library(tidymodels)
centers <- tibble(
cluster = factor(1:3),
num_points = c(100, 150, 50), # number points in each cluster
x1 = c(5, 0, -3), # x1 coordinate of cluster center
x2 = c(-1, 1, -2) # x2 coordinate of cluster center
)
set.seed(27)
labelled_points <-
centers %>%
mutate(
x1 = map2(num_points, x1, rnorm),
x2 = map2(num_points, x2, rnorm)
) %>%
select(-num_points) %>%
unnest(cols = c(x1, x2))
points <-
labelled_points %>%
select(-cluster)
points
#> # A tibble: 300 x 2
#> x1 x2
#> <dbl> <dbl>
#> 1 6.91 -2.74
#> 2 6.14 -2.45
#> 3 4.24 -0.946
#> 4 3.54 0.287
#> 5 3.91 0.408
#> 6 5.30 -1.58
#> 7 5.01 -1.77
#> 8 6.16 -1.68
#> 9 7.13 -2.17
#> 10 5.24 -2.42
#> # … with 290 more rows
kclust <- kmeans(points, centers = 3)
points_joined <- augment(kclust, points) %>%
inner_join(tidy(kclust) %>%
select(x1, x2, cluster),
by = c(".cluster" = "cluster"),
suffix = c("_points", "_centers"))
points_joined
#> # A tibble: 300 x 5
#> x1_points x2_points .cluster x1_centers x2_centers
#> <dbl> <dbl> <fct> <dbl> <dbl>
#> 1 6.91 -2.74 3 5.00 -1.05
#> 2 6.14 -2.45 3 5.00 -1.05
#> 3 4.24 -0.946 3 5.00 -1.05
#> 4 3.54 0.287 3 5.00 -1.05
#> 5 3.91 0.408 3 5.00 -1.05
#> 6 5.30 -1.58 3 5.00 -1.05
#> 7 5.01 -1.77 3 5.00 -1.05
#> 8 6.16 -1.68 3 5.00 -1.05
#> 9 7.13 -2.17 3 5.00 -1.05
#> 10 5.24 -2.42 3 5.00 -1.05
#> # … with 290 more rows
points_joined %>%
select(.cluster, x1_centers, x2_centers)
#> # A tibble: 300 x 3
#> .cluster x1_centers x2_centers
#> <fct> <dbl> <dbl>
#> 1 3 5.00 -1.05
#> 2 3 5.00 -1.05
#> 3 3 5.00 -1.05
#> 4 3 5.00 -1.05
#> 5 3 5.00 -1.05
#> 6 3 5.00 -1.05
#> 7 3 5.00 -1.05
#> 8 3 5.00 -1.05
#> 9 3 5.00 -1.05
#> 10 3 5.00 -1.05
#> # … with 290 more rowsCreated on 2020-08-22 by the reprex package (v0.3.0.9001)