As an alternative:
dt[, .SD[1:3], cyl]
When you look at speed on the example dataset, the head method is on par with the .I method of @eddi. Comparing with the microbenchmark package:
microbenchmark(head = dt[, head(.SD, 3), cyl],
SD = dt[, .SD[1:3], cyl],
I = dt[dt[, .I[1:3], cyl]$V1],
times = 10, unit = "relative")
results in:
Unit: relative
expr min lq mean median uq max neval cld
head 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000 10 a
SD 2.156562 2.319538 2.306065 2.365190 2.318540 2.1908401 10 b
I 1.001810 1.029511 1.007371 1.018514 1.016583 0.9442973 10 a
However, data.table is specifically designed for large datasets. So, running this comparison again:
# creating a 30 million dataset
largeDT <- dt[,.SD[sample(.N, 1e7, replace = TRUE)], cyl]
# running the benchmark on the large dataset
microbenchmark(head = largeDT[, head(.SD, 3), cyl],
SD = largeDT[, .SD[1:3], cyl],
I = largeDT[largeDT[, .I[1:3], cyl]$V1],
times = 10, unit = "relative")
results in:
Unit: relative
expr min lq mean median uq max neval cld
head 2.279753 2.194702 2.221330 2.177774 2.276986 2.33876 10 b
SD 2.060959 2.187486 2.312009 2.236548 2.568240 2.55462 10 b
I 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000 10 a
Now the .I method is clearly the fastest one.
Update 2016-02-12:
With the most recent development version of the data.table package, the .I method still wins. Whether the .SD method or the head() method is faster seems to depend on the size of the dataset. Now the benchmark gives:
Unit: relative
expr min lq mean median uq max neval cld
head 2.093240 3.166974 3.473216 3.771612 4.136458 3.052213 10 b
SD 1.840916 1.939864 2.658159 2.786055 3.112038 3.411113 10 b
I 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 10 a
However with a somewhat smaller dataset (but still quite big), the odds change:
largeDT2 <- dt[,.SD[sample(.N, 1e6, replace = TRUE)], cyl]
the benchmark is now slightly in favor of the head method over the .SD method:
Unit: relative
expr min lq mean median uq max neval cld
head 1.808732 1.917790 2.087754 1.902117 2.340030 2.441812 10 b
SD 1.923151 1.937828 2.150168 2.040428 2.413649 2.436297 10 b
I 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 10 a
