textplot_network: Plot a network of feature co-occurrences

In quanteda.textplots: Plots for the Quantitative Analysis of Textual Data

Plot a network of feature co-occurrences

Description

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

Usage

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 class 'fcm'
as.network(x, min_freq = 0.5, omit_isolated = TRUE, ...)

## S3 method for class '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	colour 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	colour of vertices.
vertex_size	size of vertices
vertex_labelcolor	colour 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().

Author(s)

Kohei Watanabe and Stefan Müller

See Also

fcm

network::network()

igraph::graph_from_adjacency_matrix()

Examples

set.seed(100)
library("quanteda")
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 <- colSums(fcmat) |>
  sort(decreasing = TRUE) |>
  head(30) |>
  names()
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 = Matrix::rowSums(fcm_30) / min(Matrix::rowSums(fcm_30)))
# Vector inputs to vertex_labelsize can be scaled if too small / large
textplot_network(fcm_30,
                 vertex_labelsize = 1.5 * Matrix::rowSums(fcm_30) /
                                    min(Matrix::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(tokens(txt))
    as.igraph(mat, min_freq = 1, omit_isolated = FALSE)
}

quanteda.textplots documentation built on Sept. 11, 2024, 5:35 p.m.