Filter nodes and edges in a GraphSpace object

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)

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.

Value

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

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

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)
#> Validating the 'igraph' object...
#> Vertex attributes 'x' and 'y' missing; computing layout...
#> Vertex attribute 'name' missing; assigning names... 
#> Ignoring graph-level attributes: 'name', 'mode', 'center'
#> Creating a 'GraphSpace' object...
gs <- normalizeGraphSpace(gs)
#> Normalizing node coordinates to graph space...

#--- 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)
#> Warning: The 'GraphSpace' object has no edges to filter.

#--- 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")
#> Warning: No edges matched the filter expression.
#> ℹ The returned object contains no edges.

# 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)