I'm trying to do a simple genomic track intersection in R, and running into major performance problems, probably related to my use of for loops.

In this situation, I have pre-defined windows at intervals of 100bp and I'm trying to calculate how much of each window is covered by the annotations in mylist. Graphically, it looks something like this:

          0    100   200    300    400   500   600  
windows: |-----|-----|-----|-----|-----|-----|

mylist:    |-|   |-----------|

So I wrote some code to do just that, but it's fairly slow and has become a bottleneck in my code:

##window for each 100-bp segment    
windows <- numeric(6)

##second track
mylist = vector("list")
mylist[[1]] = c(1,20)
mylist[[2]] = c(120,320)


##do the intersection
for(i in 1:length(mylist)){
  st <- floor(mylist[[i]][1]/100)+1
  sp <- floor(mylist[[i]][2]/100)+1
  for(j in st:sp){       
    b <- max((j-1)*100, mylist[[i]][1])
    e <- min(j*100, mylist[[i]][2])
    windows[j] <- windows[j] + e - b + 1
  }
}

print(windows)
[1]  20  81 101  21   0   0

Naturally, this is being used on data sets that are much larger than the example I provide here. Through some profiling, I can see that the bottleneck is in the for loops, but my clumsy attempt to vectorize it using *apply functions resulted in code that runs an order of magnitude more slowly.

I suppose I could write something in C, but I'd like to avoid that if possible. Can anyone suggest another approach that will speed this calculation up?