You can use the is.na() function in R to check for missing values in vectors and data frames.

#check if each individual value is NA
is.na(x)

#count total NA values
sum(is.na(x))

#identify positions of NA values
which(is.na(x))

The following examples show how to use this function in practice.

Example 1: Use is.na() with Vectors

The following code shows how to use the is.na() function to check for missing values in a vector:

#define vector with some missing values
x <- c(3, 5, 5, NA, 7, NA, 12, 16)

#check if each individual value is NA
is.na(x)

[1] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE

#count total NA values
sum(is.na(x))

[1] 2

#identify positions of NA values
which(is.na(x))

[1] 4 6

From the output we can see:

• There are 2 missing values in the vector.
• The missing values are located in position 4 and 6.

Example 2: Use is.na() with Data Frames

The following code shows how to use the is.na() function to check for missing values in a data frame:

#create data frame
df <- data.frame(var1=c(1, 3, 3, 4, 5),
                 var2=c(7, NA, NA, 3, 2),
                 var3=c(3, 3, 6, NA, 8),
                 var4=c(NA, 1, 2, 8, 9))

#view data frame
df

  var1 var2 var3 var4
1    1    7    3   NA
2    3   NA    3    1
3    3   NA    6    2
4    4    3   NA    8
5    5    2    8    9

#find total NA values in data frame
sum(is.na(df))

[1] 4

#find total NA values by column
sapply(df, function(x) sum(is.na(x)))

var1 var2 var3 var4 
   0    2    1    1 

From the output we can see that there are 4 total NA values in the data frame.

We can also see:

• There are 0 NA values in the ‘var1’ column.
• There are 2 NA values in the ‘var2’ column.
• There are 1 NA values in the ‘var3’ column.
• There are 1 NA values in the ‘var4’ column.

The following tutorials explain other useful functions that can be used to handle missing values in R.

How to Use is.null in R