This function extracts the neighborhood of each node as a graph and maps over
each of these neighborhood graphs. Conceptually it is similar to
igraph::local_scan(), but it borrows the type safe versions available in
map_bfs() and map_dfs().
map_local(order = 1, mode = "all", mindist = 0, .f, ...)
map_local_lgl(order = 1, mode = "all", mindist = 0, .f, ...)
map_local_chr(order = 1, mode = "all", mindist = 0, .f, ...)
map_local_int(order = 1, mode = "all", mindist = 0, .f, ...)
map_local_dbl(order = 1, mode = "all", mindist = 0, .f, ...)Integer giving the order of the neighborhood.
Character constant, it specifies how to use the direction of
the edges if a directed graph is analyzed. For ‘out’ only the
outgoing edges are followed, so all vertices reachable from the source
vertex in at most order steps are counted. For ‘"in"’ all
vertices from which the source vertex is reachable in at most order
steps are counted. ‘"all"’ ignores the direction of the edges. This
argument is ignored for undirected graphs.
The minimum distance to include the vertex in the result.
A function to map over all nodes. See Details
Additional parameters to pass to .f
map_local() returns a list of the same length as the number of
nodes in the graph, in the order matching the node order in the graph.
map_local_*() tries to coerce its result into a vector of the classes
logical (map_local_lgl), character (map_local_chr), integer
(map_local_int), or double (map_local_dbl). These functions will throw
an error if they are unsuccesful, so they are type safe.
The function provided to .f will be called with the following arguments in
addition to those supplied through ...:
neighborhood: The neighborhood graph of the node
graph: The full tbl_graph object
node: The index of the node currently mapped over
The neighborhood graph will contain an extra node attribute called
.central_node, which will be TRUE for the node that the neighborhood is
expanded from and FALSE for everything else.
# Smooth out values over a neighborhood
create_notable('meredith') %>%
mutate(value = rpois(graph_order(), 5)) %>%
mutate(value_smooth = map_local_dbl(order = 2, .f = function(neighborhood, ...) {
mean(as_tibble(neighborhood, active = 'nodes')$value)
}))
#> # A tbl_graph: 70 nodes and 140 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 70 × 2 (active)
#> value value_smooth
#> <int> <dbl>
#> 1 4 5.18
#> 2 5 4.55
#> 3 2 4.91
#> 4 6 5.27
#> 5 5 4.55
#> 6 7 4.55
#> 7 3 4.55
#> 8 7 5.27
#> 9 7 4.73
#> 10 2 4.27
#> # ℹ 60 more rows
#> #
#> # Edge Data: 140 × 2
#> from to
#> <int> <int>
#> 1 1 5
#> 2 1 6
#> 3 1 7
#> # ℹ 137 more rows