These functions allow to interpret spatial relations between nodes and
other geospatial features directly inside filter
and mutate calls. All functions return a logical
vector of the same length as the number of nodes in the network. Element i
in that vector is TRUE whenever any(predicate(x[i], y[j])) is
TRUE. Hence, in the case of using node_intersects, element i
in the returned vector is TRUE when node i intersects with any of
the features given in y.
node_intersects(y, ...)node_is_disjoint(y, ...)
node_touches(y, ...)
node_is_within(y, ...)
node_equals(y, ...)
node_is_covered_by(y, ...)
node_is_within_distance(y, ...)
A logical vector of the same length as the number of nodes in the network.
The geospatial features to test the nodes against, either as an
object of class sf or sfc.
Arguments passed on to the corresponding spatial predicate
function of sf. See geos_binary_pred.
See geos_binary_pred for details on each spatial
predicate. Just as with all query functions in tidygraph, these functions
are meant to be called inside tidygraph verbs such as
mutate or filter, where
the network that is currently being worked on is known and thus not needed
as an argument to the function. If you want to use an algorithm outside of
the tidygraph framework you can use with_graph to
set the context temporarily while the algorithm is being evaluated.
library(sf, quietly = TRUE)
library(tidygraph, quietly = TRUE)
# Create a network.
net = as_sfnetwork(roxel) %>%
st_transform(3035)
# Create a geometry to test against.
p1 = st_point(c(4151358, 3208045))
p2 = st_point(c(4151340, 3207520))
p3 = st_point(c(4151756, 3207506))
p4 = st_point(c(4151774, 3208031))
poly = st_multipoint(c(p1, p2, p3, p4)) %>%
st_cast('POLYGON') %>%
st_sfc(crs = 3035)
# Use predicate query function in a filter call.
within = net %>%
activate("nodes") %>%
filter(node_is_within(poly))
disjoint = net %>%
activate("nodes") %>%
filter(node_is_disjoint(poly))
oldpar = par(no.readonly = TRUE)
par(mar = c(1,1,1,1))
plot(net)
plot(within, col = "red", add = TRUE)
plot(disjoint, col = "blue", add = TRUE)
par(oldpar)
# Use predicate query function in a mutate call.
net %>%
activate("nodes") %>%
mutate(within = node_is_within(poly)) %>%
select(within)
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