What I am missing?

 Missing values are a common problem in data analysis, as they can lead to inaccurate or incomplete results. In R, missing values are represented by the special value NA, which stands for "not available".

There are a few different ways to deal with missing values in R. One approach is to simply remove the rows of the data frame that contain missing values. This can be done using the filter() function from the "dplyr" package, as shown in the following code:

mydata %>%

filter(!is.na(x)) %>%

filter(!is.na(y))

Another approach is to fill in the missing values with a default value. This can be done using the fill() function from the "tidyr" package, as shown in the following code:

mydata %>%

fill(x, y)

In this example, the fill() function fills in missing values in the "x" and "y" columns with the default value of 0.