This document discusses using the merge() function in R to perform SQL joins. It shows how merge() can be used to join two datasets (A and B) on the common field "FieldGroup" to get a full outer join, equivalent to a LEFT JOIN in SQL. The default behavior of merge() performs an inner join, but specifying all.y=TRUE ensures all records from the second dataset (B) are kept, even if they don't match the first dataset (A). It also demonstrates how the output can be larger than the inputs if duplicate keys are present.

1. SQL JOINs in R - merge()
- Wayne Tai Lee

2. Question
 A = FieldGroup, SprFert
1, UAN28
2, UAN30
 B = FieldGroup, FieldID, NO3N
1, 2, 22
2, 3, 25
3, 4, 24

3. Question
Want:
 FieldGroup, FieldID, NO3N, SprFert
1, 2, 22, UAN28
2, 3, 25, UAN30
3, 4, 24, NA

4. In SQL
 SELECT
*
FROM
B
LEFT JOIN
A
ON
B.FieldGroup = A.FieldGroup

5. R Equivalent
 output =
merge(x = A, y = B,
all.y = TRUE)

6. R Equivalent
 output =
merge(x = A, y = B,
all.y = TRUE)
 Default merges by
intersect(names(A), names(B))

7. R Equivalent
 output =
merge(x = A, y = B,
all.y = TRUE)
 Default merges by
intersect(names(A), names(B))
 all.y ensures no records from y will be
dropped.

8. Default Join
 output =
merge(A, B)
 Will get:
FieldGroup, FieldID, NO3N, SprFert
1, 2, 22, UAN28
2, 3, 25, UAN30

9. Default Join
 output =
merge(A, B)
 Will get:
FieldGroup, FieldID, NO3N, SprFert
1, 2, 22, UAN28
2, 3, 25, UAN30
 Known as “inner join” where only keep the
records when both tables have a record.

10. Multiple records?
 A = FieldGroup, SprFert
1, UAN28
2, UAN30
 B = FieldGroup, FieldID, NO3N
1, 2, 22
1, 1, 20
2, 3, 25
3, 4, 24

11. Default Join
 output =
merge(A, B)
 Will get:
FieldGroup, FieldID, NO3N, SprFert
1, 2, 22, UAN28
1, 1, 20, UAN28
2, 3, 25, UAN30

12. Good Practices
 Always check the dimensionality of your
data and output
- Your output can be larger than both A and
B when there are duplicate keys.
 Always check your output to make sure it's
not empty
- Merges may drop records so you should
double check