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

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