Finding means and medians across data frames in r

I have several data frames, a b c d, each with the same column names. I want to find the mean and开发者_开发问答 median of those data frames. In other words, construct new mean and median data frames that are the same size as a, b, etc.

I could use a couple of for loops, but I bet there is a slick way of doing this using the R built-in functions that would be faster.

Following Josh Ulrich's answer, how about

library(abind)
apply(abind(a,b,c,d,along=3),c(1,2),median)

? (Using rowMeans on the appropriate slice will still be faster than applying mean ... I think there is a rowMedians in the Biobase (Bioconductor) package if you really need speed?)

I'm not sure JD's answer gives you exactly what you want, since the resulting object wouldn't be the same dimensions as a, b, etc.

Putting your data.frames into a list is a good start though. Then you can subset each column into a new list, cbind that list into a matrix and use apply over it's rows.

a <- data.frame(rnorm(10), runif(10))
b <- data.frame(rnorm(10), runif(10))
c <- data.frame(rnorm(10), runif(10))
d <- data.frame(rnorm(10), runif(10))
myList <- list(a,b,c,d)
sapply(1:ncol(a), function(j) {  # median
  apply(do.call(cbind,lapply(myList,`[`,,j)), 1, median)
})
sapply(1:ncol(a), function(j) {  # mean
  apply(do.call(cbind,lapply(myList,`[`,,j)), 1, mean)
})
sapply(1:ncol(a), function(j) {  # faster mean
  rowMeans(do.call(cbind,lapply(myList,`[`,,j)))
})

you could string your data frames into a list of data frames, then use lapply(myList, mean, ...)