You can use the following methods to count the number of NA values in each column of a data frame in R:

Method 1: Count NA Values in Each Column Using Base R

sapply(df, function(x) sum(is.na(x)))

Method 2: Count NA Values in Each Column Using dplyr

library(dplyr)

df %>% summarise(across(everything(), ~ sum(is.na(.))))

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

#create data frame
df <- data.frame(team=c('A', 'B', 'C', 'D', 'E'),
                 points=c(99, 90, 86, 88, NA),
                 assists=c(33, NA, NA, 39, 34),
                 rebounds=c(30, 28, 24, 24, 28))

#view data frame
df

  team points assists rebounds
1    A     99      33       30
2    B     90      NA       28
3    C     86      NA       24
4    D     88      39       24
5    E     NA      34       28

Example 1: Count NA Values in Each Column Using Base R

The following code shows how to count the number of NA values in each column using the sapply() function from base R:

#count NA values in each column
sapply(df, function(x) sum(is.na(x)))

    team   points  assists rebounds 
       0        1        2        0 

From the output we can see:

• The team column has 0 NA values.
• The points column has 1 NA value.
• The assists column has 2 NA values.
• The rebounds column has 0 NA values.

Note: The sapply() function can be used to apply a function to each column in the data frame. In this example, we apply a function that counts the total number of elements equal to NA.

Example 2: Count NA Values in Each Column Using dplyr

The following code shows how to count the number of NA values in each column using the summarise() function from the package:

#count NA values in each column
sapply(df, function(x) sum(is.na(x)))

    team   points  assists rebounds 
       0        1        2        0 

From the output we can see:

• The team column has 0 NA values.
• The points column has 1 NA value.
• The assists column has 2 NA values.
• The rebounds column has 0 NA values.

These results match the ones from the previous example.

Note: The dplyr method tends to be faster than the base R method when working with extremely large data frames.