Tabulating association frequency counts - r

Question

I have data which is in this format:
User Item
1 A
1 B
1 C
1 D
2 A
2 C
2 E
What I want to get is a frequency count for each pair. Order is not important so I don't want to count the inverse. I want to end up with a result similar to this, where the frequency counts are partitioned by user.
Pair Frequency
AB 1
AC 2
AD 1
AE 1
BC 1
BD 1
BE 0
CD 1
CE 1
What tool can I use to formulate this kind of table? I'd prefer some open source solution if possible.
Edit- Added example for my comment below
I'm reading in data from a CSV file using the following two lines and removing the factors with these two steps in code.
xa<-read.csv("C:/Direcotry/MyData.csv")
xa<-data.frame(lapply(xa, as.character), stringsAsFactors=FALSE)
User Item
1 394324 Item A
2 124209 Item B
3 212457 Item C
4 427052 Item A
5 118281 Item D
6 156831 Item A
7 212442 Item E
8 156831 Item B
9 212442 Item A
10 177734 Item C
When I try running suggested answer, I get an error with this result:
Error in combn(x, 2) : n < m

Answer 1

Well R is open source.
Here's an example based on your tiny sample of data:
Here I just read your data in by copypasting it straight from your post:
> xa=read.table(stdin(),header=TRUE,as.is=TRUE)
0: User Item
1: 1 A
2: 1 B
3: 1 C
4: 1 D
5: 2 A
6: 2 C
7: 2 E
8:
So that's the data in. Then with a couple of lines of code:
> f=function(x) apply(combn(x,2),2,paste0,collapse="")
> table(unlist(tapply(xa$Item,xa$User,f)))
AB AC AD AE BC BD CD CE
1 2 1 1 1 1 1 1
If you need all the empty combinations explicitly as zeroes it takes another line or two (you need to generate all the possible combinations as a factor, rather than just the observed ones and tell table to include the empty ones).

Answer 2

After some research and suggestions by Glen, I came up with the following code which gets me a 3 column CSV file with the pair combination plus frequency count. If anyone sees a better way, let me know! But this seems to work.
The errors I was referring to in my follow up comments were caused by users having purchased only at one location.
library(reshape2)
xa<-read.csv("C:/Input.csv",as.is=TRUE)
xa=xa[!duplicated(xa),]
xa<-data.table(xa)
setkey(xa,ContactId,PurchaseLocation)
tab=table(xa$ContactId)
xa=xa[xa$ContactId %in% names(tab[tab>1]),]
f=function(x) apply(combn(x,2),2,paste0,collapse="--")
xb<-as.data.frame(table(unlist(tapply(xa$PurchaseLocation,xa$ContactId,f))))
xc=with(xb, cbind(Freq, colsplit(xb$Var1, pattern = "--", names = c('a', 'b'))))
xc=subset(xc,a!=b & a!="" & b!="" & Freq>1)
write.csv(xc,file="C:/Output.csv")
Edit- I made a small change to make it order independent by sorting the data table on a key.