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Filter Edges by Metadata

Source: R/network-utils.R
filter_edges.Rd

Filter edges using dplyr-style expressions on any edge column. Returns a cograph_network object by default (universal format), or optionally a matrix, igraph, statnet network or tna object when keep_format = TRUE and the input used one of those formats.

Usage

filter_edges(
  x,
  ...,
  keep_isolates = TRUE,
  keep_format = FALSE,
  directed = NULL,
  .keep_isolates = NULL
)

subset_edges(
  x,
  ...,
  keep_isolates = TRUE,
  keep_format = FALSE,
  directed = NULL,
  .keep_isolates = NULL
)

Arguments

x

Network input: cograph_network, matrix, igraph, network, or tna object.

...

Filter expressions using any edge column (e.g., weight > 0.5, weight > mean(weight), abs(weight) > 0.3).

keep_isolates

Logical. Keep nodes that end up with no edges? Default TRUE, matching igraph::delete_edges() and tidygraph: filtering edges does not remove nodes. Set FALSE to drop them, or call remove_isolates() afterwards.

keep_format

Logical. If TRUE, matrix, igraph, statnet network and tna inputs are returned in that format. Default FALSE returns cograph_network (universal format).

directed

Logical or NULL. If NULL (default), auto-detect from matrix symmetry. Set TRUE to force directed, FALSE to force undirected. Only used for non-cograph_network inputs.

.keep_isolates

Deprecated. Use keep_isolates.

Value

A cograph_network object with filtered edges. If keep_format = TRUE, matrix, igraph, statnet network and tna inputs are converted back to that type. With keep_isolates = TRUE, a node that loses all its edges stays in the network and a cograph_isolates_created warning is raised.

See also

filter_nodes, splot, subset_edges

Examples

filter_edges(regulation_net, weight > 0.15)
#> Cograph network: 10 nodes, 22 edges ( directed )
#> Source: matrix 
#>   Nodes (10): Explore, Plan, Monitor, Adapt, Reflect, Discuss, ... +4 more
#>   Edges: 22 / 90 (density: 24.4%)
#>   Weights: [0.160, 0.490]  |  mean: 0.323
#>   Strongest edges:
#>     Share -> Monitor  0.490
#>     Plan -> Evaluate  0.490
#>     Evaluate -> Adapt  0.430
#>     Synthesize -> Reflect  0.420
#>     Plan -> Discuss  0.400
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.