The “missing value where true/false needed” R error results from a failure to properly define the input into an “if statement” or “while statement” as either true or false. If you’re getting this error, you’re probably feeding a column of missing values or na value records into a data science procedure where the expected input is a logical vector (logical values – true / false ). This may be a single row or across the entire dataset. Either way, it will throw an error condition that will pause r code execution. It is an easy error to make, but also easy to correct.
When Does This Error Occur?
You will get this error message if the value you are putting into an “if statement” or “while statement” is not available (NA). When this occurs, these statements cannot process the data resulting in an error message. Here is a simple example of a code that produces this error message. Learning how to use the if statement properly, or different loops and functions in R programming can help you avoid these errors in the future.
# source: R Error: Missing value where true/false needed
> x = NA
> if(x) {x}
Error in if (x) { : missing value where TRUE/FALSE needed
As you can tell the variable “x” has a value of “NA” it has a result it triggered the error message.
But Why Does This Error Occur?

The reason why the “missing value where true/false needed” error message occurs is that you are passing an invalid value to “if statement” or “while statement.” These statements simply check to see if the argument is true or false. If the value it gets is not one of these, it will produce an error message. This is actually one of the simplest error messages to understand. Not only is there a message simple, but it gives meaningful information. This means the error message is useful in helping to understand what is going on.
How Do I Fix This Error?
The fix for this error is quite simple. All you need to do embed your “if statement” or “while statement” in another “if statement” that puts the value through the is.na() function to see if its value is “NA” or not. This will allow you to avoid this error message, as illustrated below.
# solution: Missing value where true/false needed
> x = NA
> if(is.na(x)) {x=FALSE} else {if(x) {x}}
Now that this simple little check, not only does it avoids the error message, but it provides you a way the correct the error when it occurs. Once the error has been detected, you can use it to define the value of the variable being checked who wrote it is no longer “NA” and causes no further problems. In our example above, because the value of “x” becomes “FALSE.” The result is that we have not just bypassed the error what about you corrected it.
# solution - bypassing error: Missing value where true/false needed
> x = TRUE
> if(is.na(x)) {x=FALSE} else {if(x) {x}}
[1] TRUE
The second example shows the results of the corrected version if x is true. This is because if x is true, it doesn’t have a value of “NA” and so the first “if statement” is false. This means that it gets passed on to the second “if statement” that detects the value of “x” as being “TRUE” and it prints the value of “x.” This is an easy error message to both understand and correct. Correcting it simply involves detecting “NA” value (missing data) before going through the “if statement.” This can be prevented by using isna to filter out bad case examples within a data frame to avoid blowing up a mathematical operation. Many base R package(s) include the narm option as well, which allows you to specify how the computation will handle missing value(s).
We hope our quick tutorial on fixing the “missing value where true/false needed” R error was helpful, and encourage you to check out more of our site for all of your R programming needs!