There's a much shorter way. You don't need that ugly h() with all those $
and potential for bugs !
Using the original f :
dt[,lapply(.SD,f),by=key(dt)]
grp1 grp2 grp3 a b d
x x x 1.000000 81.000000 161.000000
x x x 10.000000 90.000000 170.000000
x x x 5.500000 85.500000 165.500000
x x x 3.027650 3.027650 3.027650
x x x 1.816590 28.239721 54.662851
x x y 11.000000 91.000000 171.000000
x x y 20.000000 100.000000 180.000000
x x y 15.500000 95.500000 175.500000
x x y 3.027650 3.027650 3.027650
x x y 5.119482 31.542612 57.965742
[ snip ]
To get the names included, one (long) way is :
dt[,data.table(sapply(.SD,f),keep.rownames=TRUE),by=key(dt)]
grp1 grp2 grp3 rn a b d
x x x min 1.000000 81.000000 161.000000
x x x max 10.000000 90.000000 170.000000
x x x mean 5.500000 85.500000 165.500000
x x x sd 3.027650 3.027650 3.027650
x x x cv 1.816590 28.239721 54.662851
x x y min 11.000000 91.000000 171.000000
x x y max 20.000000 100.000000 180.000000
x x y mean 15.500000 95.500000 175.500000
x x y sd 3.027650 3.027650 3.027650
x x y cv 5.119482 31.542612 57.965742
[ snip ]
However, for speed on large datasets you can drop the names in f :
f <- function(x) c(min(x), max(x), mean(x), sd(x), mean(x)/sd(x))
and put the names in afterwards.
ans = dt[,lapply(.SD,f),by=key(dt)]
ans$labels = c("min","max","mean","sd","cv")
ans
grp1 grp2 grp3 a b d labels
x x x 1.000000 81.000000 161.000000 min
x x x 10.000000 90.000000 170.000000 max
x x x 5.500000 85.500000 165.500000 mean
x x x 3.027650 3.027650 3.027650 sd
x x x 1.816590 28.239721 54.662851 cv
x x y 11.000000 91.000000 171.000000 min
x x y 20.000000 100.000000 180.000000 max
x x y 15.500000 95.500000 175.500000 mean
x x y 3.027650 3.027650 3.027650 sd
x x y 5.119482 31.542612 57.965742 cv
[ snip ]
You don't want all those small pieces of memory for the names to be created
over and over again every time f runs. That's only important for large
datasets, though.
Matthew
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