Get plot for clustering result — cluster

Kmeans clustering is performed using add_clusters.

Usage

cluster_plot(
  data,
  cols,
  newcol = NULL,
  k = NULL,
  method = NULL,
  type = "lines",
  reorder = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

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.
type: The plot type, one of "lines" or "heatmap".
reorder: Reorder items to minimize line crossings, Either TRUE to automatically select a method ("olo" if seriation is installed, otherwise "min"), or one of the character values "max", "min", "spread", "gw", or "olo". Defaults to FALSE which disables reordering.
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 plot_metrics.

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

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

#> In the plot, 4 missing case(s) omitted.