# How to Use rcorr in R to Create a Correlation Matrix

You can use the rcorr function from the Hmisc package in R to create a matrix of correlation coefficients along with a matrix of p-values for variables in a data frame.

This function is particularly useful because the matrix of p-values allows you to see if the correlation coefficient between different pairwise combinations of variables is statistically significant.

This function uses the following basic syntax:

```library(Hmisc)

#create matrix of correlation coefficients and matrix of p-values
rcorr(as.matrix(df))```

The following example shows how to use the rcorr function in practice.

## Example: How to Use rcorr Function in R

Suppose we have the following data frame in R that contains information about various basketball players:

```#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),
steals=c(5, 6, 7, 7, 8, 5, 3, 4))

#view data frame
df

assists rebounds points steals
1       4       12     22      5
2       5       14     24      6
3       5       13     26      7
4       6        7     26      7
5       7        8     29      8
6       8        8     32      5
7       8        9     20      3
8      10       13     14      4
```

We can use the following syntax to create a matrix of correlation coefficients and a matrix of corresponding p-values for this data frame:

```library(Hmisc)

#create matrix of correlation coefficients and matrix of p-values
rcorr(as.matrix(df))

assists rebounds points steals
assists     1.00    -0.24  -0.33  -0.47
rebounds   -0.24     1.00  -0.52  -0.17
points     -0.33    -0.52   1.00   0.61
steals     -0.47    -0.17   0.61   1.00

n= 8

P
assists rebounds points steals
assists          0.5589   0.4253 0.2369
rebounds 0.5589           0.1844 0.6911
points   0.4253  0.1844          0.1047
steals   0.2369  0.6911   0.1047
```

The first matrix shows the correlation coefficient between each pairwise combination of variables in the data frame.

For example, we can see:

• The correlation coefficient between assists and rebounds is -0.24.
• The correlation coefficient between assists and points is -0.33.
• The correlation coefficient between assists and steals is -0.47.

And so on.

The second matrix shows the corresponding p-value for each correlation coefficient from the first matrix.

For example, we can see:

• The p-value for the correlation coefficient between assists and rebounds is 0.5589.
• The p-value for the correlation coefficient between assists and points is 0.4253.
• The p-value for the correlation coefficient between assists and steals is 0.2369.

And so on.

Note: By default, the rcorr function calculates the Pearson correlation coefficient, but you can specify type=’spearman’ if you would instead like to calculate the Spearman Rank correlation coefficient.