Is considered to be missing.

So, but the reverse is not true.

So an NA value is not necessarily, an NAN value.

I've got a few different types of missing values listed here.

So, here I created a vector x which is 1,2, NA, 10, and 3.

So, now, this is a numeric vector.

And the NA value in here's going to be a numeric missing value.

So when I call is.na on x, what it returns is a, is a logical vector.

And the logical vector indicates whether each element of the vector x

is missing or not.

And so, there's only one missing element in this vector, and so

that's the third element.

So you can see that the, that the logical vector that's returned.

The first two are false, the third is true, and the fourth and

the fifth are false.

So the, the, the element that's true indicated where the missing value is.

If I call is.NaN on this vector,

you'll see that vector that's returned is all false.

Because there aren't any NaN values, or

their aren't any MAN values in this vector so everything's false.

Of course, if I create a vector that has an end, a NAN value and an, and

an NA value in it.

You'll see that is.na returns true for both of them.

But is.nan only returns true for the for the value that's actually NAN.