If I have a data.table
> DT1 <- data.table(A=rep(c('A', 'B'), 3),
B=rep(c(1,2,3), 2),
val=rnorm(6), key='A,B')
> DT1
A B val
1: A 1 -1.6283314
2: B 2 0.5337604
3: A 3 0.9991301
4: B 1 1.1421400
5: A 2 0.1230095
6: B 3 0.4988504
and I want to subset by more than one key, like so:
> DT1[J('A', 1)]
A B val
1: A 1 -0.004898047
However, the join is dependent on the order of the keys, so the value for key A must always come first. This will not work, even if you specify names (either as J()
or as a list()
):
> DT1[J(1, 'A')]
Error in `[.data.table`(DT1, J(1, "A")) :
x.'A' is a character column being joined to i.'V1' which is type 'double'. Character columns must join to factor or character columns.
> DT1[J(B=1, A='A')]
Error in `[.data.table`(DT1, J(B = 1, A = "A")) :
x.'A' is a character column being joined to i.'B' which is type 'double'. Character columns must join to factor or character columns.
Is there a syntax where you can do this kind of grouping by i
without knowing the order of the keys?
Added: Another use case would be if I wanted to subset by B only and not by A -- is there a way to skip keys in the subsetting? The current answers that create wrapper functions for J don't seem to allow this.
EDIT: Some have mentioned doing it the data.frame way. I know that you can use a vector of logical values to subset, but this is slow as it does a scan of the entire table:
> DT1 <- data.table(A=rep(c(1,2,3), 100000), B=rep(c('A', 'B'), 150000), val=rnorm(300000), key='A,B')
> system.time(DT1[DT1$A==1, DT1$B=="A"])
user system elapsed
0.080 0.000 0.054
> system.time(DT1[J(1, 'A')])
user system elapsed
0.004 0.000 0.004
Some references to related discussions: (1)