widely_kmeans: Cluster items based on k-means across features

In dgrtwo/widyr: Widen, Process, then Re-Tidy Data

Cluster items based on k-means across features

Description

Given a tidy table of features describing each item, perform k-means clustering using kmeans() and retidy the data into one-row-per-cluster.

Usage

widely_kmeans(tbl, item, feature, value, k, fill = 0, ...)

Arguments

tbl	Table
item	Item to cluster (as a bare column name)
feature	Feature column (dimension in clustering)
value	Value column
k	Number of clusters
fill	What to fill in for missing values
...	Other arguments passed on to kmeans()

See Also

widely_hclust()

Examples

library(gapminder)
library(dplyr)

clusters <- gapminder %>%
  widely_kmeans(country, year, lifeExp, k = 5)

clusters

clusters %>%
  count(cluster)

# Examine a few clusters
clusters %>% filter(cluster == 1)
clusters %>% filter(cluster == 2)

dgrtwo/widyr documentation built on Nov. 14, 2022, 4:07 a.m.