Correlation of One Variable to All Others in R (Example)

In this R tutorial you’ll learn how to calculate the correlation of one data frame column to all the others.

The page contains this:

2) Example: Calculate Correlation of One Variable to All Others Using cor() Function

It’s time to dive into the example…

Creation of Exemplifying Data

Consider the following example data:

set.seed(6529489)                                 # Create example data
data <- data.frame(x1 = rnorm(100),
                   x2 = rnorm(100),
                   x3 = rnorm(100),
                   x4 = rnorm(100))
head(data)                                        # Print head of example data

table 1 data frame correlation one variable all others r

Table 1 shows that our example data is constructed of the four columns “x1”, “x2”, “x3”, and “x4”.

Example: Calculate Correlation of One Variable to All Others Using cor() Function

In this example, I’ll demonstrate how to get the Pearson correlation coefficient between a particular data frame variable with all the other variables in this data frame.

To achieve this, we can apply the cor and colnames functions as shown below:

data_cor <- cor(data[ , colnames(data) != "x1"],  # Calculate correlations
                data$x1)
data_cor                                          # Print correlation values

table 2 matrix correlation one variable all others r

As visualized in Table 2, the previous code has managed to construct a correlation matrix for only one of the columns in our data set.

Video, Further Resources & Summary

If you need more information on the R codes of this article, you might watch the following video on my YouTube channel. I demonstrate the R programming codes of this tutorial in the video:

In addition, you may read the related articles on Statistics Globe. A selection of articles about topics such as ggplot2, variables, lists, and graphics in R is shown below.

This tutorial has demonstrated how to compute the correlation of one data frame column to many others in R. If you have additional questions and/or comments, please let me know in the comments below.

6 Comments. Leave new

Vanessa
April 12, 2023 1:35 pm

Hey thank you for the code, it works fine. I need the significances, too, but cor.mtest doesnt work.Do you have any idea, how to calculate the coefficients and sgnificances?

Reply
- Cansu (Statistics Globe)
  April 13, 2023 8:01 am
  Hello Vanessa,
  
  You need to convert your data frame to a matrix to use the cor.mtest() function. First, be sure that you install the library:
  install.packages("corrplot") library(corrplot)
  Then convert your data to a matrix:
  data_matrix <- as.matrix(data)
  After that, you can run the function:
  cor.mtest(data_matrix)
  Regards,
  Cansu
  Reply
Katharina
August 28, 2023 11:50 am

How can I then plot a corrplot including significant marks (or only circles when significant) for the correlation of x1 with all the other variables (x2, x3 and x4)? resulting in a correlation “vector”

Reply
- Cansu (Statistics Globe)
  August 28, 2023 12:56 pm
  
  Hello Katharina,
  
  Would a plot like here in the second example help? See the title Use chart.Correlation(): Draw scatter plots.
  
  Best,
  Cansu
  
  Reply

Zach

September 7, 2023 2:29 pm

Is there a way to make this work with cor.test as well? Thanks!

Cansu (Statistics Globe)

September 8, 2023 12:09 pm

Hello Zach,

I understand that you are intention is to test the correlation between a variable and the others. You can achieve this by using a defined function in the apply() procedure. See the following script.

# Create a sample dataframe
df &lt;- data.frame(var1 = rnorm(100),
                 var2 = rnorm(100),
                 var3 = rnorm(100),
                 var4 = rnorm(100))
 
# Test correlation of var1 with all other variables
results &lt;- lapply(names(df)[-1], function(col_name) {
  test_result &lt;- cor.test(df$var1, df[[col_name]])
  list(column = col_name, 
       cor_coefficient = test_result$estimate, 
       p_value = test_result$p.value)
})
 
# Print the results
results