Add cluster number to a data frame

Clustering is performed using stats::kmeans.

Usage

add_clusters(data, cols, newcol = NULL, k = 2, method = "kmeans", clean = TRUE)

Arguments

data

A dataframe.

cols

A tidy selection of item columns.

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.

clean

Prepare data by data_clean.

Value

The input tibble with additional column containing cluster values as a factor. The new column is prefixed with "cls_". The new column contains the fit result in the attribute stats.kmeans.fit. The names of the items used for clustering are stored in the attribute stats.kmeans.items. The clustering diagnostics (Within-Cluster and Between-Cluster Sum of Squares) are stored in the attribute stats.kmeans.wss.

Examples

library(volker)
ds <- volker::chatgpt

volker::add_clusters(ds, starts_with("cg_adoption"), k = 3)
#> # A tibble: 97 × 20
#>     case use_private use_work cg_adoption_advantage_01 cg_adoption_advantage_02
#>    <dbl>       <dbl>    <dbl>                    <dbl>                    <dbl>
#>  1   170           4        4                        3                        4
#>  2   183           1        1                        4                        3
#>  3   195           2        4                        5                        5
#>  4   212           5        5                        4                        4
#>  5   222           2        3                        3                        2
#>  6   236           3        1                        3                        2
#>  7   255           3        1                        3                        1
#>  8   297           3        4                        4                        3
#>  9   309           3        3                        3                        4
#> 10   325           2        1                        4                        1
#> # ℹ 87 more rows
#> # ℹ 15 more variables: cg_adoption_advantage_03 <dbl>,
#> #   cg_adoption_advantage_04 <dbl>, cg_adoption_fearofuse_01 <dbl>,
#> #   cg_adoption_fearofuse_02 <dbl>, cg_adoption_fearofuse_03 <dbl>,
#> #   cg_adoption_fearofuse_04 <dbl>, cg_adoption_social_01 <dbl>,
#> #   cg_adoption_social_02 <dbl>, cg_adoption_social_03 <dbl>,
#> #   cg_adoption_social_04 <dbl>, adopter <fct>, sd_age <dbl>, …