gs_subset: Filter nodes and edges in a GraphSpace object

Description

gs_subset_nodes() retains a subset of nodes and automatically removes any edge whose endpoint is no longer present.

gs_subset_edges() retains a subset of edges without modifying the node set.

Usage

gs_subset_nodes(x, i)
gs_subset_edges(x, i)

Value

A GraphSpace object with the selected subset of nodes or edges.

Arguments

x

A GraphSpace object.

i

A filter specification. Accepted forms:

A character vector of node names (gs_subset_nodes() only; edges are identified by integer position or predicate, not by name).
An integer vector of positional indices into the node or edge table.
A logical vector whose length must match the number of nodes or edges, respectively.
An unquoted predicate evaluated against the node or edge data frame using data masking, such as nodeSize > 5 or weight > 0.5. Column names from the relevant table are available directly as variables inside the expression.

Details

Node filtering preserves the normalized coordinate state. Coordinates for surviving nodes remain in their current space ([0, 1] if normalized, raw coordinates otherwise), so normalizeGraphSpace does not need to be re-run. The @graph, @fdata, @nodes, and @edges slots are all updated consistently. The @canvas and background image are not modified.

Edge filtering leaves the node set and the layout entirely intact. Because removing an edge from a group of parallel edges invalidates the derived attributes curve_weight, is_multiple, and is_loop for the remaining members of that group, the full edge table is recomputed from @graph after deletion.

Note on parallel edges: in non-simplified graphs containing parallel edges between the same vertex pair, integer or logical indexing is the most reliable approach. A predicate expression that matches a shared attribute (such as edgeColor) will match all parallel instances simultaneously, which is usually the intended behavior.

Examples

Run this code

library(RGraphSpace)
library(igraph)

# Create a directed star graph with numeric attributes
g <- make_star(10, mode = "out")
V(g)$nodeSize <- runif(vcount(g), 1, 10)
E(g)$weight   <- runif(ecount(g), 0, 1)
gs <- GraphSpace(g)
gs <- normalizeGraphSpace(gs)

#--- gs_subset_nodes examples ---

# By node name (character vector)
gs2 <- gs_subset_nodes(gs, c("n1", "n2", "n3"))

# By integer position
gs2 <- gs_subset_nodes(gs, 1:5)

# By predicate (data masking against @nodes columns)
gs2 <- gs_subset_nodes(gs, nodeSize > 5)

# By pre-evaluated logical vector
keep <- gs$nodeSize > 5
gs2  <- gs_subset_nodes(gs, keep)

# Combining with pipes
gs2 <- gs |>
  gs_subset_nodes(nodeSize > 5) |>
  gs_subset_edges(weight > 0.3)

#--- gs_subset_edges examples ---

# By predicate on an edge attribute
gs3 <- gs_subset_edges(gs, weight > 0.5)

# By endpoint names: name1 and name2 are columns in @edges and
# can be used directly inside any predicate expression
gs3 <- gs_subset_edges(gs, name1 == "n1")
gs3 <- gs_subset_edges(gs, name2 == "n1")

# Combining endpoint and attribute conditions
gs3 <- gs_subset_edges(gs, name1 == "n1" & weight > 0.5)

# By integer position
gs3 <- gs_subset_edges(gs, 1:3)

# By logical vector
gs3 <- gs_subset_edges(gs, gs_edges(gs)$weight > 0.5)