quick/elegant way to construct mean/variance summary table

aggregate, plyr, r, reshape2

Solution

I'm a bit puzzled. Does this not work:

mvtab2 <- ddply(d,.(f1,f2,f3),
            summarise,y.mean = mean(y),y.var = var(y))

This give me something like this:

   f1 f2  f3    y.mean       y.var
1   A  a   I 0.6502307 0.095379578
2   A  a  II 0.4876630 0.110796695
3   A  a III 0.3102926 0.202805677
4   A  b   I 0.3914084 0.058693103
5   A  b  II 0.5257355 0.218631264

Which is in the right form, but it looks like the values are different that what you specified.

Edit

Here's how to make your version with `numcolwise` work:

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

You forgot to pass the actual data to `numcolwise`. And then there's the little `ddply` trick that each piece is called `piece` internally. (Which Hadley points out in the comments shouldn't be relied upon as it may change in future versions of `plyr`.)

Problem

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!

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