Generate a clustering description plot from a rainette result

Generate a clustering description plot from a rainette result

Usage

rainette_plot(
  res,
  dtm,
  k = NULL,
  type = c("bar", "cloud"),
  n_terms = 15,
  free_scales = FALSE,
  measure = c("chi2", "lr", "frequency", "docprop"),
  show_negative = FALSE,
  text_size = NULL,
  show_na_title = TRUE,
  cluster_label = NULL,
  keyness_plot_xlab = NULL
)

Arguments

res

result object of a rainette clustering

dtm

the dfm object used to compute the clustering

k

number of groups. If NULL, use the biggest number possible

type

type of term plots : barplot or wordcloud

n_terms

number of terms to display in keyness plots

free_scales

if TRUE, all the keyness plots will have the same scale

measure

statistics to compute

show_negative

if TRUE, show negative keyness features

text_size

font size for barplots, max word size for wordclouds

show_na_title

if TRUE, show number of NA as plot title

cluster_label

define a specific term for clusters identification in keyness plots. Default is "Cluster" or "Cl." depending on the number of groups.

keyness_plot_xlab

define a specific x label for keyness plots.

Value

A gtable object.

See also

quanteda.textstats::textstat_keyness(), rainette_explor(), rainette_stats()

Examples

# \donttest{
require(quanteda)
corpus <- data_corpus_inaugural
corpus <- head(corpus, n = 10)
corpus <- split_segments(corpus)
#>   Splitting...
#>   Done.
tok <- tokens(corpus, remove_punct = TRUE)
tok <- tokens_remove(tok, stopwords("en"))
dtm <- dfm(tok, tolower = TRUE)
dtm <- dfm_trim(dtm, min_docfreq = 3)
res <- rainette(dtm, k = 3, min_segment_size = 15)
#>   Merging segments to comply with min_segment_size...
#>   Clustering...
#>   Done.
rainette_plot(res, dtm)

# }