Spatial Data Analysis in R

Optional extension for FRST 232 — Modern Data Analytics for Forestry and Natural Resources

Optional: Spatial Data Analysis in R

Spatial data analysis is an important skill in forestry and natural-resources work, but it is treated as optional extension material for FRST 232 — it is no longer part of the required course sequence. The required pathway focuses on Excel, R, tidyverse workflows, data cleaning, joins, visualization, an integrated case study, and communication.

This short resource keeps the two spatial chapters available for students who want extra help with spatial data analysis in R:

1. Spatial Data in R with sf — points, lines, and polygons; coordinate reference systems; reading spatial files; reprojecting; distances and buffers.
2. Map Forestry Data with leaflet — static thematic maps with geom_sf(), and interactive web maps with leaflet.

Data

Both chapters use the CSRD parks GeoJSON (Columbia Shuswap Regional District Open Data); the optional air-quality examples reuse the BC air-quality extract (airdata3.csv). Both files ship with this resource — download links are in each chapter.

How this connects to the main book

These chapters were originally part of Modern Data Analytics for Forestry and Natural Resources and still reference earlier chapters of that book (for example, the cleaned airdata_clean air-quality tibble from its Chapter 6). You can follow them on their own — the sf/leaflet code is self-contained and reads the bundled data — but where a snippet assumes an object from the main course, treat it as illustrative and rebuild it from the data provided here.