16  Joins

using DataFrames

a = DataFrame(PID = collect(1:9),
              Hospital = ["UCSF", "HUP", "Stanford",
                          "Stanford", "UCSF", "HUP", 
                          "HUP", "Stanford", "UCSF"],
              Age = [22, 34, 41, 19, 53, 21, 63, 22, 19],
              Sex = [1, 1, 0, 1, 0, 0, 1, 0, 0])

9×4 DataFrame

Row	PID	Hospital	Age	Sex
	Int64	String	Int64	Int64
1	1	UCSF	22	1
2	2	HUP	34	1
3	3	Stanford	41	0
4	4	Stanford	19	1
5	5	UCSF	53	0
6	6	HUP	21	0
7	7	HUP	63	1
8	8	Stanford	22	0
9	9	UCSF	19	0

b = DataFrame(PID = collect(6:12),
              V1 = [153, 89, 112, 228,  91, 190, 101],
              Department = ["Neurology", "Radiology",
                            "Emergency", "Cardiology",
                            "Surgery", "Neurology", "Psychiatry"])

7×3 DataFrame

Row	PID	V1	Department
	Int64	Int64	String
1	6	153	Neurology
2	7	89	Radiology
3	8	112	Emergency
4	9	228	Cardiology
5	10	91	Surgery
6	11	190	Neurology
7	12	101	Psychiatry

16.1 Inner join

ab_inner = innerjoin(a, b, on = :PID)

4×6 DataFrame

Row	PID	Hospital	Age	Sex	V1	Department
	Int64	String	Int64	Int64	Int64	String
1	6	HUP	21	0	153	Neurology
2	7	HUP	63	1	89	Radiology
3	8	Stanford	22	0	112	Emergency
4	9	UCSF	19	0	228	Cardiology

Note that the resulting table only contains cases found in both data frames (i.e. IDs 6 through 9)

16.2 Outer join

ab_outer = outerjoin(a, b, on = :PID)

12×6 DataFrame

Row	PID	Hospital	Age	Sex	V1	Department
	Int64	String?	Int64?	Int64?	Int64?	String?
1	6	HUP	21	0	153	Neurology
2	7	HUP	63	1	89	Radiology
3	8	Stanford	22	0	112	Emergency
4	9	UCSF	19	0	228	Cardiology
5	1	UCSF	22	1	missing	missing
6	2	HUP	34	1	missing	missing
7	3	Stanford	41	0	missing	missing
8	4	Stanford	19	1	missing	missing
9	5	UCSF	53	0	missing	missing
10	10	missing	missing	missing	91	Surgery
11	11	missing	missing	missing	190	Neurology
12	12	missing	missing	missing	101	Psychiatry

Note that the resulting data frame contains all IDs found in either input data frame and missing values are represented with missing

16.3 Left outer join

ab_leftouter = leftjoin(a, b, on = :PID)

9×6 DataFrame

Row	PID	Hospital	Age	Sex	V1	Department
	Int64	String	Int64	Int64	Int64?	String?
1	6	HUP	21	0	153	Neurology
2	7	HUP	63	1	89	Radiology
3	8	Stanford	22	0	112	Emergency
4	9	UCSF	19	0	228	Cardiology
5	1	UCSF	22	1	missing	missing
6	2	HUP	34	1	missing	missing
7	3	Stanford	41	0	missing	missing
8	4	Stanford	19	1	missing	missing
9	5	UCSF	53	0	missing	missing

Note how the resulting data frame contains all IDs present in the left input DataFrame only.

16.4 Right outer join

ab_rightouter = rightjoin(a, b, on = :PID)

7×6 DataFrame

Row	PID	Hospital	Age	Sex	V1	Department
	Int64	String?	Int64?	Int64?	Int64	String
1	6	HUP	21	0	153	Neurology
2	7	HUP	63	1	89	Radiology
3	8	Stanford	22	0	112	Emergency
4	9	UCSF	19	0	228	Cardiology
5	10	missing	missing	missing	91	Surgery
6	11	missing	missing	missing	190	Neurology
7	12	missing	missing	missing	101	Psychiatry

Note how the resulting data frame contains all IDs present in the right input DataFrame only.