r - apply function to each row of a data.table

Question

I'm looking to use data.table to improve speed for a given function, but I'm not sure I'm implementing it the correct way:

Data

Given two data.table s ( dt and dt_lookup )

library(data.table)
set.seed(1234)
t <- seq(1,100); l <- letters; la <- letters[1:13]; lb <- letters[14:26]
n <- 10000
dt <- data.table(id=seq(1:n), 
                 thisTime=sample(t, n, replace=TRUE), 
                 thisLocation=sample(la,n,replace=TRUE),
                 finalLocation=sample(lb,n,replace=TRUE))
setkey(dt, thisLocation)

set.seed(4321)
dt_lookup <- data.table(lkpId = paste0("l-",seq(1,1000)),
                        lkpTime=sample(t, 10000, replace=TRUE),
                        lkpLocation=sample(l, 10000, replace=TRUE))
## NOTE: lkpId is purposly recycled
setkey(dt_lookup, lkpLocation)

I have a function that finds the lkpId that contains both thisLocation and finalLocation , and has the 'nearest' lkpTime (ie the minimum non-negative value of thisTime - lkpTime )

Function

## function to get the 'next' lkpId (i.e. the lkpId with both thisLocation and finalLocation,
## with the minimum non-negative time between thisTime and dt_lookup$lkpTime)
getId <- function(thisTime, thisLocation, finalLocation){

  ## filter lookup based on thisLocation and finalLocation,
  ## and only return values where the lkpId has both 'this' and 'final' locations
  tempThis <- unique(dt_lookup[lkpLocation == thisLocation,lkpId])
  tempFinal <- unique(dt_lookup[lkpLocation == finalLocation,lkpId])
  availServices <- tempThis[tempThis %in% tempFinal]

  tempThisFinal <- dt_lookup[lkpId %in% availServices & lkpLocation==thisLocation, .(lkpId, lkpTime)]

  ## calcualte time difference between 'thisTime' and 'lkpTime' (from thisLocation)
  temp2 <- thisTime - tempThisFinal$lkpTime

  ## take the lkpId with the minimum non-negative difference
  selectedId <- tempThisFinal[min(which(temp2==min(temp2[temp2>0]))),lkpId]
  selectedId
}

Attempts at a solution

I need to get the lkpId for each row of dt . Therefore, my initial instinct was to use an *apply function, but it was taking too long (for me) when n/nrow > 1,000,000 . So I've tried to implement a data.table solution to see if it's faster:

selectedId <- dt[,.(lkpId = getId(thisTime, thisLocation, finalLocation)),by=id]

However, I'm fairly new to data.table , and this method doesn't appear to give any performance gains over an *apply solution:

lkpIds <- apply(dt, 1, function(x){
  thisLocation <- as.character(x[["thisLocation"]])
  finalLocation <- as.character(x[["finalLocation"]])
  thisTime <- as.numeric(x[["thisTime"]])
  myId <- getId(thisTime, thisLocation, finalLocation)
})

both taking ~30 seconds for n = 10,000.

Question

Is there a better way of using data.table to apply the getId function over each row of dt ?

Update 12/08/2015

Thanks to the pointer from @eddi I've redesigned my whole algorithm and am making use of rolling joins ( a good introduction ), thus making proper use of data.table . I'll write up an answer later.

Answer 1

Having spent the time since asking this question looking into what data.table has to offer , researching data.table joins thanks to @eddi's pointer (for example Rolling join on data.table , and inner join with inequality ), I've come up with a solution.

One of the tricky parts was moving away from the thought of 'apply a function to each row', and redesigning the solution to use joins.

And, there will no doubt be better ways of programming this, but here's my attempt.

## want to find a lkpId for each id, that has the minimum difference between 'thisTime' and 'lkpTime'
## and where the lkpId contains both 'thisLocation' and 'finalLocation'

## find all lookup id's where 'thisLocation' matches 'lookupLocation'
## and where thisTime - lkpTime > 0
setkey(dt, thisLocation)
setkey(dt_lookup, lkpLocation)

dt_this <- dt[dt_lookup, {
  idx = thisTime - i.lkpTime > 0
  .(id = id[idx],
    lkpId = i.lkpId,
    thisTime = thisTime[idx],
    lkpTime = i.lkpTime)
},
by=.EACHI]

## remove NAs
dt_this <- dt_this[complete.cases(dt_this)]

## find all matching 'finalLocation' and 'lookupLocaiton'
setkey(dt, finalLocation)
## inner join (and only return the id columns)
dt_final <- dt[dt_lookup, nomatch=0, allow.cartesian=TRUE][,.(id, lkpId)]

## join dt_this to dt_final (as lkpId must have both 'thisLocation' and 'finalLocation')
setkey(dt_this, id, lkpId)
setkey(dt_final, id, lkpId)

dt_join <- dt_this[dt_final, nomatch=0]

## take the combination with the minimum difference between 'thisTime' and 'lkpTime'
dt_join[,timeDiff := thisTime - lkpTime]

dt_join <- dt_join[ dt_join[order(timeDiff), .I[1], by=id]$V1]  

## equivalent dplyr code
# library(dplyr)
# dt_this <- dt_this %>%
#   group_by(id) %>%
#   arrange(timeDiff) %>%
#   slice(1) %>%
#   ungroup