I am trying to find all of the unique groupings of a vector/list of items, length 39. Below is the code I have:
x <- c("Dominion","progress","scarolina","tampa","tva","TminKTYS",
"TmaxKTYS","TminKBNA","TmaxKBNA","TminKMEM","TmaxKMEM",
"TminKCRW","TmaxKCRW","TminKROA","TmaxKROA","TminKCLT",
"TmaxKCLT","TminKCHS","TmaxKCHS","TminKATL","TmaxKATL",
"TminKCMH","TmaxKCMH","TminKJAX","TmaxKJAX","TminKLTH",
"TmaxKLTH","TminKMCO","TmaxKMCO","TminKMIA","TmaxKMIA",
"TminKPTA","TmaxKTPA","TminKPNS","TmaxKPNS","TminKLEX",
"TmaxKLEX","TminKSDF","TmaxKSDF")
# Generate a list with the combinations
zz <- sapply(seq_along(x), function(y) combn(x,y))
# Filter out all the duplicates
sapply(zz, function(z) t(unique(t(z))))
However, the code causes my computer to run out of memory. Is there a better way to do this? I realize I have a large list. thanks.
To calculate all unique subsets, you are simply creating all binary vectors with the same length as the cardinality of the original set of items. If there are 39 items, then you are looking at all binary vectors of length 39. Each element of each vector identifies, yes or no, whether or not the item is in the corresponding subset.
As there are 39 items, and each can either be in or not-in a given subset, then there are 2^39 possible subsets. Excluding the empty set, i.e. the all-0 vector, you have 2^39 - 1 possible subsets.
That is, as @joran said, about 549B vectors. Given that the binary vectors are most compactly representing the data (i.e. without strings), then you will need 549B * 39 bits to return all of the subsets. I don't think you want to store this: that's about 2.68E12 bytes. If you insist on using the characters, you're likely to be in the many tens of terabytes.
It's certainly feasible to buy a system that can support this, but not very cost-effective.
At a meta-level, it is very likely, as @JD said, that this is not the path you really need to go. I recommend posting a new question and maybe it can be refined here or on the statistics-related SE site.