These functions allow to interpret spatial relations between edges and
other geospatial features directly inside filter
and mutate calls. All functions return a logical
vector of the same length as the number of edges in the network. Element i
in that vector is TRUE whenever the chosen spatial predicate applies
to the spatial relation between the i-th edge and any of the features in
y.
Usage
edge_intersects(y, ...)
edge_is_disjoint(y, ...)
edge_touches(y, ...)
edge_crosses(y, ...)
edge_is_within(y, ...)
edge_contains(y, ...)
edge_contains_properly(y, ...)
edge_overlaps(y, ...)
edge_equals(y, ...)
edge_covers(y, ...)
edge_is_covered_by(y, ...)
edge_is_within_distance(y, ...)
edge_is_nearest(y)Arguments
- y
The geospatial features to test the edges against, either as an object of class
sforsfc.- ...
Arguments passed on to the corresponding spatial predicate function of sf. See
geos_binary_pred. The argumentsparseshould not be set.
Details
See geos_binary_pred for details on each spatial
predicate. The function edge_is_nearest instead wraps around
st_nearest_feature and returns TRUE for element i
if the i-th edge is the nearest edge to any of the features in y.
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.
Note
Note that edge_is_within_distance is a wrapper around the
st_is_within_distance predicate from sf. Hence, it is based on
'as-the-crow-flies' distance, and not on distances over the network.
Examples
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.
intersects = net |>
activate(edges) |>
filter(edge_intersects(poly))
oldpar = par(no.readonly = TRUE)
par(mar = c(1,1,1,1))
plot(st_geometry(net, "edges"))
plot(st_geometry(intersects, "edges"), col = "red", lwd = 2, add = TRUE)
par(oldpar)
# Use predicate query function in a mutate call.
net |>
activate(edges) |>
mutate(disjoint = edge_is_disjoint(poly)) |>
select(disjoint)
#> # A sfnetwork: 987 nodes and 1215 edges
#> #
#> # A directed multigraph with 9 components and spatially explicit edges
#> #
#> # Dimension: XY
#> # Bounding box: xmin: 4150707 ymin: 3206375 xmax: 4152366 ymax: 3208564
#> # Projected CRS: ETRS89-extended / LAEA Europe
#> #
#> # Edge data: 1,215 × 4 (active)
#> from to disjoint geometry
#> <int> <int> <lgl> <LINESTRING [m]>
#> 1 1 2 TRUE (4151782 3207612, 4151765 3207609)
#> 2 3 4 TRUE (4151784 3208259, 4151728 3208240)
#> 3 5 6 FALSE (4151472 3207948, 4151474 3207941, 4151473 3207934, 4151…
#> 4 7 8 TRUE (4150948 3207637, 4150990 3207647)
#> 5 9 10 FALSE (4151393 3207929, 4151388 3207948, 4151383 3207963)
#> 6 11 12 TRUE (4152127 3207036, 4152139 3207043, 4152146 3207047)
#> # ℹ 1,209 more rows
#> #
#> # Node data: 987 × 1
#> geometry
#> <POINT [m]>
#> 1 (4151782 3207612)
#> 2 (4151765 3207609)
#> 3 (4151784 3208259)
#> # ℹ 984 more rows
# Use predicate query function directly.
intersects = with_graph(net, edge_intersects(poly))
head(intersects)
#> [1] FALSE FALSE TRUE FALSE TRUE FALSE