Balancing (creating same number of rows for each individual) data

Question

Given a data.table as follows, id1 is a subject-level ID, id2 is a within-subject repeated-measure ID, X are data variables of which there are many. I want to balance the data such that every individual has the same number of rows (repeated measures), which is the max(DT[,.N,by=id1][,N]), but where id1 and id2 are adjusted as necessary, and X data values are replaced with NA for these new rows.

The following:

DT = data.table(
id1 = c(1,1,2,2,2,3,3,3,3),
id2 = c(1,2,1,2,3,1,2,3,4),
X1 = letters[1:9],
X2 = LETTERS[1:9]
)
setkey(DT,id1)

Should look like:

DT = data.table(
id1 = c(1,1,1,1,2,2,2,2,3,3,3,3),
id2 = c(1,2,3,4,1,2,3,4,1,2,3,4),
X1 = c(letters[1:2],NA,NA,letters[3:5],NA,letters[6:9]),
X2 = c(LETTERS[1:2],NA,NA,LETTERS[3:5],NA,LETTERS[6:9])
)

How do you go about doing this using data.table? For-looping to be avoided as this data-set is huge. Is this a job for reshape2?

Answer 1

You may try:

 DT2 <- CJ(id1=1:3, id2=1:4)
 merge(DT,DT2, by=c('id1', 'id2'), all=TRUE)

Answer 2

Here's a slight variation on akrun's answer that's normally used for the problem at hand:

setkey(DT, id1, id2)
DT[CJ(unique(id1), unique(id2))]