zipping lists in R

Question

As a guideline I prefer apply functions on elements of a list using lapply or *ply (from plyr) rather than explicitly iterating through them. However, this works well when I have to process one list at a time. When the function takes multiple arguments, I usually do a cycle.

I was wondering if it's possible to have a cleaner construct, still functional in nature. One possible approach could be to define a function similar to Python, zip(x,y), which takes the input lists, and returns a list, whose i-th element is list(x, y), and then apply the function to this list. But my question is whether I am using the cleanest approach or not. I am not worried about performance optimization, but rather clarity/elegance.

Below is the naive example.

        A <- as.list(0:9)
        B <- as.list(0:9)
        f <- function(x, y) x^2+y

        OUT <- list()
        for (n in 1:10) OUT[[n]] <- f(A[[n]], B[[n]])
        OUT
        [[1]]
        [1] 0

        [[2]]
        [1] 2

        ...

And here is the zipped example (which could be extended to arbitrary arguments):

zip <- function(x, y){
    stopifnot(length(x)==length(y))
    z <- list()
    for (i in seq_along(x)){
        z[[i]] <- list(x[[i]], y[[i]]) 
    }
    z
}
E <- zip(A, B)

lapply(E, function(x) f(x[[1]], x[[2]]))

[[1]]
[1] 0

[[2]]
[1] 2

 ...

Answer 1

I think you're looking for mapply:

   ‘mapply’ is a multivariate version of ‘sapply’.  ‘mapply’ applies
     ‘FUN’ to the first elements of each ...  argument, the second
     elements, the third elements, and so on.  Arguments are recycled
     if necessary.

For your example, use mapply(f, A, B)

Answer 2

I came across a similar problem today. And after learning the usage of the func mapply, I know how to solve it now.

mapply is so cool!!

Here is an examples:

en = c("cattle", "chicken", "pig")
zh = c("牛",      "鸡",      "猪")

dict <- new.env(hash = TRUE)
Add <- function(key, val) dict[[key]] <- val

mapply(Add, en, zh)
##  cattle chicken     pig 
##    "牛"    "鸡"    "猪"