Spatial Joins and Operations
Apply st_join(), st_intersection(), st_buffer(), and st_distance().
Spatial Joins and Operations is a free R Academy lesson on CoddyKit. This is lesson 3 of 4. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 4 lessons in total.
Spatial Joins with st_join
st_join(x, y) performs a spatial left join: for each feature in x, it finds features in y that satisfy a spatial predicate (default: st_intersects) and appends their attributes. Multiple matches produce multiple rows.
library(sf)
nc <- st_transform(
read_sf(system.file('shape/nc.shp', package = 'sf')), 4326
)
# Create random points within NC bounding box
set.seed(42)
bbox <- st_bbox(nc)
pts <- data.frame(
lon = runif(20, bbox['xmin'], bbox['xmax']),
lat = runif(20, bbox['ymin'], bbox['ymax'])
)
pts_sf <- st_as_sf(pts, coords = c('lon', 'lat'), crs = 4326)
# Spatial join: assign county attributes to each point
pts_with_county <- st_join(pts_sf, nc['NAME'])
cat('Points with county:\n')
print(head(pts_with_county[, c('NAME', 'geometry')], 5))st_within: Point-in-Polygon
st_within(x, y) returns a sparse matrix of logical values indicating whether each feature in x falls completely within a feature in y. It is stricter than st_intersects — boundary touches do not count.
library(sf)
nc <- st_transform(
read_sf(system.file('shape/nc.shp', package = 'sf')), 4326
)
# Random points
set.seed(7)
bbox <- st_bbox(nc)
pts_sf <- st_as_sf(
data.frame(
lon = runif(30, bbox['xmin'], bbox['xmax']),
lat = runif(30, bbox['ymin'], bbox['ymax'])
),
coords = c('lon', 'lat'), crs = 4326
)
# Which points are within NC at all?
within_nc <- st_within(pts_sf, st_union(nc), sparse = FALSE)[, 1]
cat('Points within NC:', sum(within_nc), '/ 30\n')
# Keep only points inside NC
pts_inside <- pts_sf[within_nc, ]
cat('Kept:', nrow(pts_inside), 'points\n')st_intersects: Overlap Test
st_intersects(x, y) returns TRUE when features share any geometry — boundary or interior. It is the most permissive topological predicate. Pass sparse = FALSE to get a logical matrix rather than a list of index vectors.
library(sf)
# Create two overlapping polygons
poly1 <- st_polygon(list(matrix(c(0,0, 2,0, 2,2, 0,2, 0,0), ncol=2, byrow=TRUE)))
poly2 <- st_polygon(list(matrix(c(1,1, 3,1, 3,3, 1,3, 1,1), ncol=2, byrow=TRUE)))
poly3 <- st_polygon(list(matrix(c(5,5, 6,5, 6,6, 5,6, 5,5), ncol=2, byrow=TRUE)))
sfc <- st_sfc(poly1, poly2, poly3, crs = 32119)
# Intersects matrix
mat <- st_intersects(sfc, sparse = FALSE)
cat('Intersects matrix:\n')
print(mat)
cat('\npoly1 intersects poly2:', mat[1, 2], '\n')
cat('poly1 intersects poly3:', mat[1, 3], '\n')st_buffer: Creating Buffers
st_buffer(sf_obj, dist) creates a buffer polygon around each feature at the specified distance. In a geographic CRS the distance is in degrees; in a projected CRS it is in metres — always project before buffering for accurate results.
library(sf)
# City points in WGS84
cities <- data.frame(
city = c('Raleigh', 'Charlotte', 'Greensboro'),
lon = c(-78.639, -80.843, -79.792),
lat = c(35.779, 35.227, 36.073)
)
cities_sf <- st_as_sf(cities, coords = c('lon', 'lat'), crs = 4326)
# Project to metres for accurate buffering (NC State Plane)
cities_proj <- st_transform(cities_sf, 32119)
# 25 km buffer around each city
buffers <- st_buffer(cities_proj, dist = 25000)
cat('Buffer geometry type:', unique(as.character(st_geometry_type(buffers))), '\n')
cat('Buffer area (km2):', round(as.numeric(st_area(buffers[1, ])) / 1e6, 2), '\n')
cat('Expected (pi*25^2):', round(pi * 25^2, 2), 'km2\n')st_area: Polygon Areas
st_area(sf_obj) computes the area of each polygon. In a geographic CRS it returns values in steradians (not useful); in a projected CRS with metre units it returns square metres. Results carry units from the units package.
library(sf)
library(units)
nc <- read_sf(system.file('shape/nc.shp', package = 'sf'))
nc_proj <- st_transform(nc, 32119) # NC State Plane, metres
# Area in m2 with units
nc_proj$area_m2 <- st_area(nc_proj)
# Convert to km2
nc_proj$area_km2 <- set_units(nc_proj$area_m2, 'km^2')
# Summary statistics
cat('Smallest county:', nc_proj$NAME[which.min(nc_proj$area_km2)],
round(min(as.numeric(nc_proj$area_km2)), 1), 'km2\n')
cat('Largest county: ', nc_proj$NAME[which.max(nc_proj$area_km2)],
round(max(as.numeric(nc_proj$area_km2)), 1), 'km2\n')st_distance: Pairwise Distances
st_distance(x, y) computes a matrix of distances between all features in x and all in y. On WGS84 it uses geodesic distances; on projected CRS it uses Euclidean distances. Results carry units metadata.
library(sf)
cities <- data.frame(
city = c('Raleigh', 'Charlotte', 'Greensboro', 'Wilmington'),
lon = c(-78.639, -80.843, -79.792, -77.909),
lat = c(35.779, 35.227, 36.073, 34.226)
)
cities_sf <- st_as_sf(cities, coords = c('lon', 'lat'), crs = 4326)
# Geodesic distance matrix (metres)
dist_m <- st_distance(cities_sf)
dist_km <- round(dist_m / 1000)
rownames(dist_km) <- cities$city
colnames(dist_km) <- cities$city
cat('Distance matrix (km):\n')
print(dist_km)
# Nearest neighbour for Raleigh
raleigh_dist <- as.numeric(dist_km['Raleigh', -1])
cat('\nNearest to Raleigh:',
cities$city[-1][which.min(raleigh_dist)],
min(raleigh_dist), 'km\n')st_union: Dissolving Geometries
st_union(sf_obj) merges all features into a single geometry, dissolving interior boundaries. It is useful for creating a single outline polygon from multiple county polygons, or union-ing a set of buffers.
library(sf)
nc <- st_transform(
read_sf(system.file('shape/nc.shp', package = 'sf')), 32119
)
# Dissolve all counties into the NC state outline
nc_state <- st_union(nc)
cat('Counties before union:', nrow(nc), '\n')
cat('Features after union:', length(nc_state), '(single geometry)\n')
cat('Geometry type:', as.character(st_geometry_type(nc_state)), '\n')
# Area check: sum of counties == state area
county_area_sum <- sum(as.numeric(st_area(nc)))
state_area <- as.numeric(st_area(nc_state))
cat('Sum of county areas:', round(county_area_sum / 1e9, 2), 'km2\n')
cat('State area: ', round(state_area / 1e9, 2), 'km2\n')st_intersection: Clipping
st_intersection(x, y) returns the geometric intersection of each pair of features — the area that both geometries share. It is used for clipping one layer to the extent of another.
library(sf)
nc <- st_transform(
read_sf(system.file('shape/nc.shp', package = 'sf')), 4326
)
# Clip NC counties to a bounding rectangle (eastern NC)
clip_box <- st_as_sfc(st_bbox(c(
xmin = -78, xmax = -75,
ymin = 33.5, ymax = 36.5
), crs = 4326))
eastern_nc <- st_intersection(nc, clip_box)
cat('Original features:', nrow(nc), '\n')
cat('Clipped features: ', nrow(eastern_nc), '\n')
cat('Geometry types: ', paste(unique(as.character(st_geometry_type(eastern_nc))), collapse = ', '), '\n')Spatial Aggregation with dplyr
Combine st_join() with dplyr::group_by() and summarise() to aggregate point data by polygon. The geometry column is automatically summarised (unioned by default) when grouping an sf object.
library(sf)
library(dplyr)
nc <- st_transform(
read_sf(system.file('shape/nc.shp', package = 'sf')), 4326
)
# Generate random sales events in NC
set.seed(99)
bbox <- st_bbox(nc)
pts <- st_as_sf(
data.frame(
lon = runif(200, bbox['xmin'], bbox['xmax']),
lat = runif(200, bbox['ymin'], bbox['ymax']),
sales = runif(200, 100, 1000)
),
coords = c('lon', 'lat'), crs = 4326
)
# Join then aggregate
result <- st_join(pts, nc['NAME']) |>
filter(!is.na(NAME)) |>
st_drop_geometry() |>
group_by(NAME) |>
summarise(total_sales = sum(sales), n_events = n()) |>
arrange(desc(total_sales))
cat('Top 5 counties by sales:\n')
print(head(result, 5))st_nearest_feature
st_nearest_feature(x, y) returns the index of the nearest feature in y for each feature in x. Combined with st_distance(), this gives both the nearest neighbour and its distance — useful for assignment problems.
library(sf)
# Hospitals (facilities)
hospitals <- data.frame(
name = c('Hospital A', 'Hospital B', 'Hospital C'),
lon = c(-79.5, -80.2, -78.8),
lat = c(35.8, 35.3, 36.1)
)
hosp_sf <- st_as_sf(hospitals, coords = c('lon', 'lat'), crs = 4326)
# Patient locations
patients <- data.frame(
id = 1:5,
lon = c(-79.9, -80.5, -79.0, -78.5, -80.1),
lat = c(35.7, 35.2, 35.9, 35.5, 36.0)
)
pt_sf <- st_as_sf(patients, coords = c('lon', 'lat'), crs = 4326)
# Find nearest hospital for each patient
nearest_idx <- st_nearest_feature(pt_sf, hosp_sf)
pt_sf$nearest_hospital <- hospitals$name[nearest_idx]
print(st_drop_geometry(pt_sf))Practical Workflow: Buffer and Count
A common real-world pattern: buffer a set of features (e.g. schools), then count how many points of interest (e.g. bus stops) fall within each buffer using st_join() and dplyr::count().
library(sf)
library(dplyr)
# Schools as points
schools <- data.frame(
id = 1:3,
lon = c(-79.8, -80.2, -79.4),
lat = c(35.9, 35.5, 36.1)
)
schools_sf <- st_as_sf(schools, coords = c('lon', 'lat'), crs = 4326) |>
st_transform(32119) # project to metres
# Random bus stops
set.seed(5)
stops <- data.frame(
stop_id = 1:50,
lon = rnorm(50, -79.8, 0.3),
lat = rnorm(50, 35.8, 0.2)
) |>
st_as_sf(coords = c('lon', 'lat'), crs = 4326) |>
st_transform(32119)
# 2 km buffer around each school
buffers <- st_buffer(schools_sf, dist = 2000)
buffers$school_id <- schools$id
# Count bus stops within each school buffer
joined <- st_join(stops, buffers['school_id'])
counts <- count(st_drop_geometry(joined), school_id)
cat('Bus stops within 2km:\n')
print(counts)Quick Check
You want to find all polygon features from layer B that overlap with each polygon in layer A, and append B's attributes to A. Which function should you use?
Recap: Spatial Joins and Operations
Key takeaways:
st_join(x, y)— spatial left join using a predicate (default:st_intersects)st_within()— TRUE when x is fully inside y (boundary excluded)st_intersects()— TRUE when any part of x touches y (most permissive)st_buffer(sf_obj, dist)— create buffers; use projected CRS for metre-accurate buffersst_area()/st_distance()— compute areas and distances (project first for accuracy)st_union()— dissolve all features;st_intersection()— clip one layer to anotherst_nearest_feature(x, y)— nearest neighbour lookup
library(sf)
nc <- st_transform(
read_sf(system.file('shape/nc.shp', package = 'sf')), 32119
)
# Quick operations summary
cat('Largest county:', nc$NAME[which.max(st_area(nc))], '\n')
nc_outline <- st_union(nc)
cat('State area km2:', round(as.numeric(st_area(nc_outline)) / 1e6, 0), '\n')
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Frequently Asked Questions
Is the “Spatial Joins and Operations” lesson free?
Yes — any 3 lessons of the R Academy learning path, including “Spatial Joins and Operations”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 4 lessons in total.
What will I learn in “Spatial Joins and Operations”?
Apply st_join(), st_intersection(), st_buffer(), and st_distance(). You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 3 of 4.
How long does the “Spatial Joins and Operations” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
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Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Simple Features with the sf Package
- Coordinate Reference Systems and Projections
- Spatial Joins and Operations
- Interactive Maps with leaflet