Both measure the same thing, nearly the same to be more accurate. Covariance measures how the two variables are related. A positive value indicates a positive linear relationship. In R software, it can be calculated by cov(variable x, variable y).

Correlation coefficient is covariance divided by the product of the standard deviations of the two variables. It is a normalized measurement of the relationship between two variables.If it is near to one means that there is a positive linear relationship. If '-1' means that negative linear relationship. If zero, it means no linear relationship. In R software, it can be calculated simply by cor(variable x, variable y)

Correlation coefficient is covariance divided by the product of the standard deviations of the two variables. It is a normalized measurement of the relationship between two variables.If it is near to one means that there is a positive linear relationship. If '-1' means that negative linear relationship. If zero, it means no linear relationship. In R software, it can be calculated simply by cor(variable x, variable y)

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