[R] question on more efficient data block-match processing

[Rainer Schuermann]

What I would do:

# read in your sample data
mbr <- read.table( "clipboard", header = TRUE, stringsAsFactors = FALSE )

# create a vector with the codes you want to consider
code.list <- c("A","B","C","D","E")

# reduce the data accordingly
mbr <- mbr[ mbr$code %in% code.list, ]

# get your model matrix using reshape
library( reshape )
model.matrix <- as.data.frame( cast( melt( mbr ), value ~ code ) )

# Cosmetics
colnames( model.matrix )[1] <- "Member"
model.matrix[ 2 : ( length( model.matrix[1,] ) ) ] <- 
    ifelse( model.matrix[ 2 : ( length( model.matrix[1,] ) ) ] > 0, 1, 0 )




On Thursday 06 March 2014 19:23:03 Mckinstry, Craig wrote:
> 
> I have a medical insurance claims datafile divided into blocks by member, with multiple lines per member. I am process these into a one line per member model matrix. Member block sizes vary from 1 to 50+. I am match 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?
> 
> input data:
> 	
> member	code
> 1	A
> 1	C
> 1	F
> 2	B
> 2	E
> 3	D
> 3	A
> 3	B
> 3	D
> 4	G
> 4	A
> 
> code.list <- c(A,B,C,D,E)
> for(i in 1:n.mbr){
>   mbr.i <- dat[dat$Rmbr==mbr.list[i],]	#EXTRACT BLOCK OF MEMBER CLAIMS
>   matrix.mat[i,unique(match(mbr.i$code,code.list))] <- 1
> }	
>  
> 
> output model.matrix
> Member	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 McKinstry
> 100 Market, 6th floor
> Office: 503-225-6878 | Cell: 509-778-2438
> 
> 
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