Covariance is the simplest and widely used measure of correlation. We can find the covariance between two variables in R using the cov function. Covariance measures the linear relationship between two variables in a dataset. A positive covariance value indicates a positive linear relationship between the variables, and a negative value represents the negative linear relationship. In a previous post, I have explained calculating covariance in a spreadsheet.
Steps to calculate Covariance in R
1. To illustrate how to calculate covariance in R. I use in-built women data. This data consists of two variables i.e. Average Heights and Weights of American Women. Load the inbuilt data using the following command
2. Let’s find the covariance between the heights and weights in the dataset
> cov(women$height,women$weight)  69
The covariance result is 69. The result is a positive number, which denotes a positive relationship between the two variables. Remember the order you use in cov command doesn’t matter cov(women$height,women$weight) and cov(women$weight,women$height) both these will give the same result.
Read also: How to calculate descriptive statistics using R