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"))
Spatial Statistics with R
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:
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!