quick/elegant way to construct mean/variance summary table

Question

I can achieve this task, but I feel like there must be a "best" (slickest, most compact, clearest-code, fastest?) way of doing it and have not figured it out so far ...

For a specified set of categorical factors I want to construct a table of means and variances by group.

generate data:

set.seed(1001)
d <- expand.grid(f1=LETTERS[1:3],f2=letters[1:3],
                 f3=factor(as.character(as.roman(1:3))),rep=1:4)
d$y <- runif(nrow(d))
d$z <- rnorm(nrow(d))

desired output:

  f1 f2  f3    y.mean      y.var
1  A  a   I 0.6502307 0.09537958
2  A  a  II 0.4876630 0.11079670
3  A  a III 0.3102926 0.20280568
4  A  b   I 0.3914084 0.05869310
5  A  b  II 0.5257355 0.21863126
6  A  b III 0.3356860 0.07943314
... etc. ...

using aggregate/merge:

library(reshape)
m1 <- aggregate(y~f1*f2*f3,data=d,FUN=mean)
m2 <- aggregate(y~f1*f2*f3,data=d,FUN=var)
mvtab <- merge(rename(m1,c(y="y.mean")),
      rename(m2,c(y="y.var")))

using ddply/summarise (possibly best but haven't been able to make it work):

mvtab2 <- ddply(subset(d,select=-c(z,rep)),
                .(f1,f2,f3),
                summarise,numcolwise(mean),numcolwise(var))

results in

Error in output[[var]][rng] <- df[[var]] : 
  incompatible types (from closure to logical) in subassignment type fix

using melt/cast (maybe best?)

mvtab3 <- cast(melt(subset(d,select=-c(z,rep)),
          id.vars=1:3),
     ...~.,fun.aggregate=c(mean,var))
## now have to drop "variable"
mvtab3 <- subset(mvtab3,select=-variable)
## also should rename response variables

Won't (?) work in reshape2. Explaining ...~. to someone could be tricky!

Answer 1

Here is a solution using data.table

library(data.table)
d2 = data.table(d)
ans = d2[,list(avg_y = mean(y), var_y = var(y)), 'f1, f2, f3']