但这些应该有：

with(my.Data, aggregate(weight, list(age, sex), function(x) { c(MEAN=mean(x), SD=sd(x) )}))

with(my.Data, tapply(weight, list(age, sex), function(x) { c(mean(x) , sd(x) )} ))
# Not a nice structure but the results are in there

with(my.Data, aggregate(weight ~ age + sex, FUN =  function(x) c( SD = sd(x), MN= mean(x) ) ) )
    age    sex weight.SD weight.MN
1 adult female  3.535534 97.500000
2 young female        NA 80.000000
3 adult   male        NA 90.000000
4 young   male        NA 75.

要遵守的原则是让你的函数返回“一件事”，它可以是一个向量或一个列表，但不能是两个函数调用的连续调用。

如果你想使用data.table，它内置了with和by：

library(data.table)
myDT <- data.table(my.Data, key="animal")

myDT[, c("mean", "sd") := list(mean(weight), sd(weight)), by=list(age, sex)]

myDT[, list(mean_Aggr=sum(mean(weight)), sd_Aggr=sum(sd(weight))), by=list(age, sex)]
     age    sex mean_Aggr   sd_Aggr
1: adult female     96.0  3.6055513
2: young   male     76.5  2.1213203
3: adult   male     91.0  1.4142136
4: young female     84.5  0.7071068

• 我使用了稍微不同的数据集，以便没有sd的NA值 *

本着分享的精神，* 如果您熟悉SQL*，您也可以考虑“sqldf”包。（强调是因为您确实需要知道，例如，mean是avg，以便获得您想要的结果。）

sqldf("select age, sex, 
      avg(weight) `Wt.Mean`, 
      stdev(weight) `Wt.SD` 
      from `my.Data` 
      group by age, sex")
    age    sex Wt.Mean    Wt.SD
1 adult female    97.5 3.535534
2 adult   male    90.0 0.000000
3 young female    80.0 0.000000
4 young   male    75.0 0.000000

Reshape让你传递2个函数;而reshape 2则没有。

library(reshape)
my.Data = read.table(text = "
  animal    age     sex  weight
       1  adult  female     100
       2  young    male      75
       3  adult    male      90
       4  adult  female      95
       5  young  female      80
", sep = "", header = TRUE)
my.Data[,1]<- NULL
(a1<-  melt(my.Data, id=c("age", "sex"), measured=c("weight")))
(cast(a1, age + sex ~ variable, c(mean, sd), fill=NA))

#     age    sex weight_mean weight_sd
# 1 adult female        97.5  3.535534
# 2 adult   male        90.0        NA
# 3 young female        80.0        NA
# 4 young   male        75.0        NA

我欠@Ramnath的，他昨天才注意到这一点。

SSBtools包中的函数aggregate_multiple_fun是aggregate的 Package 器，它允许多个函数和多个变量的函数。在这种情况下，有两种可能性：

library(SSBtools)
my.Data = read.table(text = "
  animal    age     sex  weight
       1  adult  female     100
       2  young    male      75
       3  adult    male      90
       4  adult  female      95
       5  young  female      80
", sep = "", header = TRUE)

aggregate_multiple_fun(my.Data, my.Data[c("age", "sex")], 
                       vars = c(mean = "weight", sd = "weight"))

#     age    sex weight_mean weight_sd
# 1 adult female        97.5  3.535534
# 2 young female        80.0        NA
# 3 adult   male        90.0        NA
# 4 young   male        75.0        NA

aggregate_multiple_fun(my.Data, my.Data[c("age", "sex")], 
                       vars = "weight", fun = c("mean", "sd"))

#     age    sex mean       sd
# 1 adult female 97.5 3.535534
# 2 young female 80.0       NA
# 3 adult   male 90.0       NA
# 4 young   male 75.0       NA