st_join | R Documentation |
spatial join, spatial filter
st_join(x, y, join, ...)
## S3 method for class 'sf'
st_join(
x,
y,
join = st_intersects,
...,
suffix = c(".x", ".y"),
left = TRUE,
largest = FALSE
)
st_filter(x, y, ...)
## S3 method for class 'sf'
st_filter(x, y, ..., .predicate = st_intersects)
x |
object of class |
y |
object of class |
join |
geometry predicate function with the same profile as st_intersects; see details |
... |
for |
suffix |
length 2 character vector; see merge |
left |
logical; if |
largest |
logical; if |
.predicate |
geometry predicate function with the same profile as st_intersects; see 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,
st_relate (which will require pattern
to be set),
or 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)
.
an object of class sf
, joined based on geometry
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)
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