how to substitute a for loop in R with an optimized function (lapply?)

Question

I've a data frame with time events on each row. In one row I've have the events types of sender (typeid=1) and on the other the events of the receiver (typeid=2). I want to calculate the delay between sender and receiver (time difference).

My data is organized in a data.frame, as the following snapshot shows:

dd[1:10,]
     timeid   valid typeid
1  18,00035 1,00000      1
2  18,00528 0,00493      2
3  18,02035 2,00000      1
4  18,02116 0,00081      2
5  18,04035 3,00000      1
6  18,04116 0,00081      2
7  18,06035 4,00000      1
8  18,06116 0,00081      2
9  18,08035 5,00000      1
10 18,08116 0,00081      2

calc_DelayVIDEO <- function (dDelay ){

        pktProcess <- TRUE
        nLost <- 0
        myDelay <- data.frame(time=-1, delay=-1, jitter=-1, nLost=-1)
        myDelay <- myDelay[-1, ]
        tini <- 0
        tend <- 0
        for (itr in c(1:length(dDelay$timeid))) {
           aRec <- dDelay[itr,]
           if (aRec$typeid == 1){
                tini <- as.numeric(aRec$timeid)
                if (!pktProcess ) {
                   nLost <- (nLost + 1)
                   myprt(paste("Packet Lost at time ", aRec$timeid, " lost= ", nLost, sep=""))
                }

                pktProcess <- FALSE 
           }else if (aRec$typeid == 2){

                tend <- as.numeric(aRec$timeid)
                dd <- tend - tini
                jit <- calc_Jitter(dant=myDelay[length(myDelay), 2], dcur=dd)
                myDelay <- rbind(myDelay, c(aRec$timeid, dd, jit, nLost))
                pktProcess <- TRUE
                #myprt(paste("time=", aRec$timeev, " delay=", dd, " Delay Var=", jit, " nLost=", nLost ))
           }
        }
        colnames(myDelay) <- c("time", "delay", "jitter", "nLost")
        return (myDelay)
}

To perform the calculations for delay I use calc_DelayVideo function, neverthless for data frames with a high number of records (~60000) it takes a lot of time.

How can I substitute the for loop with more optimized R functions? Can I use lapply to do such computation? If so, can you provide me an example?

Thanks in advance,

Answer 1

The usual solution is to think hard enough about the problem to find something vectorized.

If that fails, I sometimes resort to re-writing the loop in C++; the Rcpp package can helps with the interface.

Answer 2

The *apply suite of functions are not optimized for loops. Further, I've worked on problems where for loops are faster than apply because apply used more memory and caused my machine to swap.

I would suggest fully initializing the myDelay object and avoid using rbind (which must re-allocate memory):

init <- rep(NA, length(dDelay$timeid))
myDelay <- data.frame(time=init, delay=init, jitter=init, nLost=init)

then replace:

myDelay <- rbind(myDelay, c(aRec$timeid, dd, jit, nLost))

with

myDelay[i,] <- c(aRec$timeid, dd, jit, nLost)