heatmap_words {deepMOU}

Heatmap of word frequencies by cluster

Description

Displays the heatmap of the cluster frequency distributions of the most frequent terms sorted by the most informative ones.

Usage

heatmap_words(
  x,
  clusters,
  n_words = 50,
  legend_position = "bottom",
  font_size = 12,
  low_color = "grey92",
  top_color = "red",
  main = "Row frequencies of terms distribution",
  xlabel = NULL,
  ylabel = NULL,
  legend_title = "Entropy"
)

Arguments

x

Document-term matrix describing the frequency of terms that occur in a collection of documents. Rows correspond to documents in the collection and columns correspond to terms.

clusters

Integer vector of length of the number of cases, which indicates a clustering. The clusters have to be numbered from 1 to the number of clusters.

n_words

Number of words to include in the heatmap (default is 50).

legend_position

Position of the legend ("none", "left", "right", "bottom", "top", or two-element numeric vector as in theme). Default is "bottom".

font_size

Text size in pts (default is 12).

low_color

Base color for terms with no occurrence in a cluster (default is "grey92")

top_color

Base color for terms concentrated in a single cluster (default is "red")

main

A title for the plot. Default is "Row frequencies of terms distribution".

xlabel

A title for the x-axis. Default is NULL.

ylabel

A title for the y-axis. Default is NULL.

legend_title

A title for the legend. Default is "Entropy".

Details

Takes as input the bag-of-words matrix and returns a heatmap displaying the row frequency distribution of terms according to the clusters. Words are sorted by entropy.

Value

A graphical aid to describe the clusters according to the most informative words.

Examples

# Load the CNAE2 dataset
data("CNAE2")

# Get document labels by clustering using mou_EM
mou_CNAE2 = mou_EM(x = CNAE2, k = 2)

# Usage of the function
heatmap_words(x = mou_CNAE2$x, clusters = mou_CNAE2$clusters)