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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