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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

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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")
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