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How can I apply different aggregate functions to different columns in R? The aggregate() function only offers one function argument to be passed:

V1  V2        V3
1   18.45022  62.24411694
2   90.34637  20.86505214
1   50.77358  27.30074987
2   52.95872  30.26189013
1   61.36935  26.90993530
2   49.31730  70.60387016
1   43.64142  87.64433517
2   36.19730  83.47232907
1   91.51753  0.03056485
... ...       ...

> aggregate(sample,by=sample["V1"],FUN=sum)
  V1 V1       V2       V3
1  1 10 578.5299 489.5307
2  2 20 575.2294 527.2222

How can I apply a different function to each column, i.e. aggregate V2 with the mean() function and V2 with the sum() function, without calling aggregate() multiple times?

barbaz
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    @mdsumner any other function with any other beautiful name is appreciated as well of course – barbaz May 22 '12 at 13:15

3 Answers3

10

For that task, I will use ddply in plyr

> library(plyr)
> ddply(sample, .(V1), summarize, V2 = sum(V2), V3 = mean(V3))
  V1       V2       V3
1  1 578.5299 48.95307
2  2 575.2294 52.72222
kohske
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  • I really like plyr's simplicity. Started using it after I learned about this package here on stackoverflow. – Alex May 22 '12 at 13:36
  • That's nice! Whats the magic with the "summarize" argument here? EDIT: got that, thats the actual function applied with additional arguments passed later on. – barbaz May 22 '12 at 13:45
5

...Or the function data.table in the package of the same name:

library(data.table)

myDT <- data.table(sample) # As mdsumner suggested, this is not a great name

myDT[, list(sumV2 = sum(V2), meanV3 = mean(V3)), by = V1]

#      V1    sumV2   meanV3
# [1,]  1 578.5299 48.95307
# [2,]  2 575.2294 52.72222
BenBarnes
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2

Let's call the dataframe x rather than sample which is already taken.

EDIT:

The by function provides a more direct route than split/apply/combine

by(x, list(x$V1), f)

:EDIT

lapply(split(x, x$V1), myfunkyfunctionthatdoesadifferentthingforeachcolumn)

Of course, that's not a separate function for each column but one can do both jobs. 

myfunkyfunctionthatdoesadifferentthingforeachcolumn = function(x) c(sum(x$V2), mean(x$V3))

Convenient ways to collate the result are possible such as this (but check out plyr package for a comprehensive solution, consider this motivation to learn something better). 

 matrix(unlist(lapply(split(x, x$V1), myfunkyfunctionthatdoesadifferentthingforeachcolumn)), ncol = 2, byrow = TRUE, dimnames = list(unique(x$V1), c("sum", "mean")))
mdsumner
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  • Nice to know! I do prefer to get around that extra work of implementing the intermediate function though, thus the package that kohske suggested does exactly what I was looking for :) – barbaz May 22 '12 at 14:06