A Common API to Clustering.
tidyclust 
The goal of tidyclust is to provide a tidy, unified interface to clustering models. The packages is closely modeled after the parsnip package.
Installation
You can install the released version of tidyclust from CRAN with:
install.packages("tidyclust")
and the development version of tidyclust from GitHub with:
# install.packages("pak")
pak::pak("tidymodels/tidyclust")
Example
The first thing you do is to create a cluster specification. For this example we are creating a K-means model, using the stats engine.
library(tidyclust)
set.seed(1234)
kmeans_spec <- k_means(num_clusters = 3) %>%
set_engine("stats")
kmeans_spec
#> K Means Cluster Specification (partition)
#>
#> Main Arguments:
#> num_clusters = 3
#>
#> Computational engine: stats
This specification can then be fit using data.
kmeans_spec_fit <- kmeans_spec %>%
fit(~., data = mtcars)
kmeans_spec_fit
#> tidyclust cluster object
#>
#> K-means clustering with 3 clusters of sizes 7, 11, 14
#>
#> Cluster means:
#> mpg cyl disp hp drat wt qsec vs
#> 1 19.74286 6 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286
#> 3 26.66364 4 105.1364 82.63636 4.070909 2.285727 19.13727 0.9090909
#> 2 15.10000 8 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
#> am gear carb
#> 1 0.4285714 3.857143 3.428571
#> 3 0.7272727 4.090909 1.545455
#> 2 0.1428571 3.285714 3.500000
#>
#> Clustering vector:
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 1 1 2 1
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 3 1 3 2
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 2 1 1 3
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 3 3 3 3
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 3 2 2 2
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 2 3 3 3
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 3 2 2 2
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 3 1 3 2
#>
#> Within cluster sum of squares by cluster:
#> [1] 13954.34 11848.37 93643.90
#> (between_SS / total_SS = 80.8 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
Once you have a fitted tidyclust object, you can do a number of things. predict() returns the cluster a new observation belongs to
predict(kmeans_spec_fit, mtcars[1:4, ])
#> # A tibble: 4 × 1
#> .pred_cluster
#> <fct>
#> 1 Cluster_1
#> 2 Cluster_1
#> 3 Cluster_2
#> 4 Cluster_1
extract_cluster_assignment() returns the cluster assignments of the training observations
extract_cluster_assignment(kmeans_spec_fit)
#> # A tibble: 32 × 1
#> .cluster
#> <fct>
#> 1 Cluster_1
#> 2 Cluster_1
#> 3 Cluster_2
#> 4 Cluster_1
#> 5 Cluster_3
#> 6 Cluster_1
#> 7 Cluster_3
#> 8 Cluster_2
#> 9 Cluster_2
#> 10 Cluster_1
#> # ℹ 22 more rows
and extract_centroids() returns the locations of the clusters
extract_centroids(kmeans_spec_fit)
#> # A tibble: 3 × 12
#> .cluster mpg cyl disp hp drat wt qsec vs am gear carb
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Cluster_1 19.7 6 183. 122. 3.59 3.12 18.0 0.571 0.429 3.86 3.43
#> 2 Cluster_2 26.7 4 105. 82.6 4.07 2.29 19.1 0.909 0.727 4.09 1.55
#> 3 Cluster_3 15.1 8 353. 209. 3.23 4.00 16.8 0 0.143 3.29 3.5
Visual comparison of clustering methods
Below is a visualization of the available models and how they compare using 2 dimensional toy data sets.
Contributing
This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
If you think you have encountered a bug, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help. Footer.