merging endpoints of a range with a sequence

Question

In one of my application there is a piece of code that retrieve information from a data.table object depending on values in another.

# say this table contains customers details
dt <- data.table(id=LETTERS[1:4],
                 start=seq(as.Date("2010-01-01"), as.Date("2010-04-01"), "month"),
                 end=seq(as.Date("2010-01-01"), as.Date("2010-04-01"), "month") + c(6,8,10,5),
                 key="id")

# this one has some historical details
dt1 <- data.table(id=rep(LETTERS[1:4], each=120),
                  date=seq(as.Date("2010-01-01"), as.Date("2010-04-30"), "day"),
                  var=rnorm(120),
                  key="id,date")

# and here I finally retrieve my historical information based one customer detail
#
library(data.table)

myfunc <- function(x) {
  # some code
  period <- seq(x$start, x$end, "day")
  dt1[.(x$id, period)][, mean(var)]
  # some code
}

to get the result for all I use adply

library(plyr)
library(microbenchmark)
> adply(dt, 1, myfunc)
   id      start        end         V1
1:  A 2010-01-01 2010-01-07  0.3143536
2:  B 2010-02-01 2010-02-09 -0.5796084
3:  C 2010-03-01 2010-03-11  0.1171404
4:  D 2010-04-01 2010-04-06  0.2384237

> microbenchmark(adply(dt, 1, myfunc))
Unit: milliseconds
                 expr      min       lq   median       uq      max neval
 adply(dt, 1, myfunc) 8.812486 8.998338 9.105776 9.223637 88.14057   100

Do you know a way to avoid the adply call and do the above in one data.table statement? Or anyway a faster method? (title edit suggestion more than welcome, I couldn't think a better one, thanks)

Answer 1

This is a great spot to use the roll argument of data.table:

setkey(dt1, id, date)
setkey(dt, id, start)

dt[dt1, roll = TRUE][end >= start,
   list(start = start[1], end = end[1], result = mean(var)), by = id]

# benchmark
microbenchmark(OP    = adply(dt, 1, myfunc),
               Frank = dt[dt1[as.list(dt[,seq.Date(start,end,"day"),by="id"])][,mean(var),by=id]],
               eddi  = dt[dt1, roll = TRUE][end >= start,list(start = start[1], end = end[1], result = mean(var)), by = id])
#Unit: milliseconds
#  expr       min        lq    median        uq       max neval
#    OP 24.436126 29.184786 30.853094 32.493521 50.898664   100
# Frank  9.115676 11.303691 12.081000 13.122753 28.370415   100
#  eddi  5.336315  6.323643  6.771898  7.497285  9.531376   100

The time difference will become much more dramatic as the size of the datasets grows.

Answer 2

I can give you a bunch of nested [.data.table calls:

set.seed(1)
require(data.table)
# generate dt, dt1 as above
dt[
    dt1[
        as.list(dt[,seq.Date(start,end,"day"),by="id"])
    ][,mean(var),by=id]
]

#    id      start        end          V1
# 1:  A 2010-01-01 2010-01-07  0.04475859
# 2:  B 2010-02-01 2010-02-09 -0.01681972
# 3:  C 2010-03-01 2010-03-11  0.39791318
# 4:  D 2010-04-01 2010-04-06  0.77854732

I'm using as.list to unset the key. I wonder if there's a better way than this...

require(microbenchmark)
require(plyr)
microbenchmark(
    adply=adply(dt, 1, myfunc),
    dtdtdt= dt[dt1[as.list(dt[,seq.Date(start,end,"day"),by="id"])][,mean(var),by=id]]
)

# Unit: milliseconds
#    expr       min        lq    median        uq       max neval
#   adply 12.987334 13.247374 13.477386 14.371258 18.362505   100
#  dtdtdt  4.854708  4.944596  4.993678  5.233507  7.082461   100

EDIT: (eddi) Alternatives to the above that would require one less merge (as discussed in comments) are:

setkey(dt, NULL)

dt1[dt[, list(seq.Date(start,end,"day"), end), by=id]][,
    list(start = date[1], end = end[1], result = mean(var)), by = id]
# or
dt1[dt[, seq.Date(start,end,"day"), by=id]][,
    list(start = date[1], end = date[.N], result = mean(var)), by = id]