This seems like a trivial question that I can't seem to find a solution for:
Consider the two data.tables
library(data.table)
dt <- data.table(id = c(1,1,1,2,2,2),
                 val = c(10,20,30,10,20,30))
dt1 <- data.table(id = c(1,2),
                  V1 = c(2,1))
How do I subset dt, where dt1 tells me the row number (V1) of the grouped id I need to subset?
For example, here the result will be
#    id val
# 1:  1  20
# 2:  2  10
Update
A quick bit of benchmarking on the proposed solutions
library(data.table)
s <- 100000
set.seed(123)
dt <- data.table(id = rep(seq(1:s), each=10),
                 val = rnorm(n = s*10, 0, 1))
dt1 <- data.table(id = seq(1:s),
                  V1 = sample(1:10, s, replace=T))
library(microbenchmark)
microbenchmark(
  akrun = { dt[dt1, on='id'][, .SD[1:.N==V1] ,id] },
  david = { dt[dt1, val[i.V1], on = 'id', by = .EACHI] },
  symbolix = { dt[, id_seq := seq(1:.N), by=id][dt1, on=c(id_seq = "V1", "id") , nomatch=0] },
   times = 5
 )
#Unit: milliseconds
#     expr         min          lq        mean      median          uq         max neval
#    akrun 17809.51370 17887.89037 18005.32357 18043.80279 18130.78978 18154.62118     5
#    david    48.17367    53.76436    53.79004    54.69096    55.59657    56.72467     5
 #symbolix   507.67312   511.23492   562.59743   571.31160   579.61228   643.15525     5
                Another option is to use by = .EACHI in order to subset val while joing
dt[dt1, val[i.V1], on = 'id', by = .EACHI]
#    id V1
# 1:  1 20
# 2:  2 10
If you have more columns there, you could use .SD[i.V1] instead.
As a side note, in data.table v >= 1.9.8 the .SD[val] operation is scheduled to be fully optimized to use GForce- so hold tight.
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