aggregate methods treat missing values (NA) differently

Question

Here\'s a simple data frame with a missing value:

M = data.frame( Name = c(\'name\', \'name\'), Col1 = c(NA, 1) , Col2 = c(1, 1))
#   Name Col1 Col2
# 1 name          


        

Answer 1

If you want the formula version to be equivalent try this:

M = data.frame( Name = rep('name',5), Col1 = c(NA,rep(1,4)) , Col2 = rep(1,5))
aggregate(. ~ Name, M, function(x) sum(x, na.rm=TRUE), na.action = na.pass)

Answer 2

Good question, but in my opinion, this shouldn't have caused a major debugging headache because it is documented quite clearly in multiple places in the manual page for aggregate.

First, in the usage section:

## S3 method for class 'formula'
aggregate(formula, data, FUN, ...,
          subset, na.action = na.omit)

Later, in the description:

na.action: a function which indicates what should happen when the data contain NA values. The default is to ignore missing values in the given variables.

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I can't answer why the formula mode was written differently---that's something the function authors would have to answer---but using the above information, you can probably use the following:

aggregate(.~Name, M, FUN=sum, na.rm=TRUE, na.action=NULL)
#   Name Col1 Col2
# 1 name    1    2