如何最好地将data.table的一列与同一data.table的另一列连接？

Question

My data

I have a data.table DT with the current ( F0YR ) and the next ( F1YR ) fiscal year-end (FYE) encoded as integers. Since every next FYE will eventually become a current FYE, the integer will be both in the column F1YR and F0YR . Also, my data contains monthly observations so the same FYE will be in the data set multiple times:

library(data.table)
DT <- data.table(ID     = rep(c("A", "B"), each=9),
                 MONTH  = rep(100L:108L, times=2),
                 F0YR   = rep(c(1L, 4L, 7L), each=3, times=2),
                 F1YR   = rep(c(4L, 7L, 9L), each=3, times=2),
                 value  = c(rep(1:5, each=3), 6, 6, 7),
                 key    = "ID,F0YR")
DT
      ID MONTH F0YR F1YR value
 [1,]  A   100    1    4     1
 [2,]  A   101    1    4     1
 [3,]  A   102    1    4     1
 [4,]  A   103    4    7     2
 [5,]  A   104    4    7     2
 [6,]  A   105    4    7     2
 [7,]  A   106    7    9     3
 [8,]  A   107    7    9     3
 [9,]  A   108    7    9     3
[10,]  B   100    1    4     4
[11,]  B   101    1    4     4
...

What I want to do

For every ID and F1YR combination, I want to get the value for the ID and F0YR combination. As an example: Company A had a value of 2 for FOYR==4 . Now, I want an additional column for all combinations with ID=="A" and F1YR==4 which is set to 2, next to the already existent value of 1.

What I tried

intDT <- DT[CJ(unique(ID), unique(F0YR)), list(ID, F0YR, valueNew = value), mult="last"]
setkey(intDT, ID, F0YR)
setkey(DT, ID, F1YR)
DT <- intDT[DT]
setnames(DT, c("F0YR.1", "F0YR"), c("F0YR", "F1YR"))
DT
      ID F1YR valueNew MONTH F0YR value
 [1,]  A    4        2   100    1     1
 [2,]  A    4        2   101    1     1
 [3,]  A    4        2   102    1     1
 [4,]  A    7        3   103    4     2
 [5,]  A    7        3   104    4     2
 [6,]  A    7        3   105    4     2
 [7,]  A    9       NA   106    7     3
 [8,]  A    9       NA   107    7     3
 [9,]  A    9       NA   108    7     3
[10,]  B    4        5   100    1     4
[11,]  B    4        5   101    1     4
...

(Note that I use mult="last" here because, although the values should only change with F0YR or F1YR changes, sometimes they don't and this is just my tie breaker).

What I want

This looks improvable. First of all, I have to make a copy of my DT. Second, since I join basically the same data.table , all the column names have the same name and I have to rename them. I thought that a self join would be the way forward, but I tried and tried and couldn't get a nice solution. I have the hope that there is something easy out there which I just don't see...Does anyone have a clue? Or is my data set up in such a way that it is actually hard (maybe because I have monthly observations, but want to join only quarterly or yearly changing values).

Answer 1

In use cases like this, the mantra "aggregate first, then join with that" often helps. So, starting with your DT , and using v1.8.1 :

> agg = DT[,last(value),by=list(ID,F0YR)]
> agg
   ID F0YR V1
1:  A    1  1
2:  A    4  2
3:  A    7  3
4:  B    1  4
5:  B    4  5
6:  B    7  7

I called it agg because I couldn't think of a better name. In this case you wanted last which isn't really an aggregate as such, but you know what I mean.

Then update DT by reference by group. Here we're grouping by i .

setkey(DT,ID,F1YR)
DT[agg,newcol:=V1]
    ID MONTH F0YR F1YR value newcol
 1:  A   100    1    4     1      2
 2:  A   101    1    4     1      2
 3:  A   102    1    4     1      2
 4:  A   103    4    7     2      3
 5:  A   104    4    7     2      3
 6:  A   105    4    7     2      3
 7:  A   106    7    9     3     NA
 8:  A   107    7    9     3     NA
 9:  A   108    7    9     3     NA
10:  B   100    1    4     4      5
11:  B   101    1    4     4      5
12:  B   102    1    4     4      5
13:  B   103    4    7     5      7
14:  B   104    4    7     5      7
15:  B   105    4    7     5      7
16:  B   106    7    9     6     NA
17:  B   107    7    9     6     NA
18:  B   108    7    9     7     NA

Is that right? Not sure I fully followed. Those ops should be very fast, without any copies, and should scale to large data. At least, that's the intention.