You can use one of the following two methods to remove columns from a data frame in R that contain NA values:

Method 1: Use Base R

df[ , colSums(is.na(df))==0]

Method 2: Use dplyr

library(dplyr)

df %>% select_if(~ !any(is.na(.)))

Both methods produce the same result.

The following examples show how to use each method in practice with the following data frame:

#create data frame
df <- data.frame(team=c('A', 'B', 'C', 'D', 'E'),
                 points=c(99, NA, NA, 88, 95),
                 assists=c(33, 28, 31, 39, 34),
                 rebounds=c(30, 28, 24, 24, NA))

#view data frame
df

  team points assists rebounds
1    A     99      33       30
2    B     NA      28       28
3    C     NA      31       24
4    D     88      39       24
5    E     95      34       NA

Example 1: Remove Columns with NA Values Using Base R

The following code shows how to remove columns with NA values using functions from base R:

#define new data frame
new_df <- df[ , colSums(is.na(df))==0]

#view new data frame
new_df

  team assists
1    A      33
2    B      28
3    C      31
4    D      39
5    E      34

Notice that the two columns with NA values (points and rebounds) have both been removed from the data frame.

Example 2: Remove Columns with NA Values Using dplyr

The following code shows how to remove columns with NA values using functions from the package:

library(dplyr)

#define new data frame
new_df <- df %>% select_if(~ !any(is.na(.)))

#view new data frame
new_df

  team assists
1    A      33
2    B      28
3    C      31
4    D      39
5    E      34

Once again, the two columns with NA values (points and rebounds) have both been removed from the data frame.