rolling join with a new twist, the mult=unique

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
r
Domain
data

Research direction

Start with the A and B toy examples in the issue and compare the proposed mult="unique" result with the current rolling join and row-by-row workaround. Define the behavior for unique matching and tolerance, including how unmatched rows are represented; the issue names no source files or tests, so repository-wide join implementation and test discovery will be needed.

Written by the indexing model from the issue text.

Description

non-equi joins

Hello data.table team,

Consider the toy example. I have two tables A and B defined as follows:

> A <- data.table(a=c(1.2,3.4), c1=c(1,2))
> A
     a c1
1: 1.2 1
2: 3.4 2

> B <- data.table(a=c(1.3,3.4,3.5), c2=c(3,4,5))
> B
     a c2
1: 1.3 3
2: 3.4 4
3: 3.5 5

Now a new feature of join would allow us to do this:

> A[B,by="a",roll=1,mult="unique"]
       a  c1 c2
1:  1.3   1   3
2:  3.4   2   4
3:  3.5  NA   5

Note that a conventional rolling join would result in something different:

> A[B,on="a",roll=1]
     a c1 c2
1: 1.3  1  3
2: 3.4  2  4
3: 3.5  2  5

Unfortunately the toy example does not illustrate the usefulness of this feature very well. Consider this: I have two tables from two different sources with slightly different time stamps and some missing data. This feature would allow me to match them up row-by-row (with a specified tolerance) and identify the missing rows in one of the sources.

The most straightforward way to produce the desired result with the current version of data.table is to join A to B one row at a time (in a loop) and to discard the row that was joined from A (if there was one). The proposal above would make this a snap.

Dominant language
R
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