These functions wraps the igraph::bfs()
and igraph::dfs()
functions to
provide a consistent return value that can be used in dplyr::mutate()
calls. Each function returns an integer vector with values matching the order
of the nodes in the graph.
bfs_rank(root, mode = "out", unreachable = FALSE)
bfs_parent(root, mode = "out", unreachable = FALSE)
bfs_before(root, mode = "out", unreachable = FALSE)
bfs_after(root, mode = "out", unreachable = FALSE)
bfs_dist(root, mode = "out", unreachable = FALSE)
dfs_rank(root, mode = "out", unreachable = FALSE)
dfs_rank_out(root, mode = "out", unreachable = FALSE)
dfs_parent(root, mode = "out", unreachable = FALSE)
dfs_dist(root, mode = "out", unreachable = FALSE)
The node to start the search from
How edges are followed in the search if the graph is directed.
"out"
only follows outbound edges, "in"
only follows inbound edges, and
"all"
or "total"
follows all edges. This is ignored for undirected
graphs.
Should the search jump to a new component if the search is
terminated without all nodes being visited? Default to FALSE
(only reach
connected nodes).
An integer vector, the nature of which is determined by the function.
bfs_rank()
: Get the succession in which the nodes are visited in a breath first search
bfs_parent()
: Get the nodes from which each node is visited in a breath first search
bfs_before()
: Get the node that was visited before each node in a breath first search
bfs_after()
: Get the node that was visited after each node in a breath first search
bfs_dist()
: Get the number of nodes between the root and each node in a breath first search
dfs_rank()
: Get the succession in which the nodes are visited in a depth first search
dfs_rank_out()
: Get the succession in which each nodes subtree is completed in a depth first search
dfs_parent()
: Get the nodes from which each node is visited in a depth first search
dfs_dist()
: Get the number of nodes between the root and each node in a depth first search
# Get the depth of each node in a tree
create_tree(10, 2) %>%
activate(nodes) %>%
mutate(depth = bfs_dist(root = 1))
#> # A tbl_graph: 10 nodes and 9 edges
#> #
#> # A rooted tree
#> #
#> # Node Data: 10 × 1 (active)
#> depth
#> <int>
#> 1 0
#> 2 1
#> 3 1
#> 4 2
#> 5 2
#> 6 2
#> 7 2
#> 8 3
#> 9 3
#> 10 3
#> #
#> # Edge Data: 9 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 1 3
#> 3 2 4
#> # ℹ 6 more rows
# Reorder nodes based on a depth first search from node 3
create_notable('franklin') %>%
activate(nodes) %>%
mutate(order = dfs_rank(root = 3)) %>%
arrange(order)
#> # A tbl_graph: 12 nodes and 18 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 12 × 1 (active)
#> order
#> <int>
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
#> 6 6
#> 7 7
#> 8 8
#> 9 9
#> 10 10
#> 11 11
#> 12 12
#> #
#> # Edge Data: 18 × 2
#> from to
#> <int> <int>
#> 1 2 3
#> 2 1 2
#> 3 2 7
#> # ℹ 15 more rows