r - merging data.tables based on columns names

Question

I am trying to do some left-join merges with data.tables. The package description quote that

In all joins the names of the columns are irrelevant; the columns of x's key are joined to in order

I understand that I can use .data.table[ and data.table:::merge.data.table

What I would like is : merge X and Y specifying the keys (like by.x and by.y in base merge, ->why taking this away ?)

Let's suppose I have

DT = data.table(x=rep(c("a","b","c"),each=3),y=c(1,3,6),v=1:9,key="x,y,v")
DT1 = data.frame(x1=c("aa","bb","cc"),y1=c(1,3,6),v1=1:3,key="x1,y1,v1")

and I would like this output:

#data.table:::merge is masking I don't know how to call the base version of merge anymore
R) {base::merge}(DT,DT1,by.x="y",by.y="y1") 
y x v x1 v1
1 1 a 1 aa  1
2 1 c 7 aa  1
3 1 b 4 aa  1
4 3 a 2 bb  2
5 3 b 5 bb  2
6 3 c 8 bb  2
7 6 b 6 cc  3
8 6 a 3 cc  3
9 6 c 9 cc  3

I am very happy to use [ or data.table:::merge but I would like an option that do not modify DT or DT1 (like changing the column names and calling merge and changing it back)

Answer 1

Update: Since data.table v1.9.6 (released September 19, 2015), merge.data.table() does accept and nicely handles arguments by.x= and by.y=. Here's an updated link to the FR (now closed) referenced below.

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Yes this is a feature request not yet implemented :

FR#2033 Add by.x and by.y to merge.data.table

There isn't anything preventing it. Just something that wasn't done. I very rarely need merge and was slow to realise its usefulness more generally. We've made good progress in bringing merge performance as fast as X[Y], and this feature request is at the highest priority. If you'd like it more quickly you are more than welcome to add those arguments to merge.data.table and commit the change yourself. We try to keep source code short and together in one function/file, so by looking at merge.data.table source hopefully you can follow it and see what needs to be done.