sf (version 1.0-14)

st_join: spatial join, spatial filter

Description

spatial join, spatial filter

Usage

st_join(x, y, join, ...)
# S3 method for sf
st_join(
  x,
  y,
  join = st_intersects,
  ...,
  suffix = c(".x", ".y"),
  left = TRUE,
  largest = FALSE
)
st_filter(x, y, ...)
# S3 method for sf
st_filter(x, y, ..., .predicate = st_intersects)

Value

an object of class sf, joined based on geometry

Arguments

x

object of class sf

y

object of class sf

join

geometry predicate function with the same profile as st_intersects; see details

...

for st_join: arguments passed on to the join function or to st_intersection when largest is TRUE; for st_filter arguments passed on to the .predicate function, e.g. prepared, or a pattern for st_relate

suffix

length 2 character vector; see merge

left

logical; if TRUE return the left join, otherwise an inner join; see details. see also left_join

largest

logical; if TRUE, return x features augmented with the fields of y that have the largest overlap with each of the features of x; see https://github.com/r-spatial/sf/issues/578

.predicate

geometry predicate function with the same profile as st_intersects; see details

Details

alternative values for argument join are:

• st_contains_properly

• st_contains

• st_covered_by

• st_covers

• st_crosses

• st_disjoint

• st_equals_exact

• st_equals

• st_is_within_distance

• st_nearest_feature

• st_overlaps

• st_touches

• st_within

• any user-defined function of the same profile as the above

A left join returns all records of the x object with y fields for non-matched records filled with NA values; an inner join returns only records that spatially match.

To replicate the results of st_within(x, y) you will need to use st_join(x, y, join = "st_within", left = FALSE).

Examples

a = st_sf(a = 1:3,
 geom = st_sfc(st_point(c(1,1)), st_point(c(2,2)), st_point(c(3,3))))
b = st_sf(a = 11:14,
 geom = st_sfc(st_point(c(10,10)), st_point(c(2,2)), st_point(c(2,2)), st_point(c(3,3))))
st_join(a, b)
st_join(a, b, left = FALSE)
# two ways to aggregate y's attribute values outcome over x's geometries:
st_join(a, b) %>% aggregate(list(.$a.x), mean)
if (require(dplyr, quietly = TRUE)) {
 st_join(a, b) %>% group_by(a.x) %>% summarise(mean(a.y))
}
# example of largest = TRUE:
nc <- st_transform(st_read(system.file("shape/nc.shp", package="sf")), 2264)                
gr = st_sf(
    label = apply(expand.grid(1:10, LETTERS[10:1])[,2:1], 1, paste0, collapse = " "),
    geom = st_make_grid(st_as_sfc(st_bbox(nc))))
gr$col = sf.colors(10, categorical = TRUE, alpha = .3)
# cut, to check, NA's work out:
gr = gr[-(1:30),]
nc_j <- st_join(nc, gr, largest = TRUE)
# the two datasets:
opar = par(mfrow = c(2,1), mar = rep(0,4))
plot(st_geometry(nc_j))
plot(st_geometry(gr), add = TRUE, col = gr$col)
text(st_coordinates(st_centroid(gr)), labels = gr$label)
# the joined dataset:
plot(st_geometry(nc_j), border = 'black', col = nc_j$col)
text(st_coordinates(st_centroid(nc_j)), labels = nc_j$label, cex = .8)
plot(st_geometry(gr), border = 'green', add = TRUE)
par(opar)
# st_filter keeps the geometries in x where .predicate(x,y) returns any match in y for x
st_filter(a, b)
# for an anti-join, use the union of y
st_filter(a, st_union(b), .predicate = st_disjoint)