Avoiding for loops

you could try something along these lines:

data <- data.frame(y = rnorm(100), group = rep(1:10, each = 10))

data$sum <- ave(data$y, data$group, FUN = sum)
data$norm.y <- data$y / data$sum
data

I hope it helps.

Best,
Dimitris


Noah Silverman wrote:

Hi,

I'm trying to normalize some data.
My data is organized by groups.  I want to normalize PER GROUP as
opposed to over the entire data set.

The current double loop that I'm using takes almost an hour to run on
about 30,000 rows of data in 2,500 groups.

I'm currently doing this:

-------------------------------------
for(group in unique(data$group)){
    sum_V1 <- sum(data$V1[data$group == group])

    for(subject in data$subject[data$group == group]){
        data$V1_norm[(data$group == group & data$subject == subject)]
<- data$V1[(data$group == group & data$subject == subject)] / sum_V1
    }
}
-------------------------------------

Can anyone point me to a faster way to do this in R.

Thanks!

-N