I have:
require(data.table)
dataDT <- data.table(ID = 1:4, Num_Times = c(7, 9, 10, 13))
dataDT # the main data
ID Num_Times
1: 1 7
2: 2 9
3: 3 10
4: 4 13
probabilityDT <- data.table(val = 1:3, prob = c(0.5, 0.3, 0.2))
probabilityDT # the probabilty matrix
val prob
1: 1 0.5
2: 2 0.3
3: 3 0.2
I would like to do the following:
For each row, sample and calculate the sum.
valTemp <- c()
set.seed(999)
for (i in 1:nrow(dataDT)) {
# sample size
num_times <- dataDT[i, Num_Times]
# get samples
Temp1 <- sample(x = probabilityDT[["val"]], size = num_times, replace = TRUE, prob = probabilityDT[["prob"]])
# get sum
Temp1 <- sum(Temp1)
valTemp <- c(valTemp, Temp1)
}
dataDT[, sample_sum := valTemp]
dataDT
ID Num_Times sample_sum
1: 1 7 12
2: 2 9 14
3: 3 10 20
4: 4 13 25
How to do it more efficiently? I have ~500k rows. Can this operation be vectorized at all?