CODEWITHSUNDEEP
r
user2840286
user2840286

Reputation: 601

converting arrival time/process to count process in R

I have the data for an arrival process and I want to convert it to count process. This is what I did:

# inter-arrival time in milliseconds
x <- rpareto(100000, location = 10, shape = 1.2)
# arrival time in milliseconds
x.cumsum <- cumsum(x)
# the last arrival
x.max <- max(x.cumsum)
# the time scale for the count data, in this case 1 second
kTimeScale <- 1000

count.length <- ceiling(x.max / kTimeScale)

counts <- rep(0, times = count.length)

for (i in x.cumsum) {
  counts[round(i / kTimeScale)] <- counts[round(i / kTimeScale)] + 1
}

This works but for very large dataset (few millions it's slow). I was wondering if there is a better faster way to do this?

Upvotes: 0

Views: 504

Answers (1)

mrip
mrip

Reputation: 15163

You can do this with table:

countsTable<-table(round(x.cumsum/kTimeScale))
counts[1:10]
##  [1] 24 41  1  2 33 26 20 45 36 19
countsTable[1:10]
## 
##  0  1  2  3  4  5  6  7  8  9 
##  5 24 41  1  2 33 26 20 45 36

The difference is that your function misses the 0 values. The table function won't put in 0 for values where there are no observations but you can do something like this to fix that:

counts2<-rep(0,length(counts)+1)
counts2[as.integer(names(countsTable))+1]<-countsTable
identical(counts,counts2[-1])    
## [1] TRUE

Upvotes: 1

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