Cluster pairs of items into groups using hierarchical clustering

Reshape a table that represents pairwise distances into hierarchical clusters, returning a table with item and cluster columns.

Usage

widely_hclust(tbl, item1, item2, distance, k = NULL, h = NULL)

Arguments

tbl

Table

item1

First item

item2

Second item

distance

Distance column

k

The desired number of groups

h

Height at which to cut the hierarchically clustered tree

See also

cutree

Examples

library(gapminder)
library(dplyr)

# Construct Euclidean distances between countries based on life
# expectancy over time
country_distances <- gapminder %>%
  pairwise_dist(country, year, lifeExp)

country_distances
#> # A tibble: 20,022 × 3
#>    item1      item2       distance
#>    <fct>      <fct>          <dbl>
#>  1 Albania    Afghanistan   107.  
#>  2 Algeria    Afghanistan    76.8 
#>  3 Angola     Afghanistan     4.65
#>  4 Argentina  Afghanistan   110.  
#>  5 Australia  Afghanistan   129.  
#>  6 Austria    Afghanistan   124.  
#>  7 Bahrain    Afghanistan    98.1 
#>  8 Bangladesh Afghanistan    45.3 
#>  9 Belgium    Afghanistan   125.  
#> 10 Benin      Afghanistan    39.3 
#> # … with 20,012 more rows

# Turn this into 5 hierarchical clusters
clusters <- country_distances %>%
  widely_hclust(item1, item2, distance, k = 8)

# Examine a few such clusters
clusters %>% filter(cluster == 1)
#> # A tibble: 27 × 2
#>    item                   cluster
#>    <chr>                  <fct>  
#>  1 Albania                1      
#>  2 Argentina              1      
#>  3 Bosnia and Herzegovina 1      
#>  4 Bulgaria               1      
#>  5 Costa Rica             1      
#>  6 Croatia                1      
#>  7 Cuba                   1      
#>  8 Czech Republic         1      
#>  9 Hungary                1      
#> 10 Jamaica                1      
#> # … with 17 more rows
clusters %>% filter(cluster == 2)
#> # A tibble: 26 × 2
#>    item        cluster
#>    <chr>       <fct>  
#>  1 Algeria     2      
#>  2 China       2      
#>  3 Egypt       2      
#>  4 El Salvador 2      
#>  5 Guatemala   2      
#>  6 Honduras    2      
#>  7 India       2      
#>  8 Indonesia   2      
#>  9 Iran        2      
#> 10 Iraq        2      
#> # … with 16 more rows