Creating a Scree Plot in Base R

[This article was first published on Steve's Data Tips and Tricks, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.


A scree plot is a line plot that shows the eigenvalues or variance explained by each principal component (PC) in a Principal Component Analysis (PCA). It is a useful tool for determining the number of PCs to retain in a PCA model.

In this blog post, we will show you how to create a scree plot in base R. We will use the iris dataset as an example.

Step 1: Load the dataset and prepare the data

# Drop the non-numerical column
df <- iris[, -5]

E Step 2: Perform Principal Component Analysis

# Perform PCA on the iris dataset
pca <- prcomp(df, scale = TRUE)

E Step 3: Create the scree plot

# Extract the eigenvalues from the PCA object
eigenvalues <- pca$sdev^2

# Create a scree plot
plot(eigenvalues, type = "b",
     xlab = "Principal Component",
     ylab = "Eigenvalue")

# Add a line at y = 1 to indicate the elbow
abline(v = 2, col = "red")

# Percentage of variance explained
plot(eigenvalues/sum(eigenvalues), type = "b",
     xlab = "Principal Component",
     ylab = "Percentage of Variance Explained")
abline(v = 2, col = "red")


The scree plot shows that the first two principal components explain the most variance in the data. The third and fourth principal components explain much less variance.

Based on the scree plot, we can conclude that the first two principal components are sufficient for capturing the most important information in the data.

Here are the eigenvalues and the percentage explained

[1] 2.91849782 0.91403047 0.14675688 0.02071484
[1] 0.729624454 0.228507618 0.036689219 0.005178709

Try it yourself

Try creating a scree plot for another dataset of your choice. You can use the same steps outlined above.

Here are some additional tips for creating scree plots:

  • If you are using a dataset with a large number of variables, you may want to consider scaling the data before performing PCA. This will ensure that all of the variables are on the same scale and that no one variable has undue influence on the results.
  • You can also add a line to the scree plot at y = 1 to indicate the elbow. The elbow is the point where the scree plot begins to level off. This is often used as a heuristic for determining the number of PCs to retain.
  • Finally, keep in mind that the interpretation of a scree plot is subjective. There is no single rule for determining the number of PCs to retain. The best approach is to consider the scree plot in conjunction with other factors, such as your research goals and the specific dataset you are using.
To leave a comment for the author, please follow the link and comment on their blog: Steve's Data Tips and Tricks. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)