Clusters Plots

Cluster Plots

It is possible to show the distinct clusters or groups using geom_encircle(). If the dataset has multiple weak features, you can compute the principal components and draw a scatterplot using PC1 and PC2 as X and Y axis.

The geom_encircle() can be used to encircle the desired groups. The only thing to note is the data argument to geom_encircle().

You need to provide a subsetted dataframe that contains only the observations (rows) that belong to the group as the data argument.

library(ggplot2)
library(ggalt)
library(ggfortify)
theme_set(theme_classic())

Compute data with principal components

df <- iris[c(1, 2, 3, 4)]
# compute principal components
pca_mod <- prcomp(df)  

Data frame of principal components

df_pc <- data.frame(pca_mod$x, Species=iris$Species)  
# dataframe of principal components
df_pc_vir <- df_pc[df_pc$Species == "virginica", ]   # df for 'virginica'
df_pc_set <- df_pc[df_pc$Species == "setosa", ]      # df for 'setosa'
df_pc_ver <- df_pc[df_pc$Species == "versicolor", ]  # df for 'versicolor'

Cluster Plots

ggplot(df_pc, aes(PC1, PC2, col=Species)) + 
  geom_point(aes(shape=Species), size=2) +   # draw points
  labs(title="Iris Clustering", 
       subtitle="With principal components PC1 and PC2 as X and Y axis",
       caption="Source: Iris") + 
  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), 
                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   
                  # change axis limits
  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles
  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + 
  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))