Arun, If the new boolean is false, the result would be the same as without it and would be equal to current behavior of d[i][, j]. If it's true, it will only have an effect if i is a join (I think each.i= fits slightly better for this description than .join=) - this will replicate current underlying behavior. If you think the cross-apply is something that could work not just for i being a data-table but other things as well, then it would make perfect sense to implement that action too when the bool is true.
On Apr 30, 2013, at 2:58 AM, Arunkumar Srinivasan <[email protected]> wrote: (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.
_______________________________________________ datatable-help mailing list [email protected] https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help
