sf-terra

name	sf-terra
description	R spatial data: sf vectors, terra rasters, spdep/spatialreg spatial stats, leaflet interactive maps, ggplot2+geom_sf() choropleths. CRS, spatial joins, geometry ops. Use when execution language is R. Python equivalent: geopandas.
autoload	never
metadata	{"audience":"research-coders","domain":"r-library","library-version":"sf 1.1-0","skill-last-updated":"2026-07-07","tags":["r","geospatial","sf","terra","mapping"]}

sf-terra Skill

R geospatial analysis with sf (Simple Features for R), terra (modern raster operations), spdep/spatialreg (spatial statistics and spatial econometrics), leaflet (interactive web maps), and classInt (classification breaks for choropleth maps). Covers sf objects, CRS management, spatial joins, geometric operations, raster creation and manipulation, choropleth maps via ggplot2 + geom_sf(), interactive maps via leaflet, and spatial autocorrelation and regression. Use when the execution language is R and the task involves spatial data, map-making, or spatial statistics. Python equivalent: the geopandas skill.

What is sf?

sf (Simple Features) is the modern R package for vector geospatial data:

• sf objects: An sf object is a data.frame (or tibble) with a special geometry list-column containing sfc geometries -- tabular data meets spatial operations
• Tidyverse integration: sf objects work with dplyr verbs (filter(), mutate(), group_by(), summarize()), and the geometry column is preserved automatically
• ggplot2 integration: geom_sf() plots sf objects with automatic CRS-aware axis labels
• GDAL/GEOS/PROJ backend: Full access to industry-standard spatial libraries
• Successor to sp/rgdal/rgeos: Those packages are retired; sf is the standard

What is terra?

terra is the modern R package for raster data (replaces the retired raster package):

• SpatRaster objects: Multi-band, multi-layer raster data with CRS and extent metadata
• Raster arithmetic: Element-wise operations, aggregation, focal operations
• Raster-vector interaction: extract(), crop(), mask() using sf geometries
• Memory-efficient: Processes large rasters on disk without loading everything into memory

Version Notes

Versions installed in the DAAF container (R 4.5.3):

Package	Version	Role
sf	1.1-0	Vector spatial data (Simple Features)
terra	1.9-11	Raster spatial data
stars	0.7.2	Spatiotemporal arrays (raster time series)
spdep	1.4.2	Spatial weights and spatial autocorrelation
spatialreg	1.4.3	Spatial regression models (SAR, SEM, SDM)
leaflet	2.2.3	Interactive web maps
classInt	0.4.11	Classification intervals for choropleth breaks

sf 1.x changes to be aware of:

• s2 geometry engine enabled by default for geographic CRS (spherical geometry) -- this changes area/distance/intersection behavior from earlier versions
• Use sf_use_s2(FALSE) to temporarily revert to planar GEOS operations when s2 produces unexpected results (see gotchas.md)
• st_make_valid() is now built into sf (no longer needs lwgeom)

Also Available in This Environment

Four additional spatial packages are installed (versions verified) that map to capabilities the geopandas skill documents on the Python side:

Package	Version	Purpose	geopandas-skill equivalent
maptiles	0.11.0	Basemap tiles for static maps (get_tiles())	contextily
exactextractr	0.10.1	Fast, exact zonal statistics of rasters over polygons (exact_extract())	rasterstats zonal stats
osmdata	0.3.0	Download OpenStreetMap features via the Overpass API	osmnx / OSM download
tidygeocoder	1.0.6	Geocoding addresses to coordinates (geocode())	geopy geocoding

Honest asymmetry: point-pattern analysis (Ripley's K/G/F functions, spatial point process models) requires spatstat, which is not installed — for point-pattern methods, Python (PySAL pointpats via the geopandas skill) is the routed path. Areal interpolation (tobler-style) also has no installed R equivalent.

How to Use This Skill

Reference File Structure

File	Purpose	When to Read
quickstart.md	sf objects, st_read/st_write, basic plotting, CRS inspection	Starting with sf or quick reminder
crs.md	st_transform(), EPSG codes, CRS management, projection pitfalls	CRS errors or projection decisions
spatial-ops.md	st_join(), st_buffer(), st_intersection(), st_union(), geometric operations	Combining or transforming spatial data
raster.md	terra::rast(), raster arithmetic, extract(), crop/mask, raster-vector interaction	Working with raster data
mapping.md	ggplot2 + geom_sf(), choropleth with scale_fill, classInt for breaks, annotation	Making static maps
interactive.md	leaflet: addPolygons, addCircleMarkers, colorBin, popups, tile layers	Making interactive web maps
spatial-stats.md	spdep::poly2nb(), nb2listw(), moran.test(), spatialreg::lagsarlm()	Spatial autocorrelation and regression
gotchas.md	s2 geometry, CRS mismatch, geometry column persistence, large data performance	Debugging spatial issues

Reading Order

1. New to sf? Start with quickstart.md then spatial-ops.md
2. Making maps? Read mapping.md (relies on crs.md for projection choices)
3. Interactive maps? Read interactive.md
4. Raster data? Read raster.md
5. Spatial statistics? Read spatial-stats.md (for methodology context, also load data-scientist skill's geospatial-analysis.md)
6. Having issues? Check gotchas.md first

Related Skills

Skill	Relationship
geopandas	Python equivalent -- covers the same spatial domain for Python pipelines
data-scientist (geospatial-analysis.md, geospatial-operations.md)	Spatial methodology -- when/why to use spatial methods, interpretation guidance
ggplot2	Visualization -- geom_sf() is built on ggplot2; load ggplot2 skill for themes and styling
tidyverse	Data preparation -- sf objects work with dplyr verbs; tidyverse prepares data for spatial analysis
r-stats	Statistical modeling -- non-spatial regression and diagnostics
fixest	Fixed effects regression -- when combining spatial data with panel econometrics
r-python-translation	Cross-language spatial translation

Quick Decision Trees

"I need to read or write spatial data"

Loading/saving spatial data?
+-- Read vector file (Shapefile, GeoPackage, GeoJSON) -> ./references/quickstart.md
+-- Read GeoParquet -> ./references/quickstart.md
+-- Create sf from lat/lon columns -> ./references/quickstart.md
+-- Write to file -> ./references/quickstart.md
+-- Read raster data (GeoTIFF) -> ./references/raster.md
+-- Raster time series (NetCDF) -> ./references/raster.md

"I need to combine or transform spatial data"

Spatial operations?
+-- Join by location (point-in-polygon) -> ./references/spatial-ops.md
+-- Nearest-neighbor join -> ./references/spatial-ops.md
+-- Overlay (intersection, union, difference) -> ./references/spatial-ops.md
+-- Dissolve/aggregate polygons -> ./references/spatial-ops.md
+-- Buffer features -> ./references/spatial-ops.md
+-- Clip to boundary -> ./references/spatial-ops.md
+-- Compute distances -> ./references/spatial-ops.md
+-- Compute centroids or areas -> ./references/spatial-ops.md

"I need to fix CRS or projection issues"

CRS/projection issues?
+-- Check current CRS -> ./references/crs.md
+-- Reproject to different CRS -> ./references/crs.md
+-- Choose a projection for analysis -> ./references/crs.md
+-- Data has no CRS (set it) -> ./references/crs.md
+-- CRS mismatch error -> ./references/gotchas.md
+-- Area/distance calculations wrong -> ./references/crs.md

"I need to make a map"

Making maps?
+-- Quick static choropleth (ggplot2) -> ./references/mapping.md
+-- Classification schemes (quantiles, Jenks) -> ./references/mapping.md
+-- Layered map with multiple sf layers -> ./references/mapping.md
+-- Multi-panel maps -> ./references/mapping.md
+-- Interactive map (pan/zoom/hover) -> ./references/interactive.md
+-- Export to PNG/SVG/HTML -> ./references/mapping.md or ./references/interactive.md

"I need spatial statistics"

Spatial statistics?
+-- Build spatial weights (contiguity, distance) -> ./references/spatial-stats.md
+-- Test for spatial autocorrelation (Moran's I) -> ./references/spatial-stats.md
+-- Local clusters / hot spots (LISA) -> ./references/spatial-stats.md
+-- Spatial regression (lag, error, Durbin) -> ./references/spatial-stats.md
+-- Methodology guidance (interpretation, MAUP) -> data-scientist skill: geospatial-analysis.md

"I need to work with rasters"

Raster operations?
+-- Read GeoTIFF -> ./references/raster.md
+-- Raster arithmetic -> ./references/raster.md
+-- Extract raster values at points/polygons -> ./references/raster.md
+-- Crop/mask raster by polygon -> ./references/raster.md
+-- Raster-vector conversion -> ./references/raster.md
+-- Raster time series (stars) -> ./references/raster.md

"Something isn't working"

Having issues?
+-- s2 geometry errors -> ./references/gotchas.md
+-- CRS mismatch errors -> ./references/gotchas.md
+-- Geometry column lost after dplyr -> ./references/gotchas.md
+-- Invalid geometry errors -> ./references/gotchas.md
+-- Memory issues with large files -> ./references/gotchas.md
+-- General troubleshooting -> ./references/gotchas.md

File-First Execution in Research Workflows

In DAAF research pipelines, R spatial operations follow the file-first execution protocol -- code is written to .R script files and executed via the run_with_capture.sh wrapper, never run interactively.

The pattern:

1. Write spatial analysis code to scripts/stage{N}_{type}/{step}_{task-name}.R
2. Execute via Bash: bash {BASE_DIR}/scripts/run_with_capture.sh {PROJECT_DIR}/scripts/{script_name}.R
3. run_with_capture.sh detects the .R extension and uses Rscript automatically
4. stdout/stderr are appended to the script file as comments
5. If a script fails, create a versioned copy (_a.R, _b.R, etc.) for fixes

Read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the complete protocol.

R spatial script structure follows DAAF conventions:

# --- Config ---
library(sf)
library(dplyr)
library(ggplot2)

PROJECT_DIR <- "/daaf/research/YYYY-MM-DD_Project"
TARGET_CRS <- 5070  # NAD83 Conus Albers

# --- Load ---
# INTENT: Load school point data and county boundaries
schools <- st_read(file.path(PROJECT_DIR, "data", "schools.gpkg")) |>
  st_transform(TARGET_CRS)
counties <- st_read(file.path(PROJECT_DIR, "data", "counties.gpkg")) |>
  st_transform(TARGET_CRS)
cat("Schools:", nrow(schools), "rows\n")
cat("Counties:", nrow(counties), "rows\n")




joined  st_joinschools counties join  st_within


stopifnotnrowjoined  nrowschools
stopifnotjoinedcounty_name  
cat nrowjoined 


st_writejoined file.pathPROJECT_DIR  
         delete_dsn  
cat

Quick Reference

Essential Setup

library(sf)          # vector spatial data
library(terra)       # raster spatial data
library(dplyr)       # data manipulation (works with sf)
library(ggplot2)     # static maps via geom_sf()
library(spdep)       # spatial weights, Moran's I
library(spatialreg)  # spatial regression models
library(leaflet)     # interactive web maps
library(classInt)    # classification intervals

Core Operations

Operation	Code	Package
Read vector	st_read("data.gpkg")	sf
Read parquet (WKB geometry)	arrow::read_parquet() + st_as_sfc(structure(as.list(col), class = "WKB"), crs = ...) — sfarrow NOT installed; see quickstart.md	sf + arrow
From lat/lon	st_as_sf(df, coords = c("lon", "lat"), crs = 4326)	sf
Check CRS	st_crs(x)	sf
Reproject	st_transform(x, crs = 5070)	sf
Spatial join	st_join(points, polygons, join = st_within)	sf
Intersection	st_intersection(x, y)	sf
Buffer	st_buffer(x, dist = 1000) (CRS units)	sf
Centroid	st_centroid(x)	sf
Area	st_area(x) (returns units)	sf
Distance	st_distance(x, y)	sf
Union/dissolve	`x	> group_by(col)
Plot (static)	ggplot(x) + geom_sf(aes(fill = col))	ggplot2
Plot (interactive)	`leaflet(x)	> addTiles()
Write vector	st_write(x, "out.gpkg")	sf
Read raster	rast("elev.tif")	terra
Raster extract	extract(r, vect(sf_obj))	terra
Crop raster	crop(r, vect(sf_obj))	terra

Common CRS Codes

EPSG	Name	Use For
4326	WGS84	Storage, exchange, web (not analysis)
5070	NAD83 Conus Albers	US thematic maps (equal-area)
3857	Web Mercator	Web tiles display only
32617	UTM Zone 17N	US East Coast local analysis

Topic Index

Topic	Reference File
sf objects and structure	./references/quickstart.md
st_read / st_write	./references/quickstart.md
Creating sf from coordinates	./references/quickstart.md
Basic plotting with plot()	./references/quickstart.md
GeoParquet I/O	./references/quickstart.md
CRS fundamentals	./references/crs.md
st_transform / reprojection	./references/crs.md
Choosing projections	./references/crs.md
Common US projections	./references/crs.md
set_crs vs st_transform	./references/crs.md
Spatial joins (st_join)	./references/spatial-ops.md
Nearest-neighbor joins	./references/spatial-ops.md
Overlay (st_intersection, st_union)	./references/spatial-ops.md
Dissolve and aggregation	./references/spatial-ops.md
Buffering (st_buffer)	./references/spatial-ops.md
Clipping (st_crop, st_intersection)	./references/spatial-ops.md
Area, distance, centroid	./references/spatial-ops.md
terra::rast() basics	./references/raster.md
Raster arithmetic	./references/raster.md
extract(), crop(), mask()	./references/raster.md
stars spatiotemporal arrays	./references/raster.md
Raster-vector conversion	./references/raster.md
ggplot2 + geom_sf() maps

Citation

When sf is used as a primary analytical tool, include in the report's Software & Tools references:

Pebesma, E. (2018). "Simple Features for R: Standardized Support for Spatial Vector Data." The R Journal, 10(1), 439-446. https://doi.org/10.32614/RJ-2018-009

Cite when: sf is used for spatial operations, spatial joins, or map production central to the analysis. Do not cite when: Only used to read a shapefile for a simple reference lookup.

If terra raster operations are also used, additionally cite:

Hijmans, R.J. (2024). terra: Spatial Data Analysis. R package. https://rspatial.org/

If spdep/spatialreg spatial analysis is also used, additionally cite:

Bivand, R.S., Pebesma, E., and Gomez-Rubio, V. (2013). Applied Spatial Data Analysis with R (2nd ed.). Springer. https://doi.org/10.1007/978-1-4614-7618-4