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I have the following data frame x <- read.table(text = " id1 id2 val1 val2 1 a x 1 9 2 a x 2 4 3 a y 3 5 4 a y 4 9 5 b x 1 7 6 b y 4 4 7 b x 3 9 8 b y 2 8", header = TRUE) I want to calculate the mean of val1 and val2 grouped by id1 and id2, and simultaneously count the number of rows for each id1-id2 combination. I can perform each calculation separately: # calculate mean aggregate(. ~ id1 + id2, data = x, FUN = mean) # count rows aggregate(. ~ id1 + id2, data = x, FUN = length) In order to do both calculations in one call, I tried do.call("rbind", aggregate(. ~ id1 + id2, data = x, FUN = function(x) data.frame(m = mean(x), n = length(x)))) However, I get a garbled output along with a warning: # m n # id1 1 2 # id2 1 1 # 1.5 2 # 2 2 # 3.5 2 # 3 2 # 6.5 2 # 8 2 # 7 2 # 6 2 # Warning message: # In rbind(id1 = c(1L, 2L, 1L, 2L), id2 = c(1L, 1L, 2L, 2L), val1 = list( : # number of columns of result is not a multiple of vector length (arg 1) I could use the plyr package, but my data set is quite large and plyr is very slow (almost unusable) when the size of the dataset grows. How can I use aggregate or other functions to perform several calculations in one call?

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