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:
<- as_tibble(banknote) df
1. Read the help file of the banknote
data set
to understand what it’s all about.
?banknote
2. Create a scatter plot of the left (x-axis) and right
(y-axis) measurements on the data set. Map the Status
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())