = curl::has_internet()
```
# An Introduction to Spatial Data Analysis and Statistics: A Course in R
<!-- badges: start -->
[](https://github.com/paezha/spatial-analysis-r/graphs/contributors)
[
[](https://mybinder.org/v2/gh/paezha/spatial-analysis-r/main?urlpath=rstudio)
[](https://zenodo.org/badge/latestdoi/391072865)
<!-- badges: end -->
## Introduction
This repository hosts the code underlying the book *An Introduction to Spatial Data Analysis and Statistics: A Course in R*, by [Antonio Paez](https://www.science.mcmaster.ca/ees/component/comprofiler/userprofile/paezha.html):
> 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](https://en.wikipedia.org/wiki/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](https://www.mentimeter.com/s/de73cd678c3af6b487f2f06eee44cad7/966186ab94da/edit) | pdf
Chapter 5 | [Mapping in `R` Continued](https://www.mentimeter.com/s/0fbd2c03bf23a9da5fbac902278644fe/ee9058215205/edit) | pdf
Chapter 7 | [Maps as Processes](https://www.mentimeter.com/s/59484637c743e96b6810c5e48b2cf405/d1152a9e811c/edit) | pdf
Chapter 9 | [Point Pattern Analysis I](https://www.mentimeter.com/s/2db531a235490f7d66a0077f2c4f0930/df1cd6ccccbb/edit) | pdf
Chapter 11 | [Point Pattern Analysis II](https://www.mentimeter.com/s/100f17f481ed1f28dd98112492533ba6/389b8ed12832/edit) | pdf
Chapter 13 | [Point Pattern Analysis III](https://www.mentimeter.com/s/8495ddf8551f0083017726a9f68cfa5b/4e5c29e8c78e/edit) | pdf
Chapter 15 | [Point Pattern Analysis IV](https://www.mentimeter.com/s/587258400bd04521b52585f27296a799/57f84401a6a6/edit) | pdf
Chapter 17 | [Point Pattern Analysis V](https://www.mentimeter.com/s/fe385c8dbd256f2645507290f579b15c/2056e80d53e4/edit) | pdf
Chapter 19 | [Area Data I](https://www.mentimeter.com/s/cab8fcae7e9b2cd1f447f79b5349dd23/d939ca4b33e9/edit) | pdf
Chapter 21 | [Area Data II](https://www.mentimeter.com/s/c8442809151f00d4ac7e4a300bdf707a/20932ed527b6/edit) | pdf
Chapter 23 | [Area Data III](https://www.mentimeter.com/s/175ee004df6fb9452837023e02b2960b/96666c58d803/edit) | pdf
Chapter 25 | [Area Data IV](https://www.mentimeter.com/s/516dae79f2558cd948da3de61b1c2b54/b7bb868b80d0/edit) | pdf
Chapter 27 | [Area Data V](https://www.mentimeter.com/s/32ee96ef3aaf83559b779f3fb88fc209/341e94b74b69/edit) | pdf
Chapter 29 | [Area Data VI](https://www.mentimeter.com/s/13eade97c82235b94c73d6dec6ef34a7/10a4634c3a9d/edit) | pdf
Chapter 31 | [Spatially Continuous Data I](https://www.mentimeter.com/s/57b95c57c9a4b75e91d91adb8b6e5faa/330c2ca0d567/edit) | pdf
Chapter 33 | [Spatially Continuous Data II](https://www.mentimeter.com/s/b40aa86207d96711a40c73397bd36d08/f8248b9e8ecd/edit) | pdf
Chapter 35 | [Spatially Continuous Data III](https://www.mentimeter.com/s/d651523809c79353767df82bc7ba4d2f/d8e42a7811ed/edit) | pdf
Chapter 37 | [Spatially Continuous Data IV](https://www.mentimeter.com/s/78b3ec0313be43abe9995fe4c3447dd2/07a125804a15/edit) | pdf
### Template repository for projects
I created a [template repository](https://github.com/paezha/ES4GA3-Sample-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](https://github.com/paezha/student-projects).
## 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](https://yihui.name/en/2013/06/fix-typo-in-documentation/));
- 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](https://github.com/paezha/spatial-analysis-r/issues)).
<!-- Need to check what the style is
See [our-style.md](https://github.com/Robinlovelace/geocompr/blob/master/our-style.md) for the book's style.
-->
```{r, include=FALSE}
contributors = source("code/list-contributors.R")[[1]]
# save for future reference:
readr::write_csv(contributors, "extdata/contributors.csv")
c_txt = contributors$name
c_url = contributors$link
c_rmd = paste0("[", c_txt, "](", c_url, ")")
contributors_text = paste0(c_rmd, collapse = ", ")
```
Many thanks to all contributors to the book so far via GitHub (this list will update automatically): `r contributors_text`.
<!-- Need to figure out what this is
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`](https://github.com/Robinlovelace/geocompr/blob/master/our-impact.csv).
-->
## 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](https://github.com/r-spatial/sf#installing) and the [**remotes**](https://github.com/r-lib/remotes/) package installed):
```{r, eval=FALSE, message=FALSE}
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](https://github.com/r-spatial/sf) for information on that.
Once the dependencies have been installed you should be able to build and view a local version the book with:
```{r, eval=FALSE}
bookdown::render_book("index.Rmd") # to build the book
browseURL("_book/index.html") # to view it
```
<!-- The code associated with each chapter is saved in the `code/chapters/` folder. -->
<!-- `source("code/chapters/07-transport.R")` runs run the code chunks in chapter 7, for example. -->
<!-- These R scripts are generated with the follow command which wraps `knitr::purl()`: -->
```{r gen-code, results='hide', echo=FALSE}
# geocompkg:::generate_chapter_code()
```
## The book in binder
I think I got the binder to work.
[](https://mybinder.org/v2/gh/paezha/spatial-analysis-r/main?urlpath=rstudio)
<!--
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](https://mybinder.org/v2/gh/robinlovelace/geocompr/master?urlpath=rstudio) 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):
-->
## The book in a Docker container
TODO
<!--
To ease reproducibility we have made Docker images available, at [geocompr/geocompr](https://hub.docker.com/r/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](https://www.docker.com/community-edition#/download) and set-it up on [your computer](https://docs.docker.com/install/linux/linux-postinstall/) you can start RStudio Server without a password (see the [Rocker project](https://www.rocker-project.org/use/managing_users/) for info on how to add a password and other security steps for public-facing servers):
```sh
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:
```sh
docker run -it geocompr/geocompr R
```
See the [geocompr/docker](https://github.com/geocompr/docker#geocomputation-with-r-in-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
<!--
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:
```{r extra-pkgs, message=FALSE, eval=FALSE}
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:
```{r source-readme, eval=FALSE}
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](https://github.com/Robinlovelace/geocompr/blob/master/README.Rmd) use the following command:
```{r render-book, eval=FALSE}
rmarkdown::render("README.Rmd", output_format = "github_document", output_file = "README.md")
```
```{r, eval=FALSE, echo=FALSE}
# We aim to make every script in the `code` folder reproducible.
# To check they can all be reproduced run the following:
# Aim: test reproducibility of scripts
script_names = list.files("code", full.names = T)
avoid = "pkgs|anim|us|saga|sliver|tsp|parti|polycent|cv|svm|data|location|eco|rf|cran|hex"
dontrun = grepl(avoid, script_names)
script_names = script_names[!dontrun]
counter = 0
for(i in script_names[45:length(script_names)]) {
counter = counter + 1
print(paste0("Script number ", counter, ": ", i))
source(i)
}
```
<!-- ## Book statistics -->
<!-- An indication of the book's progress over time is illustrated below (to be updated roughly every week as the book progresses). -->
<!--
```{r gen-stats, echo=FALSE, message=FALSE, warning=FALSE, eval=FALSE}
# source("code/generate-chapter-code.R")
book_stats = readr::read_csv("extdata/word-count-time.csv",
col_types=('iiDd'))
# to prevent excessive chapter count
if(Sys.Date() > max(book_stats$date) + 5) {
book_stats_new = geocompkg:::generate_book_stats()
book_stats = bind_rows(book_stats, book_stats_new)
readr::write_csv(book_stats, "extdata/word-count-time.csv")
}
book_stats = dplyr::filter(book_stats, chapter <= 15)
library(ggplot2)
book_stats$chapter = formatC(book_stats$chapter, width = 2, format = "d", flag = "0")
book_stats$chapter = fct_rev(as.factor(book_stats$chapter))
book_stats$n_pages = book_stats$n_words / 300
```
```{r bookstats, warning=FALSE, echo=FALSE, fig.width=8, fig.height=5, eval=FALSE}
ggplot(book_stats) +
geom_area(aes(date, n_pages, fill = chapter), position = "stack") +
ylab("Estimated number of pages") +
xlab("Date") +
scale_x_date(date_breaks = "2 month",
limits = c(min(book_stats$date), as.Date("2018-10-01")),
date_labels = "%b %Y") +
coord_cartesian(ylim = c(0, 350))
```
<!-- Book statistics: estimated number of pages per chapter over time. -->
## Citations
TODO
<!--
To cite packages used in this book we use code from [Efficient R Programming](https://csgillespie.github.io/efficientR/):
```{r gen-cite, warning=FALSE}
# geocompkg:::generate_citations()
```
This generates .bib and .csv files containing the packages.
The current of packages used can be read-in as follows:
```{r pkg_df, message=FALSE}
#pkg_df = readr::read_csv("extdata/package_list.csv")
```
Other citations are stored online using Zotero.
If you would like to add to the references, please use Zotero, join the [open group](https://www.zotero.org/groups/418217/energy-and-transport) add your citation to the open [geocompr library](https://www.zotero.org/groups/418217/energy-and-transport/items/collectionKey/9K6FRP6N).
We use the following citation key format:
```
[auth:lower]_[veryshorttitle:lower]_[year]
```
This can be set from inside Zotero desktop with the Better Bibtex plugin installed (see [github.com/retorquere/zotero-better-bibtex](https://github.com/retorquere/zotero-better-bibtex)) by selecting the following menu options (with the shortcut `Alt+E` followed by `N`), and as illustrated in the figure below:
```
Edit > Preferences > Better Bibtex
```
Zotero settings: these are useful if you want to add references.
We use Zotero because it is a powerful open source reference manager that integrates well with the **citr** package.
As described in the GitHub repo [Robinlovelace/rmarkdown-citr-demo](https://github.com/Robinlovelace/rmarkdown-citr-demo).
## References
```{r}
# remotes::install_github("gadenbuie/regexplain")
# regexplain::regexplain_file("extdata/package_list.csv")
#pattern = " \\[[^\\}]*\\]" # perl=TRUE
#pkg_df$Title = gsub(pattern = pattern, replacement = "", x = pkg_df$Title, perl = TRUE)
#knitr::kable(pkg_df)
```
-->