Visualizing variance explained
With all the calculations under your belt, it always comes in handy to represent our data visually. You will now create a column plot showing variance explained by principal component.
The variable_explained
vector you created in the last exercise is available, and the ggplot()
theme_()
is set to classic.
This exercise is part of the course
Feature Engineering in R
Exercise instructions
- Use the information in the PCA tibble to create a column plot of variance explained.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
PCA = tibble(PC = 1:length(sdev), var_explained = var_explained,
cumulative = cumsum(var_explained))
# Use the information in the PCA tibble to create a column plot of variance explained
PCA %>% ggplot(aes(x = ___, y = ___)) +
geom_col(fill = "steelblue") +
xlab("Principal components") +
ylab("Variance explained")