rapply to nested list of data frames in R

问题:

i have a nested list whose fundamental element is data frames, and i want to traverse this list recursively to do some computation of each data frame, finally to get a nested list of results in the same structure as the input. I know "rapply" is exactly for such kind of task, but i met a problem that, rapply actually goes even deeper than i want, i.e. it decomposes every data frame and applies to each column instead (because a data frame itself is a list in R).

One workaround i can think about is to convert each data frame to matrix, but it will force to uniform the data types, so i don't like it really. I want to know if there is any way to control the recursive depth of rapply. Any idea? Thanks.

回答1:

1. wrap in proto

When creating your list structure try wrapping the data frames in proto objects:

library(proto)
L <- list(a = proto(DF = BOD), b = proto(DF = BOD))
rapply(L, f = function(.) colSums(.$DF), how = "replace")

giving:

$a
  Time demand 
    22     89 

$b
  Time demand 
    22     89 

Wrap the result of your function in a proto object too if you want to further rapply it;

f <- function(.) proto(result = colSums(.$DF))
out <- rapply(L, f = f, how = "replace")
str(out)

giving:

List of 2
 $ a:proto object 
 .. $ result: Named num [1:2] 22 89 
 ..  ..- attr(*, "names")= chr [1:2] "Time" "demand" 
 $ b:proto object 
 .. $ result: Named num [1:2] 22 89 
 ..  ..- attr(*, "names")= chr [1:2] "Time" "demand" 

2. write your own rapply alternative

recurse <- function (L, f) {
    if (inherits(L, "data.frame")) f(L)
    else lapply(L, recurse, f)
}

L <- list(a = BOD, b = BOD)
recurse(L, colSums)

This gives:

$a
  Time demand 
    22     89 

$b
  Time demand 
    22     89 

ADDED: second approach