I agree. It's pretty important. Missing values can be coded as funny things like minus infinity in some languages. So then, if you want to assign a subset of a vector to a new group (say the number of people who have had a an event e.g. "heart attack") and you used the argument x <= 1, "missing values" would be incorrectly categorized as "heart attack". You can see how this could really affect your analysis. With R, the usefulness of NA is that if you're careful when you import data, this never happens.
Of course if you're always careful, if your NA values in other languages are stored as numbers, you can avoid this error. But it's made easy by R's approach to NA.
Of course if you're always careful, if your NA values in other languages are stored as numbers, you can avoid this error. But it's made easy by R's approach to NA.