A correlation matrix is a square table that shows the correlation coefficients between variables in a dataset.

It offers a quick way to understand the strength of the linear relationships that exist between variables in a dataset.

There are four common ways to create a correlation matrix in R:

**Method 1: The cor Function (For getting simple matrix of correlation coefficients)**

**cor(df)**

**Method 2: The rcorr Function (For getting p-values of correlation coefficients)**

**library(Hmisc)
rcorr(as.matrix(df))**

**Method 3: The corrplot Function (For visualizing correlation matrix)**

**library(corrplot)
corrplot(cor(df))
**

**Method 4: The ggcorrplot Function (For visualizing correlation matrix)**

**library(ggcorrplot)
ggcorrplot(cor(df))**

The following examples show how to use each method with the following data frame in R:

**#create data frame
df <- data.frame(assists=c(4, 5, 5, 6, 7, 8, 8, 10),
rebounds=c(12, 14, 13, 7, 8, 8, 9, 13),
points=c(22, 24, 26, 26, 29, 32, 20, 14))
#view data frame
df
assists rebounds points
1 4 12 22
2 5 14 24
3 5 13 26
4 6 7 26
5 7 8 29
6 8 8 32
7 8 9 20
8 10 13 14
**

**Example 1: The cor Function**

We can use the **cor()** function from base R to create a correlation matrix that shows the correlation coefficients between each variable in our data frame:

**#create correlation matrix
cor(df)
assists rebounds points
assists 1.0000000 -0.2448608 -0.3295730
rebounds -0.2448608 1.0000000 -0.5220917
points -0.3295730 -0.5220917 1.0000000
**

The correlation coefficients along the diagonal of the table are all equal to 1 because each variable is perfectly correlated with itself.

All of the other correlation coefficients indicate the correlation between different pairwise combinations of variables. For example:

- The correlation coefficient between assists and rebounds is
**-0.245**. - The correlation coefficient between assists and points is
**-0.330**. - The correlation coefficient between rebounds and points is
**-0.522**.

**Example 2: The rcorr Function**

We can use the **rcorr()** function from the **Hmisc** package in R to create a correlation matrix that shows the correlation coefficients between each variable in our data frame:

**library(Hmisc)
#create matrix of correlation coefficients and p-values
rcorr(as.matrix(df))
assists rebounds points
assists 1.00 -0.24 -0.33
rebounds -0.24 1.00 -0.52
points -0.33 -0.52 1.00
n= 8
P
assists rebounds points
assists 0.5589 0.4253
rebounds 0.5589 0.1844
points 0.4253 0.1844 **

The first matrix shows the correlation coefficients between the variables and the second matrix shows the corresponding p-values.

For example, the correlation coefficient between assists and rebounds is **-0.24** and the p-value for this correlation coefficient is **0.5589**.

This tells us that the correlation between the two variables is negative but it’s not a statistically significant correlation since the p-value is not less than .05.

**Example 3: The corrplot Function**

We can use the **corrplot()** function from the **corrplot** package in R to visual the correlation matrix:

**library(corrplot)
#visualize correlation matrix
corrplot(cor(df))
**

The color and size of the circles in the correlation matrix help us visualization the correlations between each variable.

For example, the circle where the assists and rebounds variables intersect is small and light red, which tells us that the correlation is low and negative.

**Example 4: The corrplot Function**

We can use the **ggcorrplot()** function from the **ggcorrplot** package in R to visualize the correlation matrix:

**library(ggcorrplot)
#visualize correlation matrix
ggcorrplot(cor(df))
**

The color of the squares in the correlation matrix help us visualization the correlations between each variable.

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Calculate Spearman Rank Correlation in R

How to Calculate Partial Correlation in R

How to Calculate Rolling Correlation in R