Cheatsheet for dplyr join functions

Why the cheatsheet

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.

The data

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(superheroes, publishers)

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              1934

We 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.

semi_join(superheroes, publishers)

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 DC

We 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.

left_join(superheroes, publishers)

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         NA

We 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.

anti_join(superheroes, publishers)

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 Comics

We keep only Hellboy now (and do not get yr_founded).

inner_join(publishers, superheroes)

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       female

In 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).

semi_join(publishers, superheroes)

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          1939

Now 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.

left_join(publishers, superheroes)

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.

anti_join(publishers, superheroes)

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           1992

We keep only publisher Image now (and the variables found in x = publishers).

full_join(superheroes, publishers)

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                   1992

We 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.