Spatial Statistics with R

Author

Affiliation

Edzer Pebesma

Universität Münster

Published

November 15, 2024

Introduction

slack channel

Required packages

The following packages may be used during the course; it is assumed that you know how to install packages, and have permission to do so on your computer.

CRAN packages:

install.packages(c("classInt",
"colorspace",
"dplyr",
"ggplot2",
"gstat",
"hglm",
"igraph",
"lme4",
"lwgeom",
"maps" ,
"mapview",
"randomForest",
"rnaturalearth",
"s2",
"scales",
"sf",
"sp",
"spacetime",
"spdep",
"spatialreg",
"spatstat",
"spData",
"stars",
"terra",
"tidyverse",
"tmap",
"units",
"viridis",
"viridisLite",
"xts"))

non-CRAN packages:

install.packages("spDataLarge", repos = "https://nowosad.github.io/drat/", 
                 type = "source") # 23 Mb
options(timeout = 3600) # 1 hr instead of 1 min
install.packages("starsdata", repos = "http://cran.uni-muenster.de/pebesma/", 
                 type = "source") # 1 Gb

Introduction to the course

introduction of the tutor
introduction of course participants, please state
- name,
- where you’re from,
- what kind of spatial data analysis you have done so far

How we work

Live sessions are from 14:00-18:00 CET (Berlin time); daily schedule:

14:00 - 14:45 lecture
14:45 - 15:30 practical exercises (break-out groups)
15:30 - 15:45 discussion of exercises
15:45 - 16:15 break
16:15 - 17:00 lecture
17:00 - 17:45 practical exercises (break-out groups)
17:45 - 18:00 discussion of exercises

Further:

please raise hands or speak up whenever something comes up
slack communication during the full week
please share questions you run into in your actual research, preferably with (example) data and R code
please use the open channels in slack, so that everyone can learn from q + a’s

Resources

Spatial Data Science: With applications in R, by Pebesma and Bivand 2023 (open online, hard copy from CRC)
Vignettes of sf: tab “Articles”
Vignettes of stars: tab “Articles”
All these material are written using quarto or R-markdown

Why R for spatial statistics?

R is old! Think of the advantages!
R is as good as any data science language, but is more in focus with the statistical community
Most researchers in spatial statistics who share code have used or use R
R has a strong ecosystem of users and developers, who communicate and collaborate (and compete, mostly in a good way)
R spatial packages have gone full cycle:
- the first generation has been deprecated (rgdal, rgeos, maptools),
- then removed from CRAN, and
- superseded by modern versions (sf and stars replaced sp, terra replaced raster)
R is a data science language that allows you to work reproducibly
Because we have CRAN and CRAN Taskviews: Spatial, SpatioTemporal, Tracking

Reproducing or recreating the current course

Go to https://github.com/edzer/sswr/
Go to “Code”, then “copy URL to clipboard”
Clone this repo to your hard drive
Start RStudio by double clickign the sswr.Rproj file in the source directory
Reproduce these course materials by installing quarto and
- in RStudio: run build - render book, or
- on the command line: run quarto render in the course directory
Run individual code sections in RStudio, and modify them!

Exercises

Install the spDataLarge package (see instructions above)
Copy the course material from GitHub to your local machine
Open it in RStudio
Open the day1.qmd file. Try to identify a code chunk.
Run the first code chunk.
Skip to the last code chunk; run all code chunks above it (by a single click), and then run this last code chunk.
Render the entire course “book”, view the result by opening _book/index.html in a web browser (from Rstudio)

# Introduction {.unnumbered} [slack channel](https://spatialstatis-7gb9460.slack.com) ## Required packages The following packages may be used during the course; it is assumed that you know how to install packages, and have permission to do so on your computer. ### CRAN packages: ```{r eval=FALSE} install.packages(c("classInt", "colorspace", "dplyr", "ggplot2", "gstat", "hglm", "igraph", "lme4", "lwgeom", "maps" , "mapview", "randomForest", "rnaturalearth", "s2", "scales", "sf", "sp", "spacetime", "spdep", "spatialreg", "spatstat", "spData", "stars", "terra", "tidyverse", "tmap", "units", "viridis", "viridisLite", "xts")) ``` ### non-CRAN packages: ```{r eval=FALSE} install.packages("spDataLarge", repos = "https://nowosad.github.io/drat/", type = "source") # 23 Mb options(timeout = 3600) # 1 hr instead of 1 min install.packages("starsdata", repos = "http://cran.uni-muenster.de/pebesma/", type = "source") # 1 Gb ``` ### Introduction to the course * introduction of the tutor * introduction of course participants, please state * name, * where you're from, * what kind of spatial data analysis you have done so far ### How we work Live sessions are from 14:00-18:00 CET (Berlin time); daily schedule: * 14:00 - 14:45 lecture * 14:45 - 15:30 practical exercises (break-out groups) * 15:30 - 15:45 discussion of exercises * 15:45 - 16:15 break * 16:15 - 17:00 lecture * 17:00 - 17:45 practical exercises (break-out groups) * 17:45 - 18:00 discussion of exercises Further: * please raise hands or speak up whenever something comes up * slack communication during the full week * please share questions you run into in your actual research, preferably with (example) data and R code * please use the open channels in slack, so that everyone can learn from q + a's ### Resources * [Spatial Data Science: With applications in R](https://r-spatial.org/book/), by Pebesma and Bivand 2023 (open online, hard copy from [CRC](https://www.routledge.com/Spatial-Data-Science-With-Applications-in-R/Pebesma-Bivand/p/book/9781138311183)) * Vignettes of [sf](https://r-spatial.github.io/sf/): tab "Articles" * Vignettes of [stars](https://r-spatial.github.io/stars/): tab "Articles" * All these material are written using [quarto](https://quarto.org/) or R-markdown ## Why R for spatial statistics? * R is old! Think of the advantages! * R is as good as any data science language, but is more in focus with the statistical community * Most researchers in spatial statistics who share code have used or use R * R has a strong ecosystem of users and developers, who communicate and collaborate (and compete, mostly in a good way) * R spatial packages have gone full cycle: * the first generation has been deprecated (`rgdal`, `rgeos`, `maptools`), * then removed from CRAN, and * superseded by modern versions (`sf` and `stars` replaced `sp`, `terra` replaced `raster`) * R is a data science language that allows you [to work reproducibly](https://eartharxiv.org/repository/view/3216/) * Because we have [CRAN](https://cran.r-project.org/) and CRAN Taskviews: [Spatial](https://cran.r-project.org/web/views/Spatial.html), [SpatioTemporal](https://cran.r-project.org/web/views/SpatioTemporal.html), [Tracking](https://cran.r-project.org/web/views/Tracking.html) ### Reproducing or recreating the current course * Go to [https://github.com/edzer/sswr/](https://github.com/edzer/sswr/) * Go to "Code", then "copy URL to clipboard" * Clone this repo to your hard drive * Start RStudio by double clickign the `sswr.Rproj` file in the source directory * Reproduce these course materials by [installing quarto](https://quarto.org/docs/get-started/) and * in RStudio: run `build` - `render book`, or * on the command line: run `quarto render` in the course directory * Run individual code sections in RStudio, and modify them! ## Exercises 1. Install the `spDataLarge` package (see instructions above) 2. Copy the course material from GitHub to your local machine 3. Open it in RStudio 4. Open the `day1.qmd` file. Try to identify a code chunk. 5. Run the first code chunk. 6. Skip to the last code chunk; run all code chunks above it (by a single click), and then run this last code chunk. 6. Render the entire course "book", view the result by opening `_book/index.html` in a web browser (from Rstudio)