Spatial Statistics with R

Author

Affiliation

Published

March 15, 2024

Introduction

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")
install.packages("starsdata", repos = "http://cran.uni-muenster.de/pebesma/", 
                 type = "source")

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 15:00-18:00 CET (Berlin time)
- 3 blocks of 50 min + 10 mins break
- 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)
Vignettes of sf: tab “Articles”
Vignettes of stars: tab “Articles”
All these material are written using R-markdown (or quarto)

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
- removed from CRAN, and
- replaced by modern versions
R is a data science language that allows you to work reproducibly
Because we have CRAN and CRAN Taskviews: Spatial, SpatioTemporal, Tracking

Reproducing the current course

Go to https://github.com/edzer/sswr/
Go to “Code”, “copy URL to clipboard”
Clone this repo to your hard drive
Start one of the qmd files by double clicking, or on the command line with RStudio, or using some other tooling
Run the code sections!

# Introduction {.unnumbered} ## 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") install.packages("starsdata", repos = "http://cran.uni-muenster.de/pebesma/", type = "source") ``` ### 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 15:00-18:00 CET (Berlin time) * 3 blocks of 50 min + 10 mins break * 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) * 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 R-markdown (or [quarto](https://quarto.org/)) ## 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 * removed from CRAN, and * replaced by modern versions * 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 the current course * Go to [https://github.com/edzer/sswr/](https://github.com/edzer/sswr/) * Go to "Code", "copy URL to clipboard" * Clone this repo to your hard drive * Start one of the qmd files by double clicking, or on the command line with RStudio, or using some other tooling * Run the code sections!