# why is correlation na in r

#### Correlation NA in R

In R, the `cor`

function is used to calculate the correlation coefficient between two variables. If there are missing values in the data, the `cor`

function has an argument called `use`

which specifies how to handle missing values. By default, it is set to `"everything"`

, which means that if there are any missing values in the data, the result will be `NA`

[3].

When the `use`

argument is set to `"everything"`

, the `cor`

function will return `NA`

if there are any missing values in the data. This is because the correlation coefficient cannot be calculated when there are missing values, and R returns `NA`

to indicate that the result is not available [3].

To handle missing values in the data when calculating the correlation coefficient, the `use`

argument can be set to `"complete.obs"`

. This will cause the `cor`

function to calculate the correlation coefficient using only complete pairs of observations, excluding any pairs that contain missing values.

In summary, when using the `cor`

function in R, it's important to consider how missing values should be handled by setting the `use`

argument appropriately to ensure the desired behavior for the correlation calculation.