Aggregating a data.frame

aggregate_test.R

z <- function(data, FUN = mean, ...) aggregate(. ~ 1, data = data, FUN = FUN, ...)
y <- function(data, FUN = mean, ...) data.frame(t(apply(data, 2, FUN = FUN, ...)))
x <- function(data, FUN = mean, ...) data.frame(t(sapply(data, mean, ...)))

d1 <- data.frame(matrix(1e3, ncol=10))
d2 <- data.frame(matrix(1e6, ncol=10))
d3 <- data.frame(matrix(1e6, ncol=100))


# MEAN
library("microbenchmark")
microbenchmark(z(d1), y(d1), x(d1), z(d2), y(d2), x(d2), z(d3), y(d3), x(d3))

Unit: microseconds
  expr       min         lq       mean     median         uq       max neval  cld
 z(d1)  3816.927  4058.6360  4330.8091  4164.8650  4302.0775  7598.444   100   c 
 y(d1)   553.084   603.8890   666.2021   634.1020   682.0210  1966.774   100 a   
 x(d1)   340.626   368.9150   396.0036   380.2695   412.4080   554.624   100 a   
 z(d2)  3878.509  4101.3585  4461.5206  4202.9690  4415.8115  7723.917   100   c 
 y(d2)   556.933   603.5035   669.4929   637.3735   698.1860  2406.316   100 a   
 x(d2)   340.626   370.6470   414.3319   384.5035   429.1505  1914.815   100 a   
 z(d3) 27622.584 29516.8060 30809.0338 30358.7465 31212.8110 86128.128   100    d
 y(d3)  2979.413  3193.7945  3685.8535  3352.1755  3661.0475  7326.328   100   c 
 x(d3)  1549.941  1715.4425  1924.5022  1775.2930  1839.7620  3719.551   100  b