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

> data("women")

2. Let’s find the covariance between the heights and weights in the dataset

> cov(women$height,women$weight) [1] 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

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