In R, if the evaluation of a condition in an if or while statement results in NA, you will raise the error: missing value where TRUE/FALSE needed.

You can solve this error by using is.na(x) instead of x == NA.

This tutorial will go through the error in detail and how to solve it with code examples.


Example #1: if missing value where TRUE/FALSE needed with NA

Let’s look at an example where we have a vector that contains a mix of numbers and NA. We want to append the numbers to a new vector and exclude the NAs.

x <- c(3, 5, NA, 7, NA, 10, NA, 20)
y <- vector()
for (i in 1:length(x)){
   if(x[i] != NA) {
       y <-(x[i])
    }
}
y

In the above code, we use x[i] != NA as the condition in an if statement to append to the new vector if the value is not NA. Let’s run the code to see what happens:

Error in if (x[i] != NA) { : missing value where TRUE/FALSE needed

The R interpreter throws the error because an if conditional must have either a TRUE or FALSE result not NA. The simplest way to reproduce the error is:

if(NA){}
Error in if (NA) { : missing value where TRUE/FALSE needed

Solution

We can solve this error by using is.na(value), which will return either TRUE if the value is NA or FALSE if it is not NA. Let’s look at the revised code:

x <- c(3, 5, NA, 7, NA, 10, NA, 20)
y <- vector()
for (i in 1:length(x)){
   if(!is.na(x[i])) {
       y <- c(y, x[i])
    }
}

y

The condition checks for NOT NA by using the exclamation mark. Let’s run the code to see the result:

[1]  3  5  7 10 20

We successfully appended the non-NA values to the new vector y.

What is NA in R?

A missing value is an unknown value. In R, missing values are represented by the NA symbol. We can manage NA values using NA functions such as is.na() and na.omit.

Summary

Congratulations on reading to the end of this tutorial! The error: missing value where TRUE/FALSE needed occurs when the evaluation of a condition results in a NA instead of either TRUE or FALSE. Solve the error by using is.na() or use another that condition that evaluates to TRUE or FALSE.


For further reading on R related errors, go to the articles: 

Go to the online courses page on R to learn more about coding in R for data science and machine learning.

Have fun and happy researching!