# Correlation Matrix in R (3 Examples)

In this tutorial youâ€™ll learn how to **compute and plot a correlation matrix** in the R programming language.

The article consists of three examples for the creation of correlation matrices. More precisely, the article looks as follows:

So letâ€™s dive right into the programming part.

## Example Data

Iâ€™ll use the data below as basement for this R tutorial:

set.seed(28762) # Create example data x1 <- rnorm(1000) x2 <- rnorm(1000) + 0.2 * x1 x3 <- runif(1000) + 0.1 * x1 - 0.2 * x2 data <- data.frame(x1, x2, x3) head(data) # Print example data # x1 x2 x3 # 1 -0.18569232 -0.9497532 1.0033275 # 2 0.28981164 -0.9131415 0.7393190 # 3 -1.76015009 -2.1335438 1.1012058 # 4 0.01030804 -0.4538802 0.3128903 # 5 0.43926986 -0.2940416 0.1996600 # 6 -2.25920975 -0.4394634 0.1017577 |

set.seed(28762) # Create example data x1 <- rnorm(1000) x2 <- rnorm(1000) + 0.2 * x1 x3 <- runif(1000) + 0.1 * x1 - 0.2 * x2 data <- data.frame(x1, x2, x3) head(data) # Print example data # x1 x2 x3 # 1 -0.18569232 -0.9497532 1.0033275 # 2 0.28981164 -0.9131415 0.7393190 # 3 -1.76015009 -2.1335438 1.1012058 # 4 0.01030804 -0.4538802 0.3128903 # 5 0.43926986 -0.2940416 0.1996600 # 6 -2.25920975 -0.4394634 0.1017577

As you can see based on the previous output of the RStudio console, our example data contains three numeric variables.

## Example 1: Compute Correlations Between Variables

Example 1 explains how to calculate the correlation values between each pair of columns of a data set.

cor(data) # Correlation matrix of example data # x1 x2 x3 # x1 1.0000000 0.2225584 0.1625305 # x2 0.2225584 1.0000000 -0.5150919 # x3 0.1625305 -0.5150919 1.0000000 |

cor(data) # Correlation matrix of example data # x1 x2 x3 # x1 1.0000000 0.2225584 0.1625305 # x2 0.2225584 1.0000000 -0.5150919 # x3 0.1625305 -0.5150919 1.0000000

As you can see based on the previous output of the RStudio console, we created a matrix consisting of the correlations of each pair of variables. For instance, the correlation between x1 and x2 is 0.2225584.

## Example 2: Plot Correlation Matrix with corrplot Package

The R syntax below explains how to draw a correlation table in a plot with the corrplot package.

First, we need to install and load the corrplot package, if we want to use the corresponding functions:

install.packages("corrplot") # Install corrplot package library("corrplot") # Load corrplot |

install.packages("corrplot") # Install corrplot package library("corrplot") # Load corrplot

Now, we can use the corrplot function as shown below:

corrplot(cor(data), method = "circle") # Apply corrplot function |

corrplot(cor(data), method = "circle") # Apply corrplot function

As visualized in Figure 1, the previous R programming syntax created a correlation matrix graphic indicating the size of the correlation with colored circles.

## Example 3: Plot Correlation Matrix with ggcorrplot Package

This Example explains how to plot a correlation matrix with the ggcorrplot package. The ggcorrplot package is part of the ggplot2 family.

install.packages("ggcorrplot") # Install ggcorrplot package library("ggcorrplot") # Load ggcorrplot |

install.packages("ggcorrplot") # Install ggcorrplot package library("ggcorrplot") # Load ggcorrplot

Now, we can use the ggcorrplot to create a correlation graph in the style of the ggplot2 package.

ggcorrplot(cor(data)) # Apply ggcorrplot function |

ggcorrplot(cor(data)) # Apply ggcorrplot function

As revealed in Figure 2, we created a correlation matrix plot with the previous R programming syntax.

## Video & Further Resources

Do you want to learn more about the computation and plotting of correlations? Then you may want to have a look at the following video of my YouTube channel. In the video, I illustrate the R codes of the present article:

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Furthermore, you may have a look at the other posts of my website. A selection of other articles is shown here.

This tutorial explained how to **get a matrix table containing Pearson correlation coefficients** in the R programming language. Please let me know in the comments section, in case you have additional questions. In addition, please subscribe to my email newsletter to get updates on the newest tutorials.

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