Finding means and medians across data frames in r

Question

I have several data frames, a b c d , each with the same column names. I want to find the mean and 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.

Answer 1

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 apply ing mean ... I think there is a rowMedians in the Biobase (Bioconductor) package if you really need speed?)

Answer 2

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)))
})

Answer 3

您可以将数据帧串入数据帧列表，然后使用lapply(myList, mean, ...)