Generate a clustering description plot from a rainette result
Generate a clustering description plot from a rainette result
rainette_plot(
res,
dtm,
k = NULL,
type = c("bar", "cloud"),
n_terms = 15,
free_scales = FALSE,
measure = c("chi2", "lr"),
show_negative = TRUE,
text_size = NULL
)res |
result object of a |
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 |
A gtable object.
library(quanteda)
corpus <- data_corpus_inaugural
corpus <- head(corpus, n = 10)
corpus <- split_segments(corpus)
dtm <- dfm(corpus, remove = stopwords("en"), tolower = TRUE, remove_punct = TRUE)
dtm <- dfm_trim(dtm, min_termfreq = 3)
res <- rainette(dtm, k = 3)
rainette_plot(res, dtm)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.