Missing more?

(how to know what missing values you will drop in a dataset)

When dealing with missing values in a dataset, it is important to carefully consider which missing values you will drop and which you will keep. The decision of which missing values to drop will depend on the specific context and goals of your analysis.

One approach is to drop rows that contain missing values in certain columns. 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))

In this example, the filter() function removes rows that contain missing values in the "x" and "y" columns.

Another approach is to drop rows that contain missing values in any column. This can be done using the complete.cases() function, as shown in the following code:

mydata %>%

filter(complete.cases(.))

In this example, the complete.cases() function returns a logical vector that indicates which rows of the data frame are complete (i.e., contain no missing values), and the filter() function removes the rows that are not complete.