Why data.table CJ doesn't respect column major order

Question

This is a curiosity more than a question, but I was wondering why data.table CJ function returns an object with the rightmost index running faster (as opposite as base expand.grid function).

An example:

CJ(a=letters[1:2],b=LETTERS[1:2])
#   a b
#1: a A
#2: a B
#3: b A
#4: b B
expand.grid(a=letters[1:2],b=LETTERS[1:2])
#  a b
#1 a A
#2 b A
#3 a B
#4 b B

I think that the leftmost index running faster is more R-ish. Is there a reason for CJ to follow the other order?

Answer 1

It's convenient to have the result of CJ sorted like that, as it can then be keyed by all of the columns, which it is, which then enables operations like this:

dt = data.table(a = c(1,2,1), b = 1:3, c = c('a', 'a', 'b'))
setkey(dt, a, c)
#   a b c
#1: 1 1 a
#2: 1 3 b
#3: 2 2 a

dt[CJ(unique(a), unique(c))]
#   a  b c
#1: 1  1 a
#2: 1  3 b
#3: 2  2 a
#4: 2 NA b

# just checking the key:
key(dt[, CJ(unique(a), unique(c))])
#[1] "V1" "V2"