Speed-tune a for-loop in R - r

Question

I have read up on vectorization as a solution for speeding up a for-loop. However, the data structure I am creating within a for-loop seems to need to be a data.frame/table.
Here is the scenario:
I have a large table of serial numbers and timestamps. Several timestamps can apply to the same serial number. I only want the latest timestamp for every serial number.
My approach now is to create a vector with unique serial numbers. Then for each loop through this vector, I create a temporary table that holds all observations of a serial number/timestamp combinations ('temp'). I then take the last entry of this temporary table (using tail command) and put it into another table that will eventually hold all unique serial numbers and their latest timestamp ('last.pass'). Finally, I simply remove rows from the starting table serial where number/timestamp combination cannot be found 'last.pass'
Here is my code:
#create list of unique serial numbers found in merged 9000 table
hddsn.unique <- unique(merge.data$HDDSN)
#create empty data.table to populate
last.pass < data.table(HDDSN=as.character(1:length(hddsn.unique)),
ENDDATE=as.character(1:length(hddsn.unique)))
#populate last.pass with the combination of serial numbers and their latest timestamps
for (i in 1:length(hddsn.unique)) {
#create temporary table that finds all serial number/timestamp combinations
temp <- merge.data[merge.data$HDDSN %in% hddsn.unique[i],][,.(HDDSN, ENDDATE)]
#populate last.pass with the latest timestamp record for every serial number
last.pass[i,] <- tail(temp, n=1)
}
match <- which(merge.data[,(merge.data$HDDSN %in% last.pass$HDDSN) &
(merge.data$ENDDATE %in% last.pass$ENDDATE)]==TRUE)
final <- merge.data[match]
My ultimate question is, how do I maintain the automated nature of this script while speeding it up, say, through vectorization or turning it into a function.
Thank you!!!

Answer 1

How about this. Without a clear idea of what your input data looks like, I took a guess.
# make some dummy data with multiple visits per serial
merge.data <- data.frame(HDDSN = 1001:1020,
timestamps = sample(1:9999, 100))
# create a function to find the final visit for a given serial
fun <- function(serial) {
this.serial <- subset(merge.data, HDDSN==serial)
this.serial[which.max(this.serial$timestamps), ]
}
# apply the function to each serial number and clean up the result
final <- as.data.frame(t(sapply(unique(merge.data$HDDSN), fun)))

Answer 2

This data has several ENDDATE for each HDDSN
merge.data <- data.frame(HDDSN = 1001:1100, ENDDATE = sample(9999, 1000))
place it in order, first by HDDSN then by ENDDATE
df = merge.data[do.call("order", merge.data),]
then find the last entry for each HDDSN
df[!duplicated(df[["HDDSN"]], fromLast=TRUE),]
The following illustrate the key steps
> head(df, 12)
HDDSN ENDDATE
701 1001 4
101 1001 101
1 1001 1225
301 1001 2800
201 1001 6051
501 1001 6714
801 1001 6956
601 1001 7894
401 1001 8234
901 1001 8676
802 1002 247
402 1002 274
> head(df[!duplicated(df[["HDDSN"]], fromLast=TRUE),])
HDDSN ENDDATE
901 1001 8676
902 1002 6329
803 1003 9947
204 1004 8825
505 1005 8472
606 1006 9743
If there are composite keys, then look for duplicates on a data.frame rather than a vector, !duplicated(df[, c("key1", "key2")]), as illustrated in the following:
> df = data.frame(k0=c(1:3, 1:6), k1=1:3)
> df[!duplicated(df, fromLast=TRUE),]
k0 k1
1 1 1
2 2 2
3 3 3
7 4 1
8 5 2
9 6 3
(the row numbers are from the original data frame, so rows 4-6 were duplicates). (Some care might need to be taken, especially if one of the columns is numeric, because duplicated.data.frame pastes columns together into a single string and rounding error may creep in).