do_cluster: K-Means Cluster

In aedobbyn/dobtools: dobtools

Description

From beginning to end

Usage

do_cluster(df, vars, to_scale, n_centers = 5,
  algorithm = "Hartigan-Wong", trace = FALSE)

Arguments

df	A dataframe
vars	Variables from that dataframe to keep, some of which will be scaled and used for clustering.
to_scale	A subset of vars, all numeric, to be scaled and used for clustering.
n_centers	The number of clusters you want.
algorithm	kmeans algo to use
trace	Trace progress?

Examples

v <- names(iris)
t_s <- v[-which(v == "Species" | v == "Petal.Length")]

clustered_iris <- do_cluster(iris, vars = v, to_scale = t_s, n_centers = 3)
## Not run: 
ggplot(data = clustered_iris %>%
                  trim_outliers(exclude = c("cluster_assignment", "Species"))) +
  geom_text(aes(Sepal.Width_scaled, Sepal.Length_scaled,
                colour = cluster_assignment,
                label = Species)) +
  theme_bw()

## End(Not run)

aedobbyn/dobtools documentation built on May 28, 2019, 2:33 a.m.