Counts the documents of each cluster of a clustered hypergraph, as a number and as a share of the clustered documents, and, when a weight per document is given (a repeat count, a citation count), the weighted size and share as well.
Usage
hg_topic_sizes(hg, clusters, weights = NULL)
# S3 method for class 'hypernets_topic_sizes'
plot(x, ...)Arguments
- hg
The document hypergraph the clustering was computed on.
- clusters
The tidy table returned by
hg_cluster()(columnsnode,cluster), or a named vector of cluster labels.- weights
NULL(default), or a numeric vector named by document giving each document's weight.- x
A table returned by the verb.
- ...
Unused; for S3 consistency.
Value
A base data.frame of class hypernets_topic_sizes, one row per
topic in natural order: topic, n, share, and with weights
also weighted_n and weighted_share. plot() draws the shares as
horizontal bars, weighted beside unweighted when both exist. Raises
hypernets_bad_input for unknown node names or weights that do not name
every clustered document.
Examples
hg <- text_hypergraph(c(
cooking_1 = "simmer the soup with onions and carrots",
cooking_2 = "this soup recipe needs salt on a cold night",
space_1 = "the telescope revealed a distant galaxy and stars",
space_2 = "astronomers aimed the telescope at the stars all night"
), stop_words = c("the", "with", "and", "a", "this", "at", "on", "all"))
topics <- hg_cluster(hg, k = 2, seed = 1)
hg_topic_sizes(hg, topics)
#> topic n share
#> 1 Cluster 1 2 0.5
#> 2 Cluster 2 2 0.5
hg_topic_sizes(hg, topics, weights = c(cooking_1 = 3, cooking_2 = 1,
space_1 = 1, space_2 = 1))
#> topic n share weighted_n weighted_share
#> 1 Cluster 1 2 0.5 4 0.6666667
#> 2 Cluster 2 2 0.5 2 0.3333333