R data.table column names not working within a function - r

Question

I am trying to use a data.table within a function, and I am trying to understand why my code is failing. I have a data.table as follows:
DT <- data.table(my_name=c("A","B","C","D","E","F"),my_id=c(2,2,3,3,4,4))
> DT
my_name my_id
1: A 2
2: B 2
3: C 3
4: D 3
5: E 4
6: F 4
I am trying to create all pairs of "my_name" with different values of "my_id", which for DT would be:
Var1 Var2
A C
A D
A E
A F
B C
B D
B E
B F
C E
C F
D E
D F
I have a function to return all pairs of "my_name" for a given pair of values of "my_id" which works as expected.
get_pairs <- function(id1,id2,tdt) {
return(expand.grid(tdt[my_id==id1,my_name],tdt[my_id==id2,my_name]))
}
> get_pairs(2,3,DT)
Var1 Var2
1 A C
2 B C
3 A D
4 B D
Now, I want to execute this function for all pairs of ids, which I try to do by finding all pairs of ids and then using mapply with the get_pairs function.
> combn(unique(DT$my_id),2)
[,1] [,2] [,3]
[1,] 2 2 3
[2,] 3 4 4
tid1 <- combn(unique(DT$my_id),2)[1,]
tid2 <- combn(unique(DT$my_id),2)[2,]
mapply(get_pairs, tid1, tid2, DT)
Error in expand.grid(tdt[my_id == id1, my_name], tdt[my_id == id2, my_name]) :
object 'my_id' not found
Again, if I try to do the same thing without an mapply, it works.
get_pairs3(tid1[1],tid2[1],DT)
Var1 Var2
1 A C
2 B C
3 A D
4 B D
Why does this function fail only when used within an mapply? I think this has something to do with the scope of data.table names, but I'm not sure.
Alternatively, is there a different/more efficient way to accomplish this task? I have a large data.table with a third id "sample" and I need to get all of these pairs for each sample (e.g. operating on DT[sample=="sample_id",] ). I am new to the data.table package, and I may not be using it in the most efficient way.

Answer 1

The function debugonce() is extremely useful in these scenarios.
debugonce(mapply)
mapply(get_pairs, tid1, tid2, DT)
# Hit enter twice
# from within BROWSER
debugonce(FUN)
# Hit enter twice
# you'll be inside your function, and then type DT
DT
# [1] "A" "B" "C" "D" "E" "F"
Q # (to quit debugging mode)
which is wrong. Basically, mapply() takes the first element of each input argument and passes it to your function. In this case you've provided a data.table, which is also list. So, instead of passing the entire data.table, it's passing each element of the list (columns).
So, you can get around this by doing:
mapply(get_pairs, tid1, tid2, list(DT))
But mapply() simplifies the result by default, and therefore you'd get a matrix back. You'll have to use SIMPLIFY = FALSE.
mapply(get_pairs, tid1, tid2, list(DT), SIMPLIFY = FALSE)
Or simply use Map:
Map(get_pairs, tid1, tid2, list(DT))
Use rbindlist() to bind the results.
HTH

Answer 2

Enumerate all possible pairs
u_name <- unique(DT$my_name)
all_pairs <- CJ(u_name,u_name)[V1 < V2]
Enumerate observed pairs
obs_pairs <- unique(
DT[,{un <- unique(my_name); CJ(un,un)[V1 < V2]}, by=my_id][, !"my_id"]
)
Take the difference
all_pairs[!J(obs_pairs)]
CJ is like expand.grid except that it creates a data.table with all of its columns as its key. A data.table X must be keyed for a join X[J(Y)] or a not-join X[!J(Y)] (like the last line) to work. The J is optional, but makes it more obvious that we're doing a join.
Simplifications. #CathG pointed out that there is a cleaner way of constructing obs_pairs if you always have two sorted "names" for each "id" (as in the example data): use as.list(un) in place of CJ(un,un)[V1 < V2].

Answer 3

Why does this function fail only when used within an mapply? I think
this has something to do with the scope of data.table names, but I'm
not sure.
The reason the function is failing has nothing to do with scoping in this case. mapply vectorizes the function, it takes each element of each parameter and passes to the function. So, in your case, the data.table elements are its columns, so mapply is passing the column my_name instead of the complete data.table.
If you want to pass the complete data.table to mapply, you should use the MoreArgs parameter. Then your function will work:
res <- mapply(get_pairs, tid1, tid2, MoreArgs = list(tdt=DT), SIMPLIFY = FALSE)
do.call("rbind", res)
Var1 Var2
1 A C
2 B C
3 A D
4 B D
5 A E
6 B E
7 A F
8 B F
9 C E
10 D E
11 C F
12 D F