19  Joins

19.1 Introduction

It’s rare that a data analysis involves only a single data frame. Typically you have many data frames, and you must join them together to answer the questions that you’re interested in. This chapter will introduce you to two important types of joins:

  • Mutating joins, which add new variables to one data frame from matching observations in another.
  • Filtering joins, which filter observations from one data frame based on whether or not they match an observation in another.

We’ll begin by discussing keys, the variables used to connect a pair of data frames in a join. We cement the theory with an examination of the keys in the datasets from the nycflights13 package, then use that knowledge to start joining data frames together. Next we’ll discuss how joins work, focusing on their action on the rows. We’ll finish up with a discussion of non-equi joins, a family of joins that provide a more flexible way of matching keys than the default equality relationship.

19.1.1 Prerequisites

In this chapter, we’ll explore the five related datasets from nycflights13 using the join functions from dplyr.

19.2 Keys

To understand joins, you need to first understand how two tables can be connected through a pair of keys, within each table. In this section, you’ll learn about the two types of key and see examples of both in the datasets of the nycflights13 package. You’ll also learn how to check that your keys are valid, and what to do if your table lacks a key.

19.2.1 Primary and foreign keys

Every join involves a pair of keys: a primary key and a foreign key. A primary key is a variable or set of variables that uniquely identifies each observation. When more than one variable is needed, the key is called a compound key. For example, in nycflights13:

  • airlines records two pieces of data about each airline: its carrier code and its full name. You can identify an airline with its two letter carrier code, making carrier the primary key.

    airlines
#> # A tibble: 16 × 2
#>   carrier name                    
#>   <chr>   <chr>                   
#> 1 9E      Endeavor Air Inc.       
#> 2 AA      American Airlines Inc.  
#> 3 AS      Alaska Airlines Inc.    
#> 4 B6      JetBlue Airways         
#> 5 DL      Delta Air Lines Inc.    
#> 6 EV      ExpressJet Airlines Inc.
#> # ℹ 10 more rows

  • airports records data about each airport. You can identify each airport by its three letter airport code, making faa the primary key.

    airports
#> # A tibble: 1,458 × 8
#>   faa   name                            lat   lon   alt    tz dst  
#>   <chr> <chr>                         <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 04G   Lansdowne Airport              41.1 -80.6  1044    -5 A    
#> 2 06A   Moton Field Municipal Airport  32.5 -85.7   264    -6 A    
#> 3 06C   Schaumburg Regional            42.0 -88.1   801    -6 A    
#> 4 06N   Randall Airport                41.4 -74.4   523    -5 A    
#> 5 09J   Jekyll Island Airport          31.1 -81.4    11    -5 A    
#> 6 0A9   Elizabethton Municipal Airpo…  36.4 -82.2  1593    -5 A    
#> # ℹ 1,452 more rows
#> # ℹ 1 more variable: tzone <chr>

  • planes records data about each plane. You can identify a plane by its tail number, making tailnum the primary key.

    planes
#> # A tibble: 3,322 × 9
#>   tailnum  year type              manufacturer    model     engines
#>   <chr>   <int> <chr>             <chr>           <chr>       <int>
#> 1 N10156   2004 Fixed wing multi… EMBRAER         EMB-145XR       2
#> 2 N102UW   1998 Fixed wing multi… AIRBUS INDUSTR… A320-214        2
#> 3 N103US   1999 Fixed wing multi… AIRBUS INDUSTR… A320-214        2
#> 4 N104UW   1999 Fixed wing multi… AIRBUS INDUSTR… A320-214        2
#> 5 N10575   2002 Fixed wing multi… EMBRAER         EMB-145LR       2
#> 6 N105UW   1999 Fixed wing multi… AIRBUS INDUSTR… A320-214        2
#> # ℹ 3,316 more rows
#> # ℹ 3 more variables: seats <int>, speed <int>, engine <chr>

  • weather records data about the weather at the origin airports. You can identify each observation by the combination of location and time, making origin and time_hour the compound primary key.

    weather
#> # A tibble: 26,115 × 15
#>   origin  year month   day  hour  temp  dewp humid wind_dir
#>   <chr>  <int> <int> <int> <int> <dbl> <dbl> <dbl>    <dbl>
#> 1 EWR     2013     1     1     1  39.0  26.1  59.4      270
#> 2 EWR     2013     1     1     2  39.0  27.0  61.6      250
#> 3 EWR     2013     1     1     3  39.0  28.0  64.4      240
#> 4 EWR     2013     1     1     4  39.9  28.0  62.2      250
#> 5 EWR     2013     1     1     5  39.0  28.0  64.4      260
#> 6 EWR     2013     1     1     6  37.9  28.0  67.2      240
#> # ℹ 26,109 more rows
#> # ℹ 6 more variables: wind_speed <dbl>, wind_gust <dbl>, …

A foreign key is a variable (or set of variables) that corresponds to a primary key in another table. For example:

  • flights$tailnum is a foreign key that corresponds to the primary key planes$tailnum.
  • flights$carrier is a foreign key that corresponds to the primary key airlines$carrier.
  • flights$origin is a foreign key that corresponds to the primary key airports$faa.
  • flights$dest is a foreign key that corresponds to the primary key airports$faa.
  • flights$origin-flights$time_hour is a compound foreign key that corresponds to the compound primary key weather$origin-weather$time_hour.

These relationships are summarized visually in Figure 19.1.

The relationships between airports, planes, flights, weather, and airlines datasets from the nycflights13 package. airports$faa connected to the flights$origin and flights$dest. planes$tailnum is connected to the flights$tailnum. weather$time_hour and weather$origin are jointly connected to flights$time_hour and flights$origin. airlines$carrier is connected to flights$carrier. There are no direct connections between airports, planes, airlines, and weather data frames.
Figure 19.1: Connections between all five data frames in the nycflights13 package. Variables making up a primary key are colored grey, and are connected to their corresponding foreign keys with arrows.

You’ll notice a nice feature in the design of these keys: the primary and foreign keys almost always have the same names, which, as you’ll see shortly, will make your joining life much easier. It’s also worth noting the opposite relationship: almost every variable name used in multiple tables has the same meaning in each place. There’s only one exception: year means year of departure in flights and year manufactured in planes. This will become important when we start actually joining tables together.

19.2.2 Checking primary keys

Now that that we’ve identified the primary keys in each table, it’s good practice to verify that they do indeed uniquely identify each observation. One way to do that is to count() the primary keys and look for entries where n is greater than one. This reveals that planes and weather both look good:

planes |> 
  count(tailnum) |> 
  filter(n > 1)
#> # A tibble: 0 × 2
#> # ℹ 2 variables: tailnum <chr>, n <int>

weather |> 
  count(time_hour, origin) |> 
  filter(n > 1)
#> # A tibble: 0 × 3
#> # ℹ 3 variables: time_hour <dttm>, origin <chr>, n <int>

You should also check for missing values in your primary keys — if a value is missing then it can’t identify an observation!

planes |> 
  filter(is.na(tailnum))
#> # A tibble: 0 × 9
#> # ℹ 9 variables: tailnum <chr>, year <int>, type <chr>, manufacturer <chr>,
#> #   model <chr>, engines <int>, seats <int>, speed <int>, engine <chr>

weather |> 
  filter(is.na(time_hour) | is.na(origin))
#> # A tibble: 0 × 15
#> # ℹ 15 variables: origin <chr>, year <int>, month <int>, day <int>,
#> #   hour <int>, temp <dbl>, dewp <dbl>, humid <dbl>, wind_dir <dbl>, …

19.2.3 Surrogate keys

So far we haven’t talked about the primary key for flights. It’s not super important here, because there are no data frames that use it as a foreign key, but it’s still useful to consider because it’s easier to work with observations if we have some way to describe them to others.

After a little thinking and experimentation, we determined that there are three variables that together uniquely identify each flight:

flights |> 
  count(time_hour, carrier, flight) |> 
  filter(n > 1)
#> # A tibble: 0 × 4
#> # ℹ 4 variables: time_hour <dttm>, carrier <chr>, flight <int>, n <int>

Does the absence of duplicates automatically make time_hour-carrier-flight a primary key? It’s certainly a good start, but it doesn’t guarantee it. For example, are altitude and latitude a good primary key for airports?

airports |>
  count(alt, lat) |> 
  filter(n > 1)
#> # A tibble: 1 × 3
#>     alt   lat     n
#>   <dbl> <dbl> <int>
#> 1    13  40.6     2

Identifying an airport by its altitude and latitude is clearly a bad idea, and in general it’s not possible to know from the data alone whether or not a combination of variables makes a good a primary key. But for flights, the combination of time_hour, carrier, and flight seems reasonable because it would be really confusing for an airline and its customers if there were multiple flights with the same flight number in the air at the same time.

That said, we might be better off introducing a simple numeric surrogate key using the row number:

flights2 <- flights |> 
  mutate(id = row_number(), .before = 1)
flights2
#> # A tibble: 336,776 × 20
#>      id  year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1     1  2013     1     1      517            515         2      830
#> 2     2  2013     1     1      533            529         4      850
#> 3     3  2013     1     1      542            540         2      923
#> 4     4  2013     1     1      544            545        -1     1004
#> 5     5  2013     1     1      554            600        -6      812
#> 6     6  2013     1     1      554            558        -4      740
#> # ℹ 336,770 more rows
#> # ℹ 12 more variables: sched_arr_time <int>, arr_delay <dbl>, …

Surrogate keys can be particularly useful when communicating to other humans: it’s much easier to tell someone to take a look at flight 2001 than to say look at UA430 which departed 9am 2013-01-03.

19.2.4 Exercises

  1. We forgot to draw the relationship between weather and airports in Figure 19.1. What is the relationship and how should it appear in the diagram?

  2. weather only contains information for the three origin airports in NYC. If it contained weather records for all airports in the USA, what additional connection would it make to flights?

  3. The year, month, day, hour, and origin variables almost form a compound key for weather, but there’s one hour that has duplicate observations. Can you figure out what’s special about that hour?

  4. We know that some days of the year are special and fewer people than usual fly on them (e.g., Christmas eve and Christmas day). How might you represent that data as a data frame? What would be the primary key? How would it connect to the existing data frames?

  5. Draw a diagram illustrating the connections between the Batting, People, and Salaries data frames in the Lahman package. Draw another diagram that shows the relationship between People, Managers, AwardsManagers. How would you characterize the relationship between the Batting, Pitching, and Fielding data frames?

19.3 Basic joins

Now that you understand how data frames are connected via keys, we can start using joins to better understand the flights dataset. dplyr provides six join functions: left_join(), inner_join(), right_join(), full_join(), semi_join(), and anti_join(). They all have the same interface: they take a pair of data frames (x and y) and return a data frame. The order of the rows and columns in the output is primarily determined by x.

In this section, you’ll learn how to use one mutating join, left_join(), and two filtering joins, semi_join() and anti_join(). In the next section, you’ll learn exactly how these functions work, and about the remaining inner_join(), right_join() and full_join().

19.3.1 Mutating joins

A mutating join allows you to combine variables from two data frames: it first matches observations by their keys, then copies across variables from one data frame to the other. Like mutate(), the join functions add variables to the right, so if your dataset has many variables, you won’t see the new ones. For these examples, we’ll make it easier to see what’s going on by creating a narrower dataset with just six variables1:

flights2 <- flights |> 
  select(year, time_hour, origin, dest, tailnum, carrier)
flights2
#> # A tibble: 336,776 × 6
#>    year time_hour           origin dest  tailnum carrier
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>  
#> 1  2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA     
#> 2  2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA     
#> 3  2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA     
#> 4  2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6     
#> 5  2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL     
#> 6  2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA     
#> # ℹ 336,770 more rows

There are four types of mutating join, but there’s one that you’ll use almost all of the time: left_join(). It’s special because the output will always have the same rows as x, the data frame you’re joining to2. The primary use of left_join() is to add in additional metadata. For example, we can use left_join() to add the full airline name to the flights2 data:

flights2 |>
  left_join(airlines)
#> Joining with `by = join_by(carrier)`
#> # A tibble: 336,776 × 7
#>    year time_hour           origin dest  tailnum carrier name                
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>   <chr>               
#> 1  2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA      United Air Lines In…
#> 2  2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA      United Air Lines In…
#> 3  2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA      American Airlines I…
#> 4  2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6      JetBlue Airways     
#> 5  2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL      Delta Air Lines Inc.
#> 6  2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA      United Air Lines In…
#> # ℹ 336,770 more rows

Or we could find out the temperature and wind speed when each plane departed:

flights2 |> 
  left_join(weather |> select(origin, time_hour, temp, wind_speed))
#> Joining with `by = join_by(time_hour, origin)`
#> # A tibble: 336,776 × 8
#>    year time_hour           origin dest  tailnum carrier  temp wind_speed
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>   <dbl>      <dbl>
#> 1  2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA       39.0       12.7
#> 2  2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA       39.9       15.0
#> 3  2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA       39.0       15.0
#> 4  2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6       39.0       15.0
#> 5  2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL       39.9       16.1
#> 6  2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA       39.0       12.7
#> # ℹ 336,770 more rows

Or what size of plane was flying:

flights2 |> 
  left_join(planes |> select(tailnum, type, engines, seats))
#> Joining with `by = join_by(tailnum)`
#> # A tibble: 336,776 × 9
#>    year time_hour           origin dest  tailnum carrier type                
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>   <chr>               
#> 1  2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA      Fixed wing multi en…
#> 2  2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA      Fixed wing multi en…
#> 3  2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA      Fixed wing multi en…
#> 4  2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6      Fixed wing multi en…
#> 5  2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL      Fixed wing multi en…
#> 6  2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA      Fixed wing multi en…
#> # ℹ 336,770 more rows
#> # ℹ 2 more variables: engines <int>, seats <int>

When left_join() fails to find a match for a row in x, it fills in the new variables with missing values. For example, there’s no information about the plane with tail number N3ALAA so the type, engines, and seats will be missing:

flights2 |> 
  filter(tailnum == "N3ALAA") |> 
  left_join(planes |> select(tailnum, type, engines, seats))
#> Joining with `by = join_by(tailnum)`
#> # A tibble: 63 × 9
#>    year time_hour           origin dest  tailnum carrier type  engines seats
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>   <chr>   <int> <int>
#> 1  2013 2013-01-01 06:00:00 LGA    ORD   N3ALAA  AA      <NA>       NA    NA
#> 2  2013 2013-01-02 18:00:00 LGA    ORD   N3ALAA  AA      <NA>       NA    NA
#> 3  2013 2013-01-03 06:00:00 LGA    ORD   N3ALAA  AA      <NA>       NA    NA
#> 4  2013 2013-01-07 19:00:00 LGA    ORD   N3ALAA  AA      <NA>       NA    NA
#> 5  2013 2013-01-08 17:00:00 JFK    ORD   N3ALAA  AA      <NA>       NA    NA
#> 6  2013 2013-01-16 06:00:00 LGA    ORD   N3ALAA  AA      <NA>       NA    NA
#> # ℹ 57 more rows

We’ll come back to this problem a few times in the rest of the chapter.

19.3.2 Specifying join keys

By default, left_join() will use all variables that appear in both data frames as the join key, the so called natural join. This is a useful heuristic, but it doesn’t always work. For example, what happens if we try to join flights2 with the complete planes dataset?

flights2 |> 
  left_join(planes)
#> Joining with `by = join_by(year, tailnum)`
#> # A tibble: 336,776 × 13
#>    year time_hour           origin dest  tailnum carrier type  manufacturer
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>   <chr> <chr>       
#> 1  2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA      <NA>  <NA>        
#> 2  2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA      <NA>  <NA>        
#> 3  2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA      <NA>  <NA>        
#> 4  2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6      <NA>  <NA>        
#> 5  2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL      <NA>  <NA>        
#> 6  2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA      <NA>  <NA>        
#> # ℹ 336,770 more rows
#> # ℹ 5 more variables: model <chr>, engines <int>, seats <int>, …

We get a lot of missing matches because our join is trying to use tailnum and year as a compound key. Both flights and planes have a year column but they mean different things: flights$year is the year the flight occurred and planes$year is the year the plane was built. We only want to join on tailnum so we need to provide an explicit specification with join_by():

flights2 |> 
  left_join(planes, join_by(tailnum))
#> # A tibble: 336,776 × 14
#>   year.x time_hour           origin dest  tailnum carrier year.y
#>    <int> <dttm>              <chr>  <chr> <chr>   <chr>    <int>
#> 1   2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA        1999
#> 2   2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA        1998
#> 3   2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA        1990
#> 4   2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6        2012
#> 5   2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL        1991
#> 6   2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA        2012
#> # ℹ 336,770 more rows
#> # ℹ 7 more variables: type <chr>, manufacturer <chr>, model <chr>, …

Note that the year variables are disambiguated in the output with a suffix (year.x and year.y), which tells you whether the variable came from the x or y argument. You can override the default suffixes with the suffix argument.

join_by(tailnum) is short for join_by(tailnum == tailnum). It’s important to know about this fuller form for two reasons. Firstly, it describes the relationship between the two tables: the keys must be equal. That’s why this type of join is often called an equi join. You’ll learn about non-equi joins in Section 19.5.

Secondly, it’s how you specify different join keys in each table. For example, there are two ways to join the flight2 and airports table: either by dest or origin:

flights2 |> 
  left_join(airports, join_by(dest == faa))
#> # A tibble: 336,776 × 13
#>    year time_hour           origin dest  tailnum carrier name                
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>   <chr>               
#> 1  2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA      George Bush Interco…
#> 2  2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA      George Bush Interco…
#> 3  2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA      Miami Intl          
#> 4  2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6      <NA>                
#> 5  2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL      Hartsfield Jackson …
#> 6  2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA      Chicago Ohare Intl  
#> # ℹ 336,770 more rows
#> # ℹ 6 more variables: lat <dbl>, lon <dbl>, alt <dbl>, tz <dbl>, …

flights2 |> 
  left_join(airports, join_by(origin == faa))
#> # A tibble: 336,776 × 13
#>    year time_hour           origin dest  tailnum carrier name               
#>   <int> <dttm>              <chr>  <chr> <chr>   <chr>   <chr>              
#> 1  2013 2013-01-01 05:00:00 EWR    IAH   N14228  UA      Newark Liberty Intl
#> 2  2013 2013-01-01 05:00:00 LGA    IAH   N24211  UA      La Guardia         
#> 3  2013 2013-01-01 05:00:00 JFK    MIA   N619AA  AA      John F Kennedy Intl
#> 4  2013 2013-01-01 05:00:00 JFK    BQN   N804JB  B6      John F Kennedy Intl
#> 5  2013 2013-01-01 06:00:00 LGA    ATL   N668DN  DL      La Guardia         
#> 6  2013 2013-01-01 05:00:00 EWR    ORD   N39463  UA      Newark Liberty Intl
#> # ℹ 336,770 more rows
#> # ℹ 6 more variables: lat <dbl>, lon <dbl>, alt <dbl>, tz <dbl>, …

In older code you might see a different way of specifying the join keys, using a character vector:

  • by = "x" corresponds to join_by(x).
  • by = c("a" = "x") corresponds to join_by(a == x).

Now that it exists, we prefer join_by() since it provides a clearer and more flexible specification.

inner_join(), right_join(), full_join() have the same interface as left_join(). The difference is which rows they keep: left join keeps all the rows in x, the right join keeps all rows in y, the full join keeps all rows in either x or y, and the inner join only keeps rows that occur in both x and y. We’ll come back to these in more detail later.

19.3.3 Filtering joins

As you might guess the primary action of a filtering join is to filter the rows. There are two types: semi-joins and anti-joins. Semi-joins keep all rows in x that have a match in y. For example, we could use a semi-join to filter the airports dataset to show just the origin airports:

airports |> 
  semi_join(flights2, join_by(faa == origin))
#> # A tibble: 3 × 8
#>   faa   name                  lat   lon   alt    tz dst   tzone           
#>   <chr> <chr>               <dbl> <dbl> <dbl> <dbl> <chr> <chr>           
#> 1 EWR   Newark Liberty Intl  40.7 -74.2    18    -5 A     America/New_York
#> 2 JFK   John F Kennedy Intl  40.6 -73.8    13    -5 A     America/New_York
#> 3 LGA   La Guardia           40.8 -73.9    22    -5 A     America/New_York

Or just the destinations:

airports |> 
  semi_join(flights2, join_by(faa == dest))
#> # A tibble: 101 × 8
#>   faa   name                     lat    lon   alt    tz dst   tzone          
#>   <chr> <chr>                  <dbl>  <dbl> <dbl> <dbl> <chr> <chr>          
#> 1 ABQ   Albuquerque Internati…  35.0 -107.   5355    -7 A     America/Denver 
#> 2 ACK   Nantucket Mem           41.3  -70.1    48    -5 A     America/New_Yo…
#> 3 ALB   Albany Intl             42.7  -73.8   285    -5 A     America/New_Yo…
#> 4 ANC   Ted Stevens Anchorage…  61.2 -150.    152    -9 A     America/Anchor…
#> 5 ATL   Hartsfield Jackson At…  33.6  -84.4  1026    -5 A     America/New_Yo…
#> 6 AUS   Austin Bergstrom Intl   30.2  -97.7   542    -6 A     America/Chicago
#> # ℹ 95 more rows

Anti-joins are the opposite: they return all rows in x that don’t have a match in y. They’re useful for finding missing values that are implicit in the data, the topic of Section 18.3. Implicitly missing values don’t show up as NAs but instead only exist as an absence. For example, we can find rows that are missing from airports by looking for flights that don’t have a matching destination airport:

flights2 |> 
  anti_join(airports, join_by(dest == faa)) |> 
  distinct(dest)
#> # A tibble: 4 × 1
#>   dest 
#>   <chr>
#> 1 BQN  
#> 2 SJU  
#> 3 STT  
#> 4 PSE

Or we can find which tailnums are missing from planes:

flights2 |>
  anti_join(planes, join_by(tailnum)) |> 
  distinct(tailnum)
#> # A tibble: 722 × 1
#>   tailnum
#>   <chr>  
#> 1 N3ALAA 
#> 2 N3DUAA 
#> 3 N542MQ 
#> 4 N730MQ 
#> 5 N9EAMQ 
#> 6 N532UA 
#> # ℹ 716 more rows

19.3.4 Exercises

  1. Find the 48 hours (over the course of the whole year) that have the worst delays. Cross-reference it with the weather data. Can you see any patterns?

  2. Imagine you’ve found the top 10 most popular destinations using this code:

    top_dest <- flights2 |>
  count(dest, sort = TRUE) |>
  head(10)

    How can you find all flights to those destinations?

  3. Does every departing flight have corresponding weather data for that hour?

  4. What do the tail numbers that don’t have a matching record in planes have in common? (Hint: one variable explains ~90% of the problems.)

  5. Add a column to planes that lists every carrier that has flown that plane. You might expect that there’s an implicit relationship between plane and airline, because each plane is flown by a single airline. Confirm or reject this hypothesis using the tools you’ve learned in previous chapters.

  6. Add the latitude and the longitude of the origin and destination airport to flights. Is it easier to rename the columns before or after the join?

  7. Compute the average delay by destination, then join on the airports data frame so you can show the spatial distribution of delays. Here’s an easy way to draw a map of the United States:

    airports |>
  semi_join(flights, join_by(faa == dest)) |>
  ggplot(aes(x = lon, y = lat)) +
    borders("state") +
    geom_point() +
    coord_quickmap()

    You might want to use the size or color of the points to display the average delay for each airport.

  8. What happened on June 13 2013? Draw a map of the delays, and then use Google to cross-reference with the weather.

19.4 How do joins work?

Now that you’ve used joins a few times it’s time to learn more about how they work, focusing on how each row in x matches rows in y. We’ll begin by introducing a visual representation of joins, using the simple tibbles defined below and shown in Figure 19.2. In these examples we’ll use a single key called key and a single value column (val_x and val_y), but the ideas all generalize to multiple keys and multiple values.

x <- tribble(
  ~key, ~val_x,
     1, "x1",
     2, "x2",
     3, "x3"
)
y <- tribble(
  ~key, ~val_y,
     1, "y1",
     2, "y2",
     4, "y3"
)
x and y are two data frames with 2 columns and 3 rows, with contents as described in the text. The values of the keys are colored: 1 is green, 2 is purple, 3 is orange, and 4 is yellow.
Figure 19.2: Graphical representation of two simple tables. The colored key columns map background color to key value. The grey columns represent the “value” columns that are carried along for the ride.

Figure 19.3 introduces the foundation for our visual representation. It shows all potential matches between x and y as the intersection between lines drawn from each row of x and each row of y. The rows and columns in the output are primarily determined by x, so the x table is horizontal and lines up with the output.

x and y are placed at right-angles, with horizonal lines extending from x and vertical lines extending from y. There are 3 rows in x and 3 rows in y, which leads to nine intersections representing nine potential matches.
Figure 19.3: To understand how joins work, it’s useful to think of every possible match. Here we show that with a grid of connecting lines.

To describe a specific type of join, we indicate matches with dots. The matches determine the rows in the output, a new data frame that contains the key, the x values, and the y values. For example, Figure 19.4 shows an inner join, where rows are retained if and only if the keys are equal.

x and y are placed at right-angles with lines forming a grid of potential matches. Keys 1 and 2 appear in both x and y, so we get a match, indicated by a dot. Each dot corresponds to a row in the output, so the resulting joined data frame has two rows.
Figure 19.4: An inner join matches each row in x to the row in y that has the same value of key. Each match becomes a row in the output.

We can apply the same principles to explain the outer joins, which keep observations that appear in at least one of the data frames. These joins work by adding an additional “virtual” observation to each data frame. This observation has a key that matches if no other key matches, and values filled with NA. There are three types of outer joins:

  • A left join keeps all observations in x, Figure 19.5. Every row of x is preserved in the output because it can fall back to matching a row of NAs in y.

    Compared to the previous diagram showing an inner join, the y table gets a new virtual row containin NA that will match any row in x that didn't otherwise match. This means that the output now has three rows. For key = 3, which matches this virtual row, val_y takes value NA.
    Figure 19.5: A visual representation of the left join where every row in x appears in the output.

  • A right join keeps all observations in y, Figure 19.6. Every row of y is preserved in the output because it can fall back to matching a row of NAs in x. The output still matches x as much as possible; any extra rows from y are added to the end.

    Compared to the previous diagram showing an left join, the x table now gains a virtual row so that every row in y gets a match in x. val_x contains NA for the row in y that didn't match x.
    Figure 19.6: A visual representation of the right join where every row of y appears in the output.

  • A full join keeps all observations that appear in x or y, Figure 19.7. Every row of x and y is included in the output because both x and y have a fall back row of NAs. Again, the output starts with all rows from x, followed by the remaining unmatched y rows.

    Now both x and y have a virtual row that always matches. The result has 4 rows: keys 1, 2, 3, and 4 with all values from val_x and val_y, however key 2, val_y and key 4, val_x are NAs since those keys don't have a match in the other data frames.
    Figure 19.7: A visual representation of the full join where every row in x and y appears in the output.

Another way to show how the types of outer join differ is with a Venn diagram, as in Figure 19.8. However, this is not a great representation because while it might jog your memory about which rows are preserved, it fails to illustrate what’s happening with the columns.

Venn diagrams for inner, full, left, and right joins. Each join represented with two intersecting circles representing data frames x and y, with x on the right and y on the left. Shading indicates the result of the join.
Figure 19.8: Venn diagrams showing the difference between inner, left, right, and full joins.

The joins shown here are the so-called equi joins, where rows match if the keys are equal. Equi joins are the most common type of join, so we’ll typically omit the equi prefix, and just say “inner join” rather than “equi inner join”. We’ll come back to non-equi joins in Section 19.5.

19.4.1 Row matching

So far we’ve explored what happens if a row in x matches zero or one row in y. What happens if it matches more than one row? To understand what’s going on let’s first narrow our focus to the inner_join() and then draw a picture, Figure 19.9.

A join diagram where x has key values 1, 2, and 3, and y has key values 1, 2, 2. The output has three rows because key 1 matches one row, key 2 matches two rows, and key 3 matches zero rows.
Figure 19.9: The three ways a row in x can match. x1 matches one row in y, x2 matches two rows in y, x3 matches zero rows in y. Note that while there are three rows in x and three rows in the output, there isn’t a direct correspondence between the rows.

There are three possible outcomes for a row in x:

  • If it doesn’t match anything, it’s dropped.
  • If it matches 1 row in y, it’s preserved.
  • If it matches more than 1 row in y, it’s duplicated once for each match.

In principle, this means that there’s no guaranteed correspondence between the rows in the output and the rows in x, but in practice, this rarely causes problems. There is, however, one particularly dangerous case which can cause a combinatorial explosion of rows. Imagine joining the following two tables:

df1 <- tibble(key = c(1, 2, 2), val_x = c("x1", "x2", "x3"))
df2 <- tibble(key = c(1, 2, 2), val_y = c("y1", "y2", "y3"))

While the first row in df1 only matches one row in df2, the second and third rows both match two rows. This is sometimes called a many-to-many join, and will cause dplyr to emit a warning:

df1 |> 
  inner_join(df2, join_by(key))
#> Warning in inner_join(df1, df2, join_by(key)): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 2 of `x` matches multiple rows in `y`.
#> ℹ Row 2 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#>   "many-to-many"` to silence this warning.
#> # A tibble: 5 × 3
#>     key val_x val_y
#>   <dbl> <chr> <chr>
#> 1     1 x1    y1   
#> 2     2 x2    y2   
#> 3     2 x2    y3   
#> 4     2 x3    y2   
#> 5     2 x3    y3

If you are doing this deliberately, you can set relationship = "many-to-many", as the warning suggests.

19.4.2 Filtering joins

The number of matches also determines the behavior of the filtering joins. The semi-join keeps rows in x that have one or more matches in y, as in Figure 19.10. The anti-join keeps rows in x that match zero rows in y, as in Figure 19.11. In both cases, only the existence of a match is important; it doesn’t matter how many times it matches. This means that filtering joins never duplicate rows like mutating joins do.

A join diagram with old friends x and y. In a semi join, only the presence of a match matters so the output contains the same columns as x.
Figure 19.10: In a semi-join it only matters that there is a match; otherwise values in y don’t affect the output.
An anti-join is the inverse of a semi-join so matches are drawn with red lines indicating that they will be dropped from the output.
Figure 19.11: An anti-join is the inverse of a semi-join, dropping rows from x that have a match in y.

19.5 Non-equi joins

So far you’ve only seen equi joins, joins where the rows match if the x key equals the y key. Now we’re going to relax that restriction and discuss other ways of determining if a pair of rows match.

But before we can do that, we need to revisit a simplification we made above. In equi joins the x keys and y are always equal, so we only need to show one in the output. We can request that dplyr keep both keys with keep = TRUE, leading to the code below and the re-drawn inner_join() in Figure 19.12.

x |> inner_join(y, join_by(key == key), keep = TRUE)
#> # A tibble: 2 × 4
#>   key.x val_x key.y val_y
#>   <dbl> <chr> <dbl> <chr>
#> 1     1 x1        1 y1   
#> 2     2 x2        2 y2
A join diagram showing an inner join betwen x and y. The result now includes four columns: key.x, val_x, key.y, and val_y. The values of key.x and key.y are identical, which is why we usually only show one.
Figure 19.12: An inner join showing both x and y keys in the output.

When we move away from equi joins we’ll always show the keys, because the key values will often be different. For example, instead of matching only when the x$key and y$key are equal, we could match whenever the x$key is greater than or equal to the y$key, leading to Figure 19.13. dplyr’s join functions understand this distinction equi and non-equi joins so will always show both keys when you perform a non-equi join.

A join diagram illustrating join_by(key >= key). The first row of x matches one row of y and the second and thirds rows each match two rows. This means the output has five rows containing each of the following (key.x, key.y) pairs: (1, 1), (2, 1), (2, 2), (3, 1), (3, 2).
Figure 19.13: A non-equi join where the x key must be greater than or equal to the y key. Many rows generate multiple matches.

Non-equi join isn’t a particularly useful term because it only tells you what the join is not, not what it is. dplyr helps by identifying four particularly useful types of non-equi join:

  • Cross joins match every pair of rows.
  • Inequality joins use <, <=, >, and >= instead of ==.
  • Rolling joins are similar to inequality joins but only find the closest match.
  • Overlap joins are a special type of inequality join designed to work with ranges.

Each of these is described in more detail in the following sections.

19.5.1 Cross joins

A cross join matches everything, as in Figure 19.14, generating the Cartesian product of rows. This means the output will have nrow(x) * nrow(y) rows.

A join diagram showing a dot for every combination of x and y.
Figure 19.14: A cross join matches each row in x with every row in y.

Cross joins are useful when generating permutations. For example, the code below generates every possible pair of names. Since we’re joining df to itself, this is sometimes called a self-join. Cross joins use a different join function because there’s no distinction between inner/left/right/full when you’re matching every row.

df <- tibble(name = c("John", "Simon", "Tracy", "Max"))
df |> cross_join(df)
#> # A tibble: 16 × 2
#>   name.x name.y
#>   <chr>  <chr> 
#> 1 John   John  
#> 2 John   Simon 
#> 3 John   Tracy 
#> 4 John   Max   
#> 5 Simon  John  
#> 6 Simon  Simon 
#> # ℹ 10 more rows

19.5.2 Inequality joins

Inequality joins use <, <=, >=, or > to restrict the set of possible matches, as in Figure 19.13 and Figure 19.15.

A diagram depicting an inequality join where a data frame x is joined by a data frame y where the key of x is less than the key of y, resulting in a triangular shape in the top-left corner.
Figure 19.15: An inequality join where x is joined to y on rows where the key of x is less than the key of y. This makes a triangular shape in the top-left corner.

Inequality joins are extremely general, so general that it’s hard to come up with meaningful specific use cases. One small useful technique is to use them to restrict the cross join so that instead of generating all permutations, we generate all combinations:

df <- tibble(id = 1:4, name = c("John", "Simon", "Tracy", "Max"))

df |> inner_join(df, join_by(id < id))
#> # A tibble: 6 × 4
#>    id.x name.x  id.y name.y
#>   <int> <chr>  <int> <chr> 
#> 1     1 John       2 Simon 
#> 2     1 John       3 Tracy 
#> 3     1 John       4 Max   
#> 4     2 Simon      3 Tracy 
#> 5     2 Simon      4 Max   
#> 6     3 Tracy      4 Max

19.5.3 Rolling joins

Rolling joins are a special type of inequality join where instead of getting every row that satisfies the inequality, you get just the closest row, as in Figure 19.16. You can turn any inequality join into a rolling join by adding closest(). For example join_by(closest(x <= y)) matches the smallest y that’s greater than or equal to x, and join_by(closest(x > y)) matches the biggest y that’s less than x.

A rolling join is a subset of an inequality join so some matches are grayed out indicating that they're not used because they're not the "closest".
Figure 19.16: A rolling join is similar to a greater-than-or-equal inequality join but only matches the first value.

Rolling joins are particularly useful when you have two tables of dates that don’t perfectly line up and you want to find (e.g.) the closest date in table 1 that comes before (or after) some date in table 2.

For example, imagine that you’re in charge of the party planning commission for your office. Your company is rather cheap so instead of having individual parties, you only have a party once each quarter. The rules for determining when a party will be held are a little complex: parties are always on a Monday, you skip the first week of January since a lot of people are on holiday, and the first Monday of Q3 2022 is July 4, so that has to be pushed back a week. That leads to the following party days:

parties <- tibble(
  q = 1:4,
  party = ymd(c("2022-01-10", "2022-04-04", "2022-07-11", "2022-10-03"))
)

Now imagine that you have a table of employee birthdays:

set.seed(123)
employees <- tibble(
  name = sample(babynames::babynames$name, 100),
  birthday = ymd("2022-01-01") + (sample(365, 100, replace = TRUE) - 1)
)
employees
#> # A tibble: 100 × 2
#>   name     birthday  
#>   <chr>    <date>    
#> 1 Kemba    2022-01-22
#> 2 Orean    2022-06-26
#> 3 Kirstyn  2022-02-11
#> 4 Amparo   2022-11-11
#> 5 Belen    2022-03-25
#> 6 Rayshaun 2022-01-11
#> # ℹ 94 more rows

And for each employee we want to find the last party date that comes before (or on) their birthday. We can express that with a rolling join:

employees |> 
  left_join(parties, join_by(closest(birthday >= party)))
#> # A tibble: 100 × 4
#>   name     birthday       q party     
#>   <chr>    <date>     <int> <date>    
#> 1 Kemba    2022-01-22     1 2022-01-10
#> 2 Orean    2022-06-26     2 2022-04-04
#> 3 Kirstyn  2022-02-11     1 2022-01-10
#> 4 Amparo   2022-11-11     4 2022-10-03
#> 5 Belen    2022-03-25     1 2022-01-10
#> 6 Rayshaun 2022-01-11     1 2022-01-10
#> # ℹ 94 more rows

There is, however, one problem with this approach: the folks with birthdays before January 10 don’t get a party:

employees |> 
  anti_join(parties, join_by(closest(birthday >= party)))
#> # A tibble: 2 × 2
#>   name   birthday  
#>   <chr>  <date>    
#> 1 Maks   2022-01-07
#> 2 Nalani 2022-01-04

To resolve that issue we’ll need to tackle the problem a different way, with overlap joins.

19.5.4 Overlap joins

Overlap joins provide three helpers that use inequality joins to make it easier to work with intervals:

  • between(x, y_lower, y_upper) is short for x >= y_lower, x <= y_upper.
  • within(x_lower, x_upper, y_lower, y_upper) is short for x_lower >= y_lower, x_upper <= y_upper.
  • overlaps(x_lower, x_upper, y_lower, y_upper) is short for x_lower <= y_upper, x_upper >= y_lower.

Let’s continue the birthday example to see how you might use them. There’s one problem with the strategy we used above: there’s no party preceding the birthdays Jan 1-9. So it might be better to be explicit about the date ranges that each party spans, and make a special case for those early birthdays:

parties <- tibble(
  q = 1:4,
  party = ymd(c("2022-01-10", "2022-04-04", "2022-07-11", "2022-10-03")),
  start = ymd(c("2022-01-01", "2022-04-04", "2022-07-11", "2022-10-03")),
  end = ymd(c("2022-04-03", "2022-07-11", "2022-10-02", "2022-12-31"))
)
parties
#> # A tibble: 4 × 4
#>       q party      start      end       
#>   <int> <date>     <date>     <date>    
#> 1     1 2022-01-10 2022-01-01 2022-04-03
#> 2     2 2022-04-04 2022-04-04 2022-07-11
#> 3     3 2022-07-11 2022-07-11 2022-10-02
#> 4     4 2022-10-03 2022-10-03 2022-12-31

Hadley is hopelessly bad at data entry so he also wanted to check that the party periods don’t overlap. One way to do this is by using a self-join to check if any start-end interval overlap with another:

parties |> 
  inner_join(parties, join_by(overlaps(start, end, start, end), q < q)) |> 
  select(start.x, end.x, start.y, end.y)
#> # A tibble: 1 × 4
#>   start.x    end.x      start.y    end.y     
#>   <date>     <date>     <date>     <date>    
#> 1 2022-04-04 2022-07-11 2022-07-11 2022-10-02

Ooops, there is an overlap, so let’s fix that problem and continue:

parties <- tibble(
  q = 1:4,
  party = ymd(c("2022-01-10", "2022-04-04", "2022-07-11", "2022-10-03")),
  start = ymd(c("2022-01-01", "2022-04-04", "2022-07-11", "2022-10-03")),
  end = ymd(c("2022-04-03", "2022-07-10", "2022-10-02", "2022-12-31"))
)

Now we can match each employee to their party. This is a good place to use unmatched = "error" because we want to quickly find out if any employees didn’t get assigned a party.

employees |> 
  inner_join(parties, join_by(between(birthday, start, end)), unmatched = "error")
#> # A tibble: 100 × 6
#>   name     birthday       q party      start      end       
#>   <chr>    <date>     <int> <date>     <date>     <date>    
#> 1 Kemba    2022-01-22     1 2022-01-10 2022-01-01 2022-04-03
#> 2 Orean    2022-06-26     2 2022-04-04 2022-04-04 2022-07-10
#> 3 Kirstyn  2022-02-11     1 2022-01-10 2022-01-01 2022-04-03
#> 4 Amparo   2022-11-11     4 2022-10-03 2022-10-03 2022-12-31
#> 5 Belen    2022-03-25     1 2022-01-10 2022-01-01 2022-04-03
#> 6 Rayshaun 2022-01-11     1 2022-01-10 2022-01-01 2022-04-03
#> # ℹ 94 more rows

19.5.5 Exercises

  1. Can you explain what’s happening with the keys in this equi join? Why are they different?

    x |> full_join(y, join_by(key == key))
#> # A tibble: 4 × 3
#>     key val_x val_y
#>   <dbl> <chr> <chr>
#> 1     1 x1    y1   
#> 2     2 x2    y2   
#> 3     3 x3    <NA> 
#> 4     4 <NA>  y3

x |> full_join(y, join_by(key == key), keep = TRUE)
#> # A tibble: 4 × 4
#>   key.x val_x key.y val_y
#>   <dbl> <chr> <dbl> <chr>
#> 1     1 x1        1 y1   
#> 2     2 x2        2 y2   
#> 3     3 x3       NA <NA> 
#> 4    NA <NA>      4 y3

  2. When finding if any party period overlapped with another party period we used q < q in the join_by()? Why? What happens if you remove this inequality?

19.6 Summary

In this chapter, you’ve learned how to use mutating and filtering joins to combine data from a pair of data frames. Along the way you learned how to identify keys, and the difference between primary and foreign keys. You also understand how joins work and how to figure out how many rows the output will have. Finally, you’ve gained a glimpse into the power of non-equi joins and seen a few interesting use cases.

This chapter concludes the “Transform” part of the book where the focus was on the tools you could use with individual columns and tibbles. You learned about dplyr and base functions for working with logical vectors, numbers, and complete tables, stringr functions for working with strings, lubridate functions for working with date-times, and forcats functions for working with factors.

In the next part of the book, you’ll learn more about getting various types of data into R in a tidy form.

  1. Remember that in RStudio you can also use View() to avoid this problem.↩︎

  2. That’s not 100% true, but you’ll get a warning whenever it isn’t.↩︎