Calculate in deeper list levels R

Question

Imagine, I have list of two levels:

lll <- list()

lll[[1]] <- list(1:10, 1:5, 1:2)

lll[[2]] <- list(10:20, 20:30)

lll

 [[1]]

 [[1]][[1]]

  [1]  1  2  3  4  5  6  7  8  9 10



 [[1]][[2]]

 [1] 1 2 3 4 5



 [[1]][[3]]

  [1] 1 2





 [[2]]

 [[2]][[1]]

  [1] 10 11 12 13 14 15 16 17 18 19 20



 [[2]][[2]]

  [1] 20 21 22 23 24 25 26 27 28 29 30

I want calculate means of these sequences. I have written a little function, which works fine:

func <- function(list.list){

     lapply(1:length(list.list), function(i) mean(list.list[[i]]))

}



lapply(lll, func)

I don't like in this function, that I have to use anonymous function.
It gets even more complicated when I have list of 3 levels.

Maybe you know better ways to make calculations in which anonymous function would not be included? Should I use higher-order functions (

Map

,

Reduce

)?

I know how to write

for

cycle, but in this case it isn't an option.

Recommended for you: Get network issues from WhatsUp Gold. Not end users.

Answer 1

Here's a possible solution (using rapply = recursive apply) working at any level of depth :

lll <- list()
lll[[1]] <- list(1:10, 1:5, 1:2)
lll[[2]] <- list(10:20, 20:30)

res <- rapply(lll,mean,how='replace')
> res
[[1]]
[[1]][[1]]
[1] 5.5

[[1]][[2]]
[1] 3

[[1]][[3]]
[1] 1.5


[[2]]
[[2]][[1]]
[1] 15

[[2]][[2]]
[1] 25

Setting argument how='unlist' you will get :

res <- rapply(lll,mean,how='replace')
> res
[1]  5.5  3.0  1.5 15.0 25.0

Calculate in deeper list levels R

Sign up