cluster: Cluster wrapper function

In clustRcompaR: Easy Interface for Clustering a Set of Documents and Exploring Group- Based Patterns

Description

Cluster wrapper function

Usage

cluster(data, ..., n_clusters, minimum_term_frequency = 3, min_terms = 3,
  num_terms = 10, stopwords = NULL, remove_twitter = FALSE)

Arguments

data	The data frame comparing the text vector as the first column
...	Additional columns of the data frame containing metadata cfor comparison
n_clusters	The number of clusters to be used for the clustering solution
minimum_term_frequency	The minimum number of occurences for a term to be included
min_terms	The minimum number of terms for a document to be included
num_terms	Number of terms to display in clustering summary output
stopwords	Additional stopwords to exclude from clustering analysis
remove_twitter	Whether to remove text associated with Twitter content, useful for when analyzing data from this source (defaults to FALSE)

Details

Performs the clustering half of the process, including assembling and cleaning the corpus, deviationalizing and clustering.

Examples

library(clustRcompaR)
library(dplyr)
library(quanteda)

d <- inaugural_addresses
d <- mutate(d, century = ifelse(Year < 1800, "17th",
                                ifelse(Year >= 1800 & Year < 1900, "18th",
                                       ifelse(Year >= 1900 & Year < 2000, "19th", "20th"))))

three_clusters <- cluster(d, century, n_clusters = 3)
extract_terms(three_clusters)

three_clusters_comparison <- compare(three_clusters, "century")
compare_plot(three_clusters_comparison)

clustRcompaR documentation built on May 1, 2019, 11:16 p.m.