cluster_tab: Get tables for clustering result

Description

Kmeans clustering is performed using add_clusters.

cluster_tab(
  data,
  cols,
  newcol = NULL,
  k = NULL,
  method = "kmeans",
  labels = TRUE,
  clean = TRUE,
  ...
)

A volker list with with three volker tabs: cluster centers, cluster counts, and clustering diagnostics.

data: A tibble.
cols: A tidy selection of item columns or a single column with cluster values as a factor. If the column already contains a cluster result from add_clusters, it is used, and other parameters are ignored. If no cluster result exists, it is calculated with add_clusters.
newcol: Name of the new cluster column as a character vector. Set to NULL (default) to automatically build a name from the common column prefix, prefixed with "cls_".
k: Number of clusters to calculate. Set to NULL to output a scree plot for up to 10 clusters and automatically choose the number of clusters based on the elbow criterion. The within-sums of squares for the scree plot are calculated by stats::kmeans.
method: The method as character value. Currently, only kmeans is supported. All items are scaled before performing the cluster analysis using base::scale.
labels: If TRUE (default) extracts labels from the attributes, see codebook.
clean: Prepare data by data_clean.
...: Placeholder to allow calling the method with unused parameters from tab_metrics.

library(volker)
data <- volker::chatgpt

cluster_tab(data, starts_with("cg_adoption"), k = 2)

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