(The earlier message was too long and was rejected.)
So, from the discussion so far, I see that Matthew is nice enough to implement
`.JOIN` or `cross.apply`. I've a couple of questions. Suppose,
DT1 <- data.table(x=c(1,1,2,3,3), y=1:5, z=6:10)
setkey(DT1, "x")
DT2 <- data.table(x=1)
DT1[DT2, y, .JOIN=TRUE] # I guess the syntax is something like this. I
expect here the same output as current DT1[DT2, y]
The above syntax seems "okay". But my first question is what is `.JOIN=FALSE`
supposed to do under these two circumstances? Suppose,
DT1 <- data.table(x=c(1,1,2,3,3), y=1:5, z=6:10)
setkey(DT1, "x")
DT2 <- data.table(x=c(1,2,1), w=c(11:13))
# what's the output supposed to be for?
DT1[DT2, y, .JOIN=FALSE]
DT1[DT2, .JOIN = FALSE]
Depending on this I'd have to think about `drop = TRUE/FALSE`. Also, how does
it work with `subset`?
DT1[x %in% c(1,2,1), y, .JOIN=TRUE] # .JOIN is ignored?
Is this supposed to also do a "cross-apply" on the logical subset? I guess not.
So, .JOIN is an "extra" parameter that comes into play *only* when `i` is a
`data.table`?
I'd love to have some replies to these questions for me to take a stance on
`.JOIN`. Thank you.
Best,
Arun.
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