# Set a seed
suppressWarnings(RNGversion("3.5.0"))
set.seed(23)
# Create a simple graph
graph <-
create_graph() %>%
add_n_nodes(
n = 2,
type = "a",
label = c("asd", "iekd")) %>%
add_n_nodes(
n = 3,
type = "b",
label = c("idj", "edl", "ohd")) %>%
add_edges_w_string(
edges = "1->2 1->3 2->4 2->5 3->5",
rel = c(NA, "A", "B", "C", "D"))
# Create a data frame with node ID values
# representing the graph edges (with `from`
# and `to` columns), and, a set of numeric values
df_edges <-
data.frame(
from = c(1, 1, 2, 2, 3),
to = c(2, 3, 4, 5, 5),
values = round(rnorm(5, 5), 2))
# Create a data frame with node ID values
# representing the graph nodes (with the `id`
# columns), and, a set of numeric values
df_nodes <-
data.frame(
id = 1:5,
values = round(rnorm(5, 7), 2))
# Join the data frame to the graph's internal
# edge data frame (edf)
graph <-
graph %>%
join_edge_attrs(df = df_edges) %>%
join_node_attrs(df = df_nodes)
# Show the graph's internal node data frame
graph %>% get_node_df()
# Show the graph's internal edge data frame
graph %>% get_edge_df()
# Perform a simple traversal from node `4` to
# inward adjacent edges with no conditions
# on the nodes traversed to
graph %>%
select_nodes_by_id(nodes = 4) %>%
trav_in() %>%
get_selection()
# Traverse from node `5` to inbound-facing
# nodes, filtering to those nodes that have
# numeric values greater than `5.0` for
# the `values` node attribute
graph %>%
select_nodes_by_id(nodes = 4) %>%
trav_in(
conditions = values > 5.0) %>%
get_selection()
# Traverse from node `5` to any inbound
# nodes, filtering to those nodes that
# have a `type` attribute of `b`
graph %>%
select_nodes_by_id(nodes = 5) %>%
trav_in(
conditions = type == "b") %>%
get_selection()
# Traverse from node `5` to any inbound
# nodes, filtering to those nodes that
# have a degree of `2`
graph %>%
{
node_degrees <-
get_node_info(.) %>%
dplyr::select(id, deg)
join_node_attrs(., node_degrees)
} %>%
select_nodes_by_id(nodes = 5) %>%
trav_in(
conditions = deg == 2) %>%
get_selection()
# Traverse from node `5` to any inbound
# nodes, and use multiple conditions for the
# traversal
graph %>%
select_nodes_by_id(nodes = 5) %>%
trav_in(
conditions =
type == "a" &
values > 6.0) %>%
get_selection()
# Traverse from node `5` to any inbound
# nodes, and use multiple conditions with
# a single-length vector
graph %>%
select_nodes_by_id(nodes = 5) %>%
trav_in(
conditions =
type == "b" | values > 6.0) %>%
get_selection()
# Traverse from node `5` to any inbound
# nodes, and use a regular expression as
# a filtering condition
graph %>%
select_nodes_by_id(nodes = 2) %>%
trav_in(
conditions = grepl("^i.*", label)) %>%
get_selection()
# Create another simple graph to demonstrate
# copying of node attribute values to traversed
# nodes
graph <-
create_graph() %>%
add_node() %>%
select_nodes() %>%
add_n_nodes_ws(
n = 2,
direction = "from") %>%
clear_selection() %>%
select_nodes_by_id(nodes = 2:3) %>%
set_node_attrs_ws(
node_attr = value,
value = 5)
# Show the graph's internal node data frame
graph %>% get_node_df()
# Show the graph's internal edge data frame
graph %>% get_edge_df()
# Perform a traversal from the outer nodes
# (`2` and `3`) to the central node (`1`) while
# also applying the node attribute `value` to
# node `1` (summing the `value` of 5 from
# both nodes before applying the value to the
# target node)
graph <-
graph %>%
trav_in(
copy_attrs_from = value,
agg = "sum")
# Show the graph's internal node data frame
# after this change
graph %>% get_node_df()
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