CODEWITHSUNDEEP
rfunction
Peanut
Peanut

Reputation: 835

Create list of a vector's elements: loop vs. apply

I started coding in R lately, and I read that the apply function is faster than a for loop.

Let's say I want to extract numbers from a vector and insert them into a list. Using a for loop this not a problem. However, I'm curious if this is also possible with an apply function and if that makes sense in any way. I had something like this in mind (which is not working):

some.list <- list()
some.vector <- 1:10
sapply(1:10,function(i){some.list[[i]] <- some.vector[i]})

Upvotes: 0

Views: 228

Answers (1)

josliber
josliber

Reputation: 44330

There are all sorts of different ways to create a list containing the elements of a vector (the one that I would always use would be as.list). You can use R benchmarking packages to test for yourself which is faster:

fun1 <- function(v) as.list(v)
fun2 <- function(v) {
  l <- vector("list", length(v))  # Thanks to @MrFlick for pre-allocation tip
  for (i in seq_along(v)) {
    l[[i]] <- v[i]
  }
  l
}
fun2a <- function(v) {
  l <- vector("list", length(v))  # Thanks to @MrFlick for pre-allocation tip
  sapply(seq_along(v), function(i) l[[i]] <<- v[i])
  l
}
fun3 <- function(v) lapply(v, identity)
fun3a <- function(v) sapply(v, identity, simplify=FALSE)
fun4 <- function(v) unname(split(v, seq_along(v)))

v <- 1:10000
# Check if all return same thing (see http://stackoverflow.com/a/30850654/3093387)
all(sapply(list(fun2(v), fun2a(v), fun3(v), fun3a(v), fun4(v)), identical, fun1(v)))
# [1] TRUE

library(microbenchmark)
microbenchmark(fun1(v), fun2(v), fun2a(v), fun3(v), fun3a(v), fun4(v))
# Unit: microseconds
#      expr       min         lq       mean    median         uq       max neval
#   fun1(v)   139.543   178.5015   283.7498   218.720   288.1555  3730.439   100
#   fun2(v)  6809.344  7465.1110  9326.7799  7912.763 10881.0305 16963.567   100
#  fun2a(v) 10790.471 13786.2335 15912.5338 15089.547 15787.3085 71504.328   100
#   fun3(v)  4132.854  4545.2085  6612.3504  4768.798  7947.0820 63608.519   100
#  fun3a(v)  4147.731  4537.0010  5887.4457  4805.952  7604.4250 13613.517   100
#   fun4(v)  3341.360  3508.2995  3798.4246  3599.220  3797.1200  7565.591   100

For a list of length 10000, as.list is about 15x faster than lapply, sapply with simplify=FALSE, or split. In turn these three options 2-3x faster than a for loop or sapply with a <<- (using pre-allocated output lists; it is about 75x slower if we don't pre-allocate). In short, sapply and for had similar runtimes (sapply actually appeared a bit slower), and both are much slower than vectorized functions for this operation.

Upvotes: 2

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