问题
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?
回答1:
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
回答2:
...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
回答3:
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")))
来源:https://stackoverflow.com/questions/10702708/how-can-i-apply-different-aggregate-functions-to-different-columns-in-r