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I have a data.table with two columns: one ID column and one value column. I want to split up the table by the ID column and run a function foo on the value column. This works fine as long as foo does not return NAs. In that case, I get an error that tells me that the types of the groups are not consistent. My assumption is that - since is.logical(NA) equals TRUE and is.numeric(NA) equals FALSE , data.table internally assumes that I want to combine logical values with numeric ones and returns an error. However, I find this behavior peculiar. Any comments on that? Do I miss something obvious here or is that indeed intended behavior? If so, a short explanation would be great. (Notice that I do know a work-around: just let foo2 return a complete improbable number and filter for that later. However, this seems bad coding). Here is the example: library(data.table) foo1 <- function(x) {if (mean(x) < 5) {return(1)} else {return(2)}} foo2 <- function(x) {if (mean(x) < 5) {return(1)} else {return(NA)}} DT <- data.table(ID=rep(c("A", "B"), each=5), value=1:10) DT[, foo1(value), by=ID] #Works perfectly ID V1 [1,] A 1 [2,] B 2 DT[, foo2(value), by=ID] #Throws error Error in `[.data.table`(DT, , foo2(value), by = ID) : columns of j don't evaluate to consistent types for each group: result for group 2 has column 1 type 'logical' but expecting type 'numeric'

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