Autoplot of PCA in R (Example)

In this tutorial, you’ll learn how to create a scatterplot and a biplot using the autoplot() function for Principal Component Analysis (PCA) results in the R programming language.

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This page was created in collaboration with Paula Villasante Soriano and Cansu Kebabci. Please have a look at Paula’s and Cansu’s author pages to get further information about their academic backgrounds and the other articles they have written for Statistics Globe.

Let’s start right away.

Load Libraries and Create Sample Data Set

Before we start, installing the libraries, ggplot2 and the ggfortify, is convenient.

install.packages("ggplot2")
install.packages("ggfortify")

If you have already installed these libraries in the past, just ignore this first step. Then skip to the following:

library(ggplot2)
library(ggfortify)

Next, we will call our sample data, which is a built-in dataset called iris in R.

data(iris)
head(iris)
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1          5.1         3.5          1.4         0.2  setosa
# 2          4.9         3.0          1.4         0.2  setosa
# 3          4.7         3.2          1.3         0.2  setosa
# 4          4.6         3.1          1.5         0.2  setosa
# 5          5.0         3.6          1.4         0.2  setosa
# 6          5.4         3.9          1.7         0.4  setosa

Now, let’s perform our PCA using the sample data frame.

Perform PCA

In order to perform a PCA in R, we will choose all the columns except for the Species column since it is categorical. Hence, the first step is subsetting the dataset. For PCA designed for categorical variables, see our tutorial: Can PCA be Used for Categorical Variables?.

data <- iris[,1:4]
 
iris_pca <- prcomp(data, 
                 scale=TRUE)
summary(iris_pca)
#Importance of components:
#                           PC1    PC2     PC3     PC4
# Standard deviation     1.7084 0.9560 0.38309 0.14393
# Proportion of Variance 0.7296 0.2285 0.03669 0.00518
# Cumulative Proportion  0.7296 0.9581 0.99482 1.00000

Now, we can visualize our PCA results via the autoplot() function.

Scatterplot using autoplot()

All we need to visualize our PCA on a scatterplot using the autoplot() function is to input the iris_pca object to the function. If the users want to add a title and color the data points by group, they should also use the data, colour, and main arguments.

autoplot(iris_pca,
         data=iris,
         main = "Scatterplot",
         colour = 'Species')

As seen, the axis labels specifying the principal components and explained variances (compare with the summary output of iris_pca) and the legend are added by default.

Biplot using autoplot()

We should use some additional function arguments to plot a biplot via autoplot(). First and foremost, loadings should be set to TRUE to make sure that one of the main visual components of biplots, loading vectors, are added. Then the rest is about how to color and label those vectors.

autoplot(iris_pca,
         data = iris,
         colour = 'Species',
         main = "Biplot", 
         loadings = TRUE,
         loadings.label=TRUE,
         loadings.colour = "black",
         loadings.label.colour="black",
         loadings.label.repel=TRUE)

We hope you found this tutorial helpful. See you at the next one!

Video, Further Resources & Summary

Do you need more explanations on how to create an autoplot of PCA in R? Then check the following YouTube video of the Statistics Globe YouTube channel.

The YouTube video will be added soon.

You could also have a look at some of the other tutorials available on Statistics Globe:

• What is PCA?
• Can PCA be Used for Categorical Variables?
• Principal Component Analysis in R
• Scatterplot of PCA in R
• Biplot of PCA in R
• Biplot for PCA Explained – How to Interpret

This post has shown how to make an autoplot of a PCA in R. In case you have any questions, you can leave a comment below.