| title | author | output | ||||||
|---|---|---|---|---|---|---|---|---|
Cheatsheet for dplyr join functions |
Jenny Bryan |
|
Examples for those of us who don't speak SQL so good. There are lots of Venn diagrams re: SQL joins on the internet, but I wanted R examples. Those diagrams also utterly fail to show what's really going on vis-a-vis rows AND columns.
Other great places to read about joins:
- The dplyr vignette on Two-table verbs
- The Relational data chapter in R for Data Science. Excellent diagrams.
Working with two small data frames, superheroes and publishers.
library(tidyverse) ## dplyr provides the join functions
superheroes <- "
name, alignment, gender, publisher
Magneto, bad, male, Marvel
Storm, good, female, Marvel
Mystique, bad, female, Marvel
Batman, good, male, DC
Joker, bad, male, DC
Catwoman, bad, female, DC
Hellboy, good, male, Dark Horse Comics
"
superheroes <- read_csv(superheroes, skip = 1)
publishers <- "
publisher, yr_founded
DC, 1934
Marvel, 1939
Image, 1992
"
publishers <- read_csv(publishers, skip = 1)Sorry, cheat sheet does not illustrate "multiple match" situations terribly well.
Sub-plot: watch the row and variable order of the join results for a healthy reminder of why it's dangerous to rely on any of that in an analysis.
inner_join(x, y): Return all rows from x where there are matching values in y, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned. This is a mutating join.
(ijsp <- inner_join(superheroes, publishers))
#> Joining, by = "publisher"
#> # A tibble: 6 x 5
#> name alignment gender publisher yr_founded
#> <chr> <chr> <chr> <chr> <int>
#> 1 Magneto bad male Marvel 1939
#> 2 Storm good female Marvel 1939
#> 3 Mystique bad female Marvel 1939
#> 4 Batman good male DC 1934
#> 5 Joker bad male DC 1934
#> 6 Catwoman bad female DC 1934We lose Hellboy in the join because, although he appears in x = superheroes, his publisher Dark Horse Comics does not appear in y = publishers. The join result has all variables from x = superheroes plus yr_founded, from y.
|
superheroes
name alignment gender publisher Magneto bad male Marvel |
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
inner_join(x = superheroes, y = publishers)
name alignment gender publisher yr_founded Magneto bad male Marvel 1939 Storm good female Marvel 1939 Mystique bad female Marvel 1939 Batman good male DC 1934 Joker bad male DC 1934 Catwoman bad female DC 1934 |
semi_join(x, y): Return all rows from x where there are matching values in y, keeping just columns from x. A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, where a semi join will never duplicate rows of x. This is a filtering join.
(sjsp <- semi_join(superheroes, publishers))
#> Joining, by = "publisher"
#> # A tibble: 6 x 4
#> name alignment gender publisher
#> <chr> <chr> <chr> <chr>
#> 1 Magneto bad male Marvel
#> 2 Storm good female Marvel
#> 3 Mystique bad female Marvel
#> 4 Batman good male DC
#> 5 Joker bad male DC
#> 6 Catwoman bad female DCWe get a similar result as with inner_join() but the join result contains only the variables originally found in x = superheroes. But note the row order has changed.
|
superheroes
name alignment gender publisher Magneto bad male Marvel |
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
semi-join(x = superheroes, y = publishers)
name alignment gender publisher Magneto bad male Marvel |
left_join(x, y): Return all rows from x, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned. This is a mutating join.
(ljsp <- left_join(superheroes, publishers))
#> Joining, by = "publisher"
#> # A tibble: 7 x 5
#> name alignment gender publisher yr_founded
#> <chr> <chr> <chr> <chr> <int>
#> 1 Magneto bad male Marvel 1939
#> 2 Storm good female Marvel 1939
#> 3 Mystique bad female Marvel 1939
#> 4 Batman good male DC 1934
#> 5 Joker bad male DC 1934
#> 6 Catwoman bad female DC 1934
#> 7 Hellboy good male Dark Horse Comics NAWe basically get x = superheroes back, but with the addition of variable yr_founded, which is unique to y = publishers. Hellboy, whose publisher does not appear in y = publishers, has an NA for yr_founded.
|
superheroes
name alignment gender publisher Magneto bad male Marvel |
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
left_join(x = superheroes, y = publishers)
name alignment gender publisher yr_founded Magneto bad male Marvel 1939 Storm good female Marvel 1939 Mystique bad female Marvel 1939 Batman good male DC 1934 Joker bad male DC 1934 Catwoman bad female DC 1934 Hellboy good male Dark Horse Comics NA |
anti_join(x, y): Return all rows from x where there are not matching values in y, keeping just columns from x. This is a filtering join.
(ajsp <- anti_join(superheroes, publishers))
#> Joining, by = "publisher"
#> # A tibble: 1 x 4
#> name alignment gender publisher
#> <chr> <chr> <chr> <chr>
#> 1 Hellboy good male Dark Horse ComicsWe keep only Hellboy now (and do not get yr_founded).
|
superheroes
name alignment gender publisher Magneto bad male Marvel |
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
anti_join(x = superheroes, y = publishers)
name alignment gender publisher Hellboy good male Dark Horse Comics |
inner_join(x, y): Return all rows from x where there are matching values in y, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned. This is a mutating join.
(ijps <- inner_join(publishers, superheroes))
#> Joining, by = "publisher"
#> # A tibble: 6 x 5
#> publisher yr_founded name alignment gender
#> <chr> <int> <chr> <chr> <chr>
#> 1 DC 1934 Batman good male
#> 2 DC 1934 Joker bad male
#> 3 DC 1934 Catwoman bad female
#> 4 Marvel 1939 Magneto bad male
#> 5 Marvel 1939 Storm good female
#> 6 Marvel 1939 Mystique bad femaleIn a way, this does illustrate multiple matches, if you think about it from the x = publishers direction. Every publisher that has a match in y = superheroes appears multiple times in the result, once for each match. In fact, we're getting the same result as with inner_join(superheroes, publishers), up to variable order (which you should also never rely on in an analysis).
|
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
superheroes
name alignment gender publisher Magneto bad male Marvel |
inner_join(x = publishers, y = superheroes)
publisher yr_founded name alignment gender DC 1934 Batman good male |
semi_join(x, y): Return all rows from x where there are matching values in y, keeping just columns from x. A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, where a semi join will never duplicate rows of x. This is a filtering join.
(sjps <- semi_join(x = publishers, y = superheroes))
#> Joining, by = "publisher"
#> # A tibble: 2 x 2
#> publisher yr_founded
#> <chr> <int>
#> 1 DC 1934
#> 2 Marvel 1939Now the effects of switching the x and y roles is more clear. The result resembles x = publishers, but the publisher Image is lost, because there are no observations where publisher == "Image" in y = superheroes.
|
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
superheroes
name alignment gender publisher Magneto bad male Marvel |
semi-join(x = publishers, y = superheroes)
publisher yr_founded DC 1934 Marvel 1939 |
left_join(x, y): Return all rows from x, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned. This is a mutating join.
(ljps <- left_join(publishers, superheroes))
#> Joining, by = "publisher"
#> # A tibble: 7 x 5
#> publisher yr_founded name alignment gender
#> <chr> <int> <chr> <chr> <chr>
#> 1 DC 1934 Batman good male
#> 2 DC 1934 Joker bad male
#> 3 DC 1934 Catwoman bad female
#> 4 Marvel 1939 Magneto bad male
#> 5 Marvel 1939 Storm good female
#> 6 Marvel 1939 Mystique bad female
#> 7 Image 1992 <NA> <NA> <NA>We get a similar result as with inner_join() but the publisher Image survives in the join, even though no superheroes from Image appear in y = superheroes. As a result, Image has NAs for name, alignment, and gender.
|
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
superheroes
name alignment gender publisher Magneto bad male Marvel |
left_join(x = publishers, y = superheroes)
publisher yr_founded name alignment gender DC 1934 Batman good male |
anti_join(x, y): Return all rows from x where there are not matching values in y, keeping just columns from x. This is a filtering join.
(ajps <- anti_join(publishers, superheroes))
#> Joining, by = "publisher"
#> # A tibble: 1 x 2
#> publisher yr_founded
#> <chr> <int>
#> 1 Image 1992We keep only publisher Image now (and the variables found in x = publishers).
|
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
superheroes
name alignment gender publisher Magneto bad male Marvel |
anti_join(x = publishers, y = superheroes)
publisher yr_founded Image 1992 |
full_join(x, y): Return all rows and all columns from both x and y. Where there are not matching values, returns NA for the one missing. This is a mutating join.
(fjsp <- full_join(superheroes, publishers))
#> Joining, by = "publisher"
#> # A tibble: 8 x 5
#> name alignment gender publisher yr_founded
#> <chr> <chr> <chr> <chr> <int>
#> 1 Magneto bad male Marvel 1939
#> 2 Storm good female Marvel 1939
#> 3 Mystique bad female Marvel 1939
#> 4 Batman good male DC 1934
#> 5 Joker bad male DC 1934
#> 6 Catwoman bad female DC 1934
#> 7 Hellboy good male Dark Horse Comics NA
#> 8 <NA> <NA> <NA> Image 1992We get all rows of x = superheroes plus a new row from y = publishers, containing the publisher Image. We get all variables from x = superheroes AND all variables from y = publishers. Any row that derives solely from one table or the other carries NAs in the variables found only in the other table.
|
superheroes
name alignment gender publisher Magneto bad male Marvel |
publishers
publisher yr_founded DC 1934 Marvel 1939 Image 1992 |
full_join(x = superheroes, y = publishers)
name alignment gender publisher yr_founded Magneto bad male Marvel 1939 Storm good female Marvel 1939 Mystique bad female Marvel 1939 Batman good male DC 1934 Joker bad male DC 1934 Catwoman bad female DC 1934 Hellboy good male Dark Horse Comics NA NA NA NA Image 1992 |