Translating SQL joins on foreign keys to R data.table syntax

Question

The data.table package provides many of the same table handling methods as SQL. If a table has a key, that key consists of one or more columns. But a table can't have more than one key, because it can't be sorted in two different ways at the same time.

In this example, X and Y are data.tables with a single key column "id"; Y also has a non-key column "x_id".

   X <- data.table(id = 1:5, a=4:8,key="id")
   Y <- data.table(id = c(1,1, 3,5,7), x_id=c(1,4:1), key="id")

The following syntax would join the tables on their keys:

  X[Y]

How can I translate the following SQL syntax to data.table code?

  select * from X join Y on X.id = Y.x_id; 

The closest that I have gotten is:

Y[X,list(id, x_id),by = x_id,nomatch=0]

However, this does not do the same inner join as the SQL statement.

---

Here is a more clear example in which the foreign key is y_id, and we want the join to look up values of Y2 where X2$y_id = Y2$id.

    X2 <- data.table(id = 1:5, y_id = c(1,1,2,2,2), key="id")
    Y2 <- data.table(id = 1:5, b = letters[1:5], key="id")

I would like to produce the table:

   id  y_id  b
    1     1 "a"
    2     1 "a"
    3     2 "b"
    4     2 "b"
    5     2 "b"

similar to what is done by the following kludge:

> merge(data.frame(X2), data.frame(Y2), by.x = "y_id", by.y = "id")
  y_id id b
1    1  1 a
2    1  2 a
3    2  3 b
4    2  4 b
5    2  5 b

However, when I do this:

    X2[Y2, 1:2,by = y_id]

I do not get the desired result:

    y_id V1
[1,]    1  1
[2,]    1  2
[3,]    2  1
[4,]    2  2

Answer 1

Good question. Note the following (admittedly buried) in ?data.table :

When i is a data.table, x must have a key. i is joined to x using the key and the rows in x that match are returned. An equi-join is performed between each column in i to each column in x's key. The match is a binary search in compiled C in O(log n) time. If i has less columns than x's key then many rows of x may match to each row of i. If i has more columns than x's key, the columns of i not involved in the join are included in the result. If i also has a key, it is i's key columns that are used to match to x's key columns and a binary merge of the two tables is carried out.

So, the key here is that i doesn't have to be keyed. Only x must be keyed.

X2 <- data.table(id = 11:15, y_id = c(14,14,11,12,12), key="id")
     id y_id
[1,] 11   14
[2,] 12   14
[3,] 13   11
[4,] 14   12
[5,] 15   12
Y2 <- data.table(id = 11:15, b = letters[1:5], key="id")
     id b
[1,] 11 a
[2,] 12 b
[3,] 13 c
[4,] 14 d
[5,] 15 e
Y2[J(X2$y_id)]  # binary search for each item of (unsorted and unkeyed) i
     id b
[1,] 14 d
[2,] 14 d
[3,] 11 a
[4,] 12 b
[5,] 12 b

or,

Y2[SJ(X2$y_id)]  # binary merge of keyed i, see ?SJ
     id b
[1,] 11 a
[2,] 12 b
[3,] 12 b
[4,] 14 d
[5,] 14 d

identical(Y2[J(X2$y_id)], Y2[X2$y_id])
[1] FALSE