In this practical, we will apply model-based clustering on a data set of bank note measurements.
We use the following packages:
library(mclust) library(tidyverse) library(patchwork)
The data is built into the
mclust package and can be
loaded as a
tibble by running the following code:
1. Read the help file of the
banknote data set
to understand what it’s all about.
2. Create a scatter plot of the left (x-axis) and right
(y-axis) measurements on the data set. Map the
column to colour. Jitter the points to avoid overplotting. Are the
classes easy to distinguish based on these features?
%>% df ggplot(aes(x = Left, y = Right, colour = Status)) + geom_point(position = position_jitter())