Why is it faster to evaluate in `j` than with `$` in a `data.table`?

Question

Perhaps this is already answered and I missed it, but it\'s hard to search.

A very simple question: Why is dt[,x] generally a tiny bit faster than

Answer 1

With j, you are subsetting and selecting within a call to [.data.table.

With $ (and your call), you are subsetting within [.data.table and then selecting with $

You are in essence calling 2 functions not 1, thus there is a neglible difference in timing.

In your current example you are calling `sampling(1e,200000) each time.

For comparison to return identical results

dt<-data.table(id=1:1e7,var=rnorm(1e6))
setkey(dt, id)
ii <- sample(1e7,size=200000)


microbenchmark("in j" = dt[.(ii),var], "$"=dt[.(ii)]$var, '[[' =dt[.(ii)][['var']], .subset2(dt[.(ii)],'var'), dt[.(ii)][[2]], dt[['var']][ii], dt$var[ii], .subset2(dt,'var')[ii] )
Unit: milliseconds
                       expr       min        lq      mean    median        uq       max neval cld
                       in j 39.491156 40.358669 41.570057 40.860342 41.485622 70.202441   100   b
                          $ 39.957211 40.561965 41.587420 41.136836 41.634584 69.928363   100   b
                         [[ 40.046558 40.515480 42.388432 41.244444 41.750946 72.224827   100   b
 .subset2(dt[.(ii)], "var") 39.772781 40.564077 41.561271 41.111630 41.635489 69.252222   100   b
             dt[.(ii)][[2]] 40.004300 40.513669 41.682526 40.927503 41.492866 72.986995   100   b
            dt[["var"]][ii]  4.432346  4.546898  4.946219  4.623416  4.755777 31.761115   100  a 
                 dt$var[ii]  4.440496  4.539502  4.668361  4.597457  4.729214  5.425125   100  a 
    .subset2(dt, "var")[ii]  4.365939  4.508261  4.660435  4.598815  4.703858  6.072289   100  a