min_rank vs min - Hadley's "premature optimisation" point

SO_21308436.R

require(dplyr)
require(data.table)

foo <- function(N) {

    group_sizes = 10^(1:(log10(N)-1L))
    uniqval <- unique(runif(2*N))

    fans <- vector("list", length(group_sizes))
    for (i in seq_along(group_sizes)) {
        print(group_sizes[i])
        set.seed(1L)
        DF <- group_by(
                 tbl_df(
                     data.frame(
                          ID  = sample(rep(1:group_sizes[i], (N/group_sizes[i]))), 
                          val = sample(uniqval, N, replace=FALSE)
                     )
                 ), ID
              )
        ans = rep(0, 2)
        ans[1] <- system.time(filter(DF, min_rank(val) == 1L))['elapsed']
        ans[2] <- system.time(filter(DF, val==min(val)))['elapsed']
        fans[[i]] = ans
        rm(DF); gc()
    }
    fans <- as.data.table(do.call(rbind, fans))
    setnames(fans, c('min_rank', 'min'))
}

N <- 10^(5:7)
ans <- lapply(N, foo)
> ans
[[1]] # N=1e5
   min_rank   min
1:    0.102 0.004 # group_size=10
2:    0.087 0.004 # =100
3:    0.088 0.006 # =1000
4:    0.110 0.025 # =10000

[[2]] # N=1e6
   min_rank   min
1:    1.728 0.068 # =10
2:    1.005 0.076 # =100
3:    0.905 0.079 # =1000
4:    0.885 0.099 # =10000
5:    1.086 0.368 # =100000

[[3]] # N=1e7
   min_rank   min
1:   27.832 0.676 # =10
2:   16.975 1.093 # =100
3:   10.180 0.874 # =1000
4:    9.107 0.971 # =10000
5:    8.963 1.180 # =100000
6:   11.776 2.961 # =1000000