# NOT RUN {
# Set a seed
set.seed(23)
# Create a simple graph
graph <-
create_graph() %>%
add_n_nodes(
2, type = "a",
label = c("asd", "iekd")) %>%
add_n_nodes(
3, type = "b",
label = c("idj", "edl", "ohd")) %>%
add_edges_w_string(
"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_edges) %>%
join_node_attrs(df_nodes)
get_node_df(graph)
#> id type label values
#> 1 1 a asd 8.58
#> 2 2 a iekd 7.22
#> 3 3 b idj 5.95
#> 4 4 b edl 6.71
#> 5 5 b ohd 7.48
get_edge_df(graph)
#> id from to rel values
#> 1 1 1 2 <NA> 6.00
#> 2 2 1 3 A 6.11
#> 3 3 2 4 B 4.72
#> 4 4 2 5 C 6.02
#> 5 5 3 5 D 5.05
# Perform a simple traversal from node `3`
# to outward adjacent nodes with no conditions
# on the nodes traversed to
graph %>%
select_nodes_by_id(3) %>%
trav_out() %>%
get_selection()
#> [1] 5
# Traverse from node `1` to outbound
# nodes, filtering to those nodes that have
# numeric values greater than `7.0` for
# the `values` node attribute
graph %>%
select_nodes_by_id(1) %>%
trav_out(
conditions = "values > 7.0") %>%
get_selection()
#> [1] 2
# Traverse from node `1` to any outbound
# nodes, filtering to those nodes that
# have a `type` attribute of `b`
graph %>%
select_nodes_by_id(1) %>%
trav_out(
conditions = "type == 'b'") %>%
get_selection()
#> [1] 3
# Traverse from node `2` to any outbound
# nodes, filtering to those nodes that
# have a degree of `1`
graph %>%
{
node_degrees <-
node_info(.) %>%
dplyr::select(id, deg)
join_node_attrs(., node_degrees)
} %>%
select_nodes_by_id(2) %>%
trav_out(
conditions = "deg == 1") %>%
get_selection()
#> [1] 4
# Traverse from node `2` to any outbound
# nodes, and use multiple conditions for the
# traversal (using a vector in `conditions`
# creates a set of `AND` conditions)
graph %>%
select_nodes_by_id(2) %>%
trav_out(
conditions = c(
"type == 'a'",
"values > 8.0")) %>%
get_selection()
#> [1] 2
# Traverse from node `2` to any outbound
# nodes, and use multiple conditions with
# a single-length vector (here, using a
# `|` to create a set of `OR` conditions)
graph %>%
select_nodes_by_id(2) %>%
trav_out(
conditions = c(
"type == 'b' | values > 8.0")) %>%
get_selection()
#> [1] 4 5
# Traverse from node `2` to any outbound
# nodes, and use a regular expression as
# a filtering condition
graph %>%
select_nodes_by_id(2) %>%
trav_out(
conditions = "grepl('..d', label)") %>%
get_selection()
#> [1] 5
# 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(2, "to") %>%
clear_selection() %>%
select_nodes_by_id(2:3) %>%
set_node_attrs_ws("value", 5)
# Show the graph's internal node data frame
graph %>% get_node_df()
#> id type label value
#> 1 1 <NA> <NA> NA
#> 2 2 <NA> <NA> 5
#> 3 3 <NA> <NA> 5
# Show the graph's internal edge data frame
graph %>% get_edge_df()
#> id from to rel
#> 1 1 2 1 <NA>
#> 2 2 3 1 <NA>
# 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_out(
copy_attrs_from = "value",
agg = "sum")
# Show the graph's internal node data frame
# after this change
graph %>% get_node_df()
#> id type label value
#> 1 1 <NA> <NA> 10
#> 2 2 <NA> <NA> 5
#> 3 3 <NA> <NA> 5
# }
Run the code above in your browser using DataCamp Workspace