Fastest Way to Add New Variables to A Large Data.Frame

pkgs <- list("hflights", "doParallel", "foreach", "dplyr", "data.table", "microbenchmark")
sapply(pkgs, require, character.only = T)

data(hflights)
hflights_DT <- data.table(hflights)

microbenchmark(
  transform = {
    ### THE GENERIC FUNCTION MODIFYING THE DATA.FRAME, SIMILAR TO DATA.FRAME() ###
    transform(hflights, wday = ifelse(DayOfWeek %in% c(6, 7), 'weekend', 'weekday'),
              delay = ArrDelay + DepDelay)
  },
  within = {
    ### EVALUATE THE EXPRESSION WITHIN THE LOCAL ENVIRONMENT ###
    within(hflights, {wday = ifelse(DayOfWeek %in% c(6, 7), 'weekend', 'weekday')
    delay = ArrDelay + DepDelay})
  },
  mutate = {
    ### THE SPECIFIC FUNCTION IN DPLYR PACKAGE TO ADD VARIABLES ###
    mutate(hflights, wday = ifelse(DayOfWeek %in% c(6, 7), 'weekend', 'weekday'),
           delay = ArrDelay + DepDelay)
  },
  data.table = {
    ### DATA.TABLE ###
    hflights_DT[, `:=`(wday = ifelse(DayOfWeek %in% c(6, 7), 'weekend', 'weekday'),
                       delay = ArrDelay + DepDelay)]
  }
)
# Unit: milliseconds
#        expr       min        lq      mean    median        uq      max neval
#   transform 168.17041 178.67501 185.84942 183.13138 187.97160 231.0397   100
#      within  68.20806  72.91385  81.53380  75.11322  83.05596 124.4741   100
#      mutate  68.63053  72.61046  78.44092  74.66822  78.68297 127.4133   100
#  data.table  68.29788  72.54419  78.61315  75.00599  78.44248 119.8358   100