Cheatsheet for dplyr join functions

Jenny Bryan
11 September, 2014

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 interwebs, but I wanted R examples.

Full documentation for the dplyr package, which is developed by Hadley Wickham and Romain Francois on GitHub.

Working with two small data.frames, superheroes and publishers.

suppressPackageStartupMessages(library(dplyr))

superheroes <-
  c("    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(text = superheroes, strip.white = TRUE)

publishers <- 
  c("publisher, yr_founded",
    "       DC,       1934",
    "   Marvel,       1939",
    "    Image,       1992")
publishers <- read.csv(text = publishers, strip.white = TRUE)

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

(ijsp <- inner_join(superheroes, publishers))

## Joining by: "publisher"

##       name alignment gender publisher yr_founded
## 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.

(sjsp <- semi_join(superheroes, publishers))

## Joining by: "publisher"

##       name alignment gender publisher
## 1   Batman      good   male        DC
## 2    Joker       bad   male        DC
## 3 Catwoman       bad female        DC
## 4  Magneto       bad   male    Marvel
## 5    Storm      good female    Marvel
## 6 Mystique       bad female    Marvel

We get a similar result as with inner_join() but the join result contains only the variables originally found in x = superheroes.

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

(ljsp <- left_join(superheroes, publishers))

## Joining by: "publisher"

##       name alignment gender         publisher yr_founded
## 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

(ajsp <- anti_join(superheroes, publishers))

## Joining by: "publisher"

##      name alignment gender         publisher
## 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

(ijps <- inner_join(publishers, superheroes))

## Joining by: "publisher"

##   publisher yr_founded     name alignment gender
## 1    Marvel       1939  Magneto       bad   male
## 2    Marvel       1939    Storm      good female
## 3    Marvel       1939 Mystique       bad female
## 4        DC       1934   Batman      good   male
## 5        DC       1934    Joker       bad   male
## 6        DC       1934 Catwoman       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.

(sjps <- semi_join(x = publishers, y = superheroes))

## Joining by: "publisher"

##   publisher yr_founded
## 1    Marvel       1939
## 2        DC       1934

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

(ljps <- left_join(publishers, superheroes))

## Joining by: "publisher"

##   publisher yr_founded     name alignment gender
## 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

(ajps <- anti_join(publishers, superheroes))

## Joining by: "publisher"

##   publisher yr_founded
## 1     Image       1992

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

NOT dplyr: merge(superheroes, publishers, all = TRUE)

What if you want to merge two data.frames and keep rows that appear in either? In SQL jargon, this is an outer join and is not yet implemented in dplyr, though it will come. In the meantime, you could use merge() from base R.

merge(x, y): Merge two data frames by common columns or row names, or do other versions of database join operations

(OJsp <- merge(superheroes, publishers, all = TRUE))

##           publisher     name alignment gender yr_founded
## 1 Dark Horse Comics  Hellboy      good   male         NA
## 2                DC   Batman      good   male       1934
## 3                DC    Joker       bad   male       1934
## 4                DC Catwoman       bad female       1934
## 5            Marvel  Magneto       bad   male       1939
## 6            Marvel    Storm      good female       1939
## 7            Marvel Mystique       bad female       1939
## 8             Image     <NA>      <NA>   <NA>       1992

We keep Hellboy (whose publisher Dark Horse Comics is not in publishers) and Image (a publisher with no superheroes in superheroes) and get variables from both data.frames. Therefore observations for which there is no match in the two data.frames carry NAs in the variables from the other data source.

sessionInfo()

sessionInfo()

## R version 3.1.0 (2014-04-10)
## Platform: x86_64-apple-darwin10.8.0 (64-bit)
## 
## locale:
## [1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] dplyr_0.2.0.99
## 
## loaded via a namespace (and not attached):
##  [1] assertthat_0.1   digest_0.6.4     evaluate_0.5.5   formatR_0.10    
##  [5] htmltools_0.2.4  knitr_1.6        magrittr_1.0.1   parallel_3.1.0  
##  [9] Rcpp_0.11.1      rmarkdown_0.2.64 stringr_0.6.2    tools_3.1.0     
## [13] yaml_2.1.13