In SQL, the default is to ignore NULL (equivalent to NA in R).
However, it can be dangerous to fail to verify how much data was actually used
in an aggregation, so the logic behind the default na.rm setting may be one of
encouraging the user to take responsibility for missing data.
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Sent from my phone. Please excuse my brevity.
"Doran, Harold" <hdo...@air.org> wrote:
This is just posed out of curiosity, (not as a criticism per se). But what is
the functional role of the argument na.rm inside the mean() function? If there
are missing values, mean() will always return an NA as in the example below.
But, is there ever a purpose in computing a mean only to receive NA as a result?
In 10 years of using R, I have always used mean() in order to get a result,
which is the opposite of its default behavior (when there are NAs). Can anyone
suggest a reason why it is in fact desired to get NA as a result of computing
mean()?
> x <- rnorm(100)
> x[1] <- NA
> mean(x)
[1] NA
> mean(x, na.rm=TRUE)
[1] 0.08136736
If the reason is to alert the user that the vector has missing values, I
suppose I could buy that. But, I think other checks are better
Harold
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