I have a medical insurance claims datafile divided into blocks by member, with
multiple lines per member. I am processing these into a one line per member
binary model matrix. Member block sizes vary from 1 to 50+. I am matching
attributes in claims data to columns in the model matrix and have been getting
by with a for loop, but for large file size it takes much too long. Is there
vectorized/apply based method to do this more efficiently?
I am only targeting codes A-E so anything else does not register
> member<- c(rep(1,3),rep(2,2),rep(3,4),rep(4,2))
> code <- c('A','C','F','B','E','D','A','B','D','G','A')
> claims.df <- data.frame(member=member,code=code)
> claims.df
member code
1 1 A
2 1 C
3 1 F
4 2 B
5 2 E
6 3 D
7 3 A
8 3 B
9 3 D
10 4 G
11 4 A
> code.list <- c('A','B','C','D','E')
> n.code <- length(code.list)
> mbr.list <- unique(member)
> n.mbr <- length(mbr.list)
> code.mat <- matrix(0,n.mbr,n.code)
> dimnames(code.mat) <- list(mbr.list,code.list)
> for(i in 1:n.mbr){
+ mbr.i <- claims.df[claims.df$member==mbr.list[i],] #EXTRACT BLOCK OF
MEMBER CLAIMS
+ code.mat[i,unique(match(mbr.i$code,code.list))] <- 1
+ }
> code.mat
A B C D E
1 1 0 1 0 0
2 0 1 0 0 1
3 1 1 0 1 0
4 1 0 0 0 0
Craig
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