# Remove Highly Correlated Variables from Data Frame in R (Example)

In this R tutorial you’ll learn how to delete columns with a very high correlation.

The article will contain one example for the removal of columns with a high correlation. To be more specific, the post is structured as follows:

Please note: This tutorial does not discuss whether you SHOULD exclude highly correlated variables from your data. Please ensure that it is theoretically justified to remove these variables before continuing the removal in practice.

However, here’s the step-by-step process!

## Construction of Exemplifying Data

The first step is to define some data that we can use in the examples later on:

```set.seed(356947) # Create example data x1 <- rnorm(100) x2 <- x1 + rnorm(100, 0, 0.01) x3 <- x1 + x2 + rnorm(100) data <- data.frame(x1, x2, x3) head(data) # Head of example data``` Table 1 shows that our example data consists of three numerical columns called “x1”, “x2”, and “x3”.

## Example: Delete Highly Correlated Variables Using cor(), upper.tri(), apply() & any() Functions

In this section, I’ll illustrate how to remove all highly correlated columns from a data frame.

For this, we first have to calculate a correlation matrix of our data:

```cor_matrix <- cor(data) # Correlation matrix cor_matrix``` As shown in Table 2, we have created a correlation matrix of our example data frame by running the previous R code. Note that the correlations are rounded, i.e. the correlation of x1 and x2 is shown as 1 even though it is slightly below 1 in reality.

In the next step, we have to modify our correlation matrix as shown below:

```cor_matrix_rm <- cor_matrix # Modify correlation matrix cor_matrix_rm[upper.tri(cor_matrix_rm)] <- 0 diag(cor_matrix_rm) <- 0 cor_matrix_rm``` In Table 3 it is shown that we have created an updated version of our correlation matrix where the diagonal and the upper triangle have been set to zero.

Now, we can use this updated correlation matrix to remove all variables with a correlation above a certain threshold (i.e. > 0.99):

```data_new <- data[ , !apply(cor_matrix_rm, # Remove highly correlated variables 2, function(x) any(x > 0.99))] head(data_new) # Print updated data frame``` As shown in Table 4, we have created a subset of our original data frame where highly correlated variables have been excluded.

In the present example, the variable x1 was removed, because it has a correlation larger than 0.99 with the variable x2.

## Video & Further Resources

If you need more explanations on the R codes of this article, you might want to have a look at the following video on my YouTube channel. I’m explaining the content of this tutorial in the video:

Besides that, you may read the related articles on my website:

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• January 20, 2022 10:02 am

Nice and simple approach 🙂
you may consider any(abs(x) > 0.99)
Thanks