Chapter 9: Mapping & Visualization 
Introduces tools for making static, animated, and interactive maps in R. Covers the tmap package and web-based options for sharing geographic data visually.
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From Brittany Barker: ‘My "GIS and Mapping in R" workshop for the Cascadia R Conference . . . is available at GitHub and includes four exercises that focus on using "sf", "terra", "ggplot2", and "leaflet" for geospatial analyses and creating static and interactive maps’
#RStats #RSpatial
(Barker is an asst research professor at Oregon State University in Portland)
https://github.com/bbarker505/CASCADIA_R_Intro_to_GIS_2025
GitHubGitHub - bbarker505/CASCADIA_R_Intro_to_GIS_2025: "GIS and Mapping in R" Workshop materials for 2025 Cascadia R Conf"GIS and Mapping in R" Workshop materials for 2025 Cascadia R Conf - bbarker505/CASCADIA_R_Intro_to_GIS_2025
Ooh CNN-based cloud masking baked right into a VRT file. This should be fun! #rstats #rspatial #gdal #python
This is using https://github.com/DPIRD-DMA/OmniCloudMask and appears to be a massive improvement on the standard Sentinel 2A SCL band!
Replied in thread
@MichaelTBacon @eliocamp here it seems #Rspatial is innocent, #GDAL error is helpful here.
I should start collecting all the ways in which #RSpatial can go wrong spectacularly.
Chapter 8: Geographic Data I/O 
Covers how to read and write spatial data in various formats, access open geoportals, and work with geographic web services in R. Includes tips on metadata and exporting maps.
I gave a talk on measuring spatial autocorrelation in spatial machine learning at the #AGILE0GI conference.
Slides: https://jakubnowosad.com/agile-gi2025/
Always glad to discuss spatial ML or autocorrelation --- feel free to reach out!
I just posted a new short blog article! 
It's about using layer functions with {terra} in R — a handy way to work with multi-layer rasters without making your code a mess. It shows how to apply functions to raster layers.
https://www.pmassicotte.com/posts/2025-06-12-terra-layer-funs/
Just another R blog (Philippe Massicotte)Using app(), lapp(), and tapp() with SpatRaster in R – Philippe MassicotteNumerical ecologist | Data visualization | R enthousiast
Spatial prediction meets machine learning with mlr3 
This blog post by Marvin Ludwig also includes tips on autocorrelation, extrapolation & more.
Chapter 7: Coordinate Reference Systems 
This chapter explains how to work with coordinate reference systems (CRSs) in R. Learn how to reproject vector and raster data, and understand how CRS choices affect spatial analysis.
Rainy day here, so I'm using #GenerativeAI to help teach me more about Kyle Walker's cool {mapgl} #RStats . And it's been pretty fun so far.
1) I uploaded mapgl help files and vignettes to both ChatGPT & Claude.
2) I used the CustomGPT AI Tutor Blueprint https://chatgpt.com/g/g-1rlItjngn-ai-tutor-blueprint to create a good prompt for a mapGL tutor.
3) I added that prompt to Claude, and my tutor was ready!
Prompt: https://gist.github.com/smach/4c08c4d93836f0b63c17a5f1b077dac9
The {pkgprompt} and {rdocdump} #R s help turn R docs into text.
Hey #RStats / #RSpatial friends, I'm experiencing this weird bug, I've reported it to sf but Edzer can't reproduce. Anyone else seeing the same? Pretty simple steps if you want to try it out and confirm.
https://github.com/r-spatial/sf/issues/2526
GitHubError in .subset2(x, i, exact = exact) after processing data with dplyr for the R session's first time, but not if same dplyr code is run a second time · Issue #2526 · r-spatial/sf
Exciting news! 
The 2nd edition of Geocomputation with R is now available as a physical book! 
Order your copy today and explore the latest in R for spatial analysis.
Learn more about the book's journey: https://buff.ly/3TZzc4L
Chapter 6: Raster-Vector Interactions 
Crop, mask, extract, and rasterize! 
Learn how to convert between raster & vector data and perform spatial extractions in R.
Quick trick: subsetting the BIOCLIM CHELSA dataset by the Swiss Federal Institute for Forest, Snow and Landscape Research WSL using Cloud Optimized Geotiff (COG) functionality in R.
This feature seems overlooked in the documentation but could save you orders of magnitude in time, bandwidth and disk space - depending on the use case.
https://bluegreenlabs.org/post/chelsa-cog-geotiff-subsetting/
BlueGreen Labs · CHELSA dynamic BIOCLIM subsets | BlueGreen LabsThe CHELSA dataset provides access to climatologies at high resolution for the earth’s land surface areas. Among those are the global climate-related predictors at kilometer resolution for the past and future (Brun et al. 2022) - generally known as bio-climatic variables. This dataset is provided through the main CHELSA website and links to a file download option which focuses on bulk file downloads.
However, it seems that the geotiffs provided are cloud optimized geotiffs (COG files).
New blog post: Spatial Machine Learning with tidymodels 
This post shows how to apply the tidymodels framework to spatial data workflows in R. Part 3 in a series about #sml.
For the win. When you discover that a dataset is (accidentally?) formatted as COG Geotiffs, and saves you hundreds of GB in downloads.
Chapter 5: Geometry Operations 
Simplify, buffer, transform! Learn to modify vector geometries, clip spatial data, apply affine transformations, and resample rasters for better alignment.
