Plot a network of feature co-occurrences

Plot an fcm object as a network, where edges show co-occurrences of features.

textplot_network(
  x,
  min_freq = 0.5,
  omit_isolated = TRUE,
  edge_color = "#1F78B4",
  edge_alpha = 0.5,
  edge_size = 2,
  vertex_color = "#4D4D4D",
  vertex_size = 2,
  vertex_labelcolor = NULL,
  vertex_labelfont = NULL,
  vertex_labelsize = 5,
  offset = NULL,
  ...
)

# S3 method for fcm
as.network(x, min_freq = 0.5, omit_isolated = TRUE, ...)

# S3 method for fcm
as.igraph(x, min_freq = 0.5, omit_isolated = TRUE, ...)

Arguments

x	a fcm or dfm object
min_freq	a frequency count threshold or proportion for co-occurrence frequencies of features to be included.
omit_isolated	if TRUE, features do not occur more frequent than min_freq will be omitted.
edge_color	color of edges that connect vertices.
edge_alpha	opacity of edges ranging from 0 to 1.0.
edge_size	size of edges for most frequent co-occurrence The size of other edges are determined proportionally to the 99th percentile frequency instead of the maximum to reduce the impact of outliers.
vertex_color	color of vertices.
vertex_size	size of vertices
vertex_labelcolor	color of texts. Defaults to the same as vertex_color. If NA is given, texts are not rendered.
vertex_labelfont	font-family of texts. Use default font if NULL.
vertex_labelsize	size of vertex labels in mm. Defaults to size 5. Supports both integer values and vector values.
offset	if NULL, the distance between vertices and texts are determined automatically.
...	additional arguments passed to network or graph_from_adjacency_matrix. Not used for as.igraph.

Details

Currently the size of the network is limited to 1000, because of the computationally intensive nature of network formation for larger matrices. When the fcm is large, users should select features using fcm_select, set the threshold using min_freq, or implement own plotting function using as.network().

See also

fcm()

network::network()

igraph::graph_from_adjacency_matrix()

Author

Kohei Watanabe and Stefan Müller

Examples

set.seed(100)
toks <- data_char_ukimmig2010 %>%
    tokens(remove_punct = TRUE) %>%
    tokens_tolower() %>%
    tokens_remove(pattern = stopwords("english"), padding = FALSE)
fcmat <- fcm(toks, context = "window", tri = FALSE)
feat <- names(topfeatures(fcmat, 30))
fcm_select(fcmat, pattern = feat) %>%
    textplot_network(min_freq = 0.5)
fcm_select(fcmat, pattern = feat) %>%
    textplot_network(min_freq = 0.8)
fcm_select(fcmat, pattern = feat) %>%
    textplot_network(min_freq = 0.8, vertex_labelcolor = rep(c('gray40', NA), 15))
fcm_select(fcmat, pattern = feat) %>%
    textplot_network(vertex_labelsize = 10)
fcm_30 <- fcm_select(fcmat, pattern = feat)
textplot_network(fcm_30, vertex_labelsize = rowSums(fcm_30)/min(rowSums(fcm_30)))
# Vector inputs to vertex_labelsize can be scaled if too small / large
textplot_network(fcm_30, vertex_labelsize = 1.5 * rowSums(fcm_30)/min(rowSums(fcm_30)))

# as.igraph
if (requireNamespace("igraph", quietly = TRUE)) {
    txt <- c("a a a b b c", "a a c e", "a c e f g")
    mat <- fcm(txt)
    as.igraph(mat, min_freq = 1, omit_isolated = FALSE)
}
#> IGRAPH b8beaee DN-- 6 26 -- 
#> + attr: name (v/c), frequency (v/n)
#> + edges from b8beaee (vertex names):
#>  [1] a->b a->b a->b a->b a->b a->b a->c a->c a->c a->c a->c a->c b->c b->c a->e
#> [16] a->e a->e c->e c->e a->f c->f e->f a->g c->g e->g f->g