Benchmarks for the options presented at http://stackoverflow.com/questions/28526860/r-turn-list-into-dataframe

SO28526860.R

set.seed(1)
n <- 100000
l <- cbind(sequence(n), sample(c(letters, LETTERS), n, TRUE))
l <- split(l, 1:nrow(l))

library(microbenchmark)
library(stringi)
rb <- function() do.call(rbind, l)
rb2 <- function() {
  A <- data.frame(do.call(rbind, l), stringsAsFactors = FALSE)
  A[] <- lapply(A, type.convert)
  A
}
rbdf <- function() setNames(do.call(rbind.data.frame, l), c("col1", "col2"))
s2a <- function() t(simplify2array(l))
s2a2 <- function() {
  A <- data.frame(t(simplify2array(l)), stringsAsFactors = FALSE)
  A[] <- lapply(A, type.convert)
  A
}
sl2m <- function() stri_list2matrix(l, byrow = TRUE)
sl2m2 <- function() {
  A <- data.frame(stri_list2matrix(l, byrow = TRUE), stringsAsFactors = FALSE)
  A[] <- lapply(A, type.convert)
  A
}
microbenchmark(rb(), rb2(), rbdf(), s2a(), s2a2(), sl2m(), sl2m2(), times = 10)
# Unit: milliseconds
#     expr         min          lq        mean      median          uq         max neval
#     rb() 2107.037607 2126.034404 2189.736374 2168.413060 2270.935747 2304.998340    10
#    rb2() 2167.301232 2179.369576 2252.435357 2196.388378 2220.951455 2737.306016    10
#   rbdf() 3760.737631 3846.041151 4050.422107 4006.229182 4193.175611 4576.599444    10
#    s2a()  270.689418  290.114168  342.303776  306.092319  312.439534  674.295284    10
#   s2a2()  326.833278  365.212878  433.037203  380.049525  437.508256  861.766501    10
#   sl2m()    5.533927    5.655753    6.034395    5.733178    6.801719    7.028414    10
#  sl2m2()   25.641438   26.191216   61.006942   37.640613   54.172781  247.672368    10