textplot_network {quanteda.textplots} | R Documentation |
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 |
|
min_freq |
a frequency count threshold or proportion for co-occurrence frequencies of features to be included. |
omit_isolated |
if |
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_labelfont |
font-family of texts. Use default font if
|
vertex_labelsize |
size of vertex labels in mm. Defaults to size 5. Supports both integer values and vector values. |
offset |
if |
... |
additional arguments passed to network or
graph_from_adjacency_matrix. Not
used for |
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
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)
}