13 Relational data

13.1 Introduction

It’s rare that a data analysis involves only a single table of data. Typically you have many tables of data, and you must combine them to answer the questions that you’re interested in. Collectively, multiple tables of data are called relational data because it is the relations, not just the individual datasets, that are important.

Relations are always defined between a pair of tables. All other relations are built up from this simple idea: the relations of three or more tables are always a property of the relations between each pair. Sometimes both elements of a pair can be the same table! This is needed if, for example, you have a table of people, and each person has a reference to their parents.

To work with relational data you need verbs that work with pairs of tables. There are three families of verbs designed to work with relational data:

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 the other table.
Set operations, which treat observations as if they were set elements.

The most common place to find relational data is in a relational database management system (or RDBMS), a term that encompasses almost all modern databases. If you’ve used a database before, you’ve almost certainly used SQL. If so, you should find the concepts in this chapter familiar, although their expression in dplyr is a little different. Generally, dplyr is a little easier to use than SQL because dplyr is specialised to do data analysis: it makes common data analysis operations easier, at the expense of making it more difficult to do other things that aren’t commonly needed for data analysis.

13.1.1 Prerequisites

We will explore relational data from nycflights13 using the two-table verbs from dplyr.

library(tidyverse)
library(nycflights13)

13.2 nycflights13

We will use the nycflights13 package to learn about relational data. nycflights13 contains four tibbles that are related to the flights table that you used in data transformation:

airlines lets you look up the full carrier name from its abbreviated code:

airlines
#> # A tibble: 16 x 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.
#> # ... with 10 more rows

airports gives information about each airport, identified by the faa airport code:

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

planes gives information about each plane, identified by its tailnum:

planes
#> # A tibble: 3,322 x 9
#>   tailnum  year type           manufacturer   model  engines seats speed engine 
#>   <chr>   <int> <chr>          <chr>          <chr>    <int> <int> <int> <chr>  
#> 1 N10156   2004 Fixed wing mu~ EMBRAER        EMB-1~       2    55    NA Turbo-~
#> 2 N102UW   1998 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~
#> 3 N103US   1999 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~
#> 4 N104UW   1999 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~
#> 5 N10575   2002 Fixed wing mu~ EMBRAER        EMB-1~       2    55    NA Turbo-~
#> 6 N105UW   1999 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~
#> # ... with 3,316 more rows

weather gives the weather at each NYC airport for each hour:

weather
#> # A tibble: 26,115 x 15
#>   origin  year month   day  hour  temp  dewp humid wind_dir wind_speed wind_gust
#>   <chr>  <int> <int> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>     <dbl>
#> 1 EWR     2013     1     1     1  39.0  26.1  59.4      270      10.4         NA
#> 2 EWR     2013     1     1     2  39.0  27.0  61.6      250       8.06        NA
#> 3 EWR     2013     1     1     3  39.0  28.0  64.4      240      11.5         NA
#> 4 EWR     2013     1     1     4  39.9  28.0  62.2      250      12.7         NA
#> 5 EWR     2013     1     1     5  39.0  28.0  64.4      260      12.7         NA
#> 6 EWR     2013     1     1     6  37.9  28.0  67.2      240      11.5         NA
#> # ... with 26,109 more rows, and 4 more variables: precip <dbl>,
#> #   pressure <dbl>, visib <dbl>, time_hour <dttm>

One way to show the relationships between the different tables is with a drawing:

This diagram is a little overwhelming, but it’s simple compared to some you’ll see in the wild! The key to understanding diagrams like this is to remember each relation always concerns a pair of tables. You don’t need to understand the whole thing; you just need to understand the chain of relations between the tables that you are interested in.

For nycflights13:

flights connects to planes via a single variable, tailnum.
flights connects to airlines through the carrier variable.
flights connects to airports in two ways: via the origin and dest variables.
flights connects to weather via origin (the location), and year, month, day and hour (the time).

13.2.1 Exercises

Imagine you wanted to draw (approximately) the route each plane flies from its origin to its destination. What variables would you need? What tables would you need to combine?
I forgot to draw the relationship between weather and airports. What is the relationship and how should it appear in the diagram?
weather only contains information for the origin (NYC) airports. If it contained weather records for all airports in the USA, what additional relation would it define with flights?
We know that some days of the year are “special”, and fewer people than usual fly on them. How might you represent that data as a data frame? What would be the primary keys of that table? How would it connect to the existing tables?

13.3 Keys

The variables used to connect each pair of tables are called keys. A key is a variable (or set of variables) that uniquely identifies an observation. In simple cases, a single variable is sufficient to identify an observation. For example, each plane is uniquely identified by its tailnum. In other cases, multiple variables may be needed. For example, to identify an observation in weather you need five variables: year, month, day, hour, and origin.

There are two types of keys:

A primary key uniquely identifies an observation in its own table. For example, planes$tailnum is a primary key because it uniquely identifies each plane in the planes table.
A foreign key uniquely identifies an observation in another table. For example, flights$tailnum is a foreign key because it appears in the flights table where it matches each flight to a unique plane.

A variable can be both a primary key and a foreign key. For example, origin is part of the weather primary key, and is also a foreign key for the airports table.

Once you’ve identified the primary keys in your tables, 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:

planes %>% 
  count(tailnum) %>% 
  filter(n > 1)
#> # A tibble: 0 x 2
#> # ... with 2 variables: tailnum <chr>, n <int>

weather %>% 
  count(year, month, day, hour, origin) %>% 
  filter(n > 1)
#> # A tibble: 3 x 6
#>    year month   day  hour origin     n
#>   <int> <int> <int> <int> <chr>  <int>
#> 1  2013    11     3     1 EWR        2
#> 2  2013    11     3     1 JFK        2
#> 3  2013    11     3     1 LGA        2

Sometimes a table doesn’t have an explicit primary key: each row is an observation, but no combination of variables reliably identifies it. For example, what’s the primary key in the flights table? You might think it would be the date plus the flight or tail number, but neither of those are unique:

flights %>% 
  count(year, month, day, flight) %>% 
  filter(n > 1)
#> # A tibble: 29,768 x 5
#>    year month   day flight     n
#>   <int> <int> <int>  <int> <int>
#> 1  2013     1     1      1     2
#> 2  2013     1     1      3     2
#> 3  2013     1     1      4     2
#> 4  2013     1     1     11     3
#> 5  2013     1     1     15     2
#> 6  2013     1     1     21     2
#> # ... with 29,762 more rows

flights %>% 
  count(year, month, day, tailnum) %>% 
  filter(n > 1)
#> # A tibble: 64,928 x 5
#>    year month   day tailnum     n
#>   <int> <int> <int> <chr>   <int>
#> 1  2013     1     1 N0EGMQ      2
#> 2  2013     1     1 N11189      2
#> 3  2013     1     1 N11536      2
#> 4  2013     1     1 N11544      3
#> 5  2013     1     1 N11551      2
#> 6  2013     1     1 N12540      2
#> # ... with 64,922 more rows

When starting to work with this data, I had naively assumed that each flight number would be only used once per day: that would make it much easier to communicate problems with a specific flight. Unfortunately that is not the case! If a table lacks a primary key, it’s sometimes useful to add one with mutate() and row_number(). That makes it easier to match observations if you’ve done some filtering and want to check back in with the original data. This is called a surrogate key.

A primary key and the corresponding foreign key in another table form a relation. Relations are typically one-to-many. For example, each flight has one plane, but each plane has many flights. In other data, you’ll occasionally see a 1-to-1 relationship. You can think of this as a special case of 1-to-many. You can model many-to-many relations with a many-to-1 relation plus a 1-to-many relation. For example, in this data there’s a many-to-many relationship between airlines and airports: each airline flies to many airports; each airport hosts many airlines.

13.3.1 Exercises

Add a surrogate key to flights.
Identify the keys in the following datasets
1. Lahman::Batting,
2. babynames::babynames
3. nasaweather::atmos
4. fueleconomy::vehicles
5. ggplot2::diamonds
(You might need to install some packages and read some documentation.)
Draw a diagram illustrating the connections between the Batting, People, and Salaries tables in the Lahman package. Draw another diagram that shows the relationship between People, Managers, AwardsManagers.

How would you characterise the relationship between the Batting, Pitching, and Fielding tables?

13.4 Mutating joins

The first tool we’ll look at for combining a pair of tables is the mutating join. A mutating join allows you to combine variables from two tables. It first matches observations by their keys, then copies across variables from one table to the other.

Like mutate(), the join functions add variables to the right, so if you have a lot of variables already, the new variables won’t get printed out. For these examples, we’ll make it easier to see what’s going on in the examples by creating a narrower dataset:

flights2 <- flights %>% 
  select(year:day, hour, origin, dest, tailnum, carrier)
flights2
#> # A tibble: 336,776 x 8
#>    year month   day  hour origin dest  tailnum carrier
#>   <int> <int> <int> <dbl> <chr>  <chr> <chr>   <chr>  
#> 1  2013     1     1     5 EWR    IAH   N14228  UA     
#> 2  2013     1     1     5 LGA    IAH   N24211  UA     
#> 3  2013     1     1     5 JFK    MIA   N619AA  AA     
#> 4  2013     1     1     5 JFK    BQN   N804JB  B6     
#> 5  2013     1     1     6 LGA    ATL   N668DN  DL     
#> 6  2013     1     1     5 EWR    ORD   N39463  UA     
#> # ... with 336,770 more rows

(Remember, when you’re in RStudio, you can also use View() to avoid this problem.)

Imagine you want to add the full airline name to the flights2 data. You can combine the airlines and flights2 data frames with left_join():

flights2 %>%
  select(-origin, -dest) %>% 
  left_join(airlines, by = "carrier")
#> # A tibble: 336,776 x 7
#>    year month   day  hour tailnum carrier name                  
#>   <int> <int> <int> <dbl> <chr>   <chr>   <chr>                 
#> 1  2013     1     1     5 N14228  UA      United Air Lines Inc. 
#> 2  2013     1     1     5 N24211  UA      United Air Lines Inc. 
#> 3  2013     1     1     5 N619AA  AA      American Airlines Inc.
#> 4  2013     1     1     5 N804JB  B6      JetBlue Airways       
#> 5  2013     1     1     6 N668DN  DL      Delta Air Lines Inc.  
#> 6  2013     1     1     5 N39463  UA      United Air Lines Inc. 
#> # ... with 336,770 more rows

The result of joining airlines to flights2 is an additional variable: name. This is why I call this type of join a mutating join. In this case, you could have got to the same place using mutate() and R’s base subsetting:

flights2 %>%
  select(-origin, -dest) %>% 
  mutate(name = airlines$name[match(carrier, airlines$carrier)])
#> # A tibble: 336,776 x 7
#>    year month   day  hour tailnum carrier name                  
#>   <int> <int> <int> <dbl> <chr>   <chr>   <chr>                 
#> 1  2013     1     1     5 N14228  UA      United Air Lines Inc. 
#> 2  2013     1     1     5 N24211  UA      United Air Lines Inc. 
#> 3  2013     1     1     5 N619AA  AA      American Airlines Inc.
#> 4  2013     1     1     5 N804JB  B6      JetBlue Airways       
#> 5  2013     1     1     6 N668DN  DL      Delta Air Lines Inc.  
#> 6  2013     1     1     5 N39463  UA      United Air Lines Inc. 
#> # ... with 336,770 more rows

But this is hard to generalise when you need to match multiple variables, and takes close reading to figure out the overall intent.

The following sections explain, in detail, how mutating joins work. You’ll start by learning a useful visual representation of joins. We’ll then use that to explain the four mutating join functions: the inner join, and the three outer joins. When working with real data, keys don’t always uniquely identify observations, so next we’ll talk about what happens when there isn’t a unique match. Finally, you’ll learn how to tell dplyr which variables are the keys for a given join.

13.4.1 Understanding joins

To help you learn how joins work, I’m going to use a visual representation:

x <- tribble(
  ~key, ~val_x,
     1, "x1",
     2, "x2",
     3, "x3"
)
y <- tribble(
  ~key, ~val_y,
     1, "y1",
     2, "y2",
     4, "y3"
)

The coloured column represents the “key” variable: these are used to match the rows between the tables. The grey column represents the “value” column that is carried along for the ride. In these examples I’ll show a single key variable, but the idea generalises in a straightforward way to multiple keys and multiple values.

A join is a way of connecting each row in x to zero, one, or more rows in y. The following diagram shows each potential match as an intersection of a pair of lines.

(If you look closely, you might notice that we’ve switched the order of the key and value columns in x. This is to emphasise that joins match based on the key; the value is just carried along for the ride.)

In an actual join, matches will be indicated with dots. The number of dots = the number of matches = the number of rows in the output.

13.4.2 Inner join

The simplest type of join is the inner join. An inner join matches pairs of observations whenever their keys are equal:

(To be precise, this is an inner equijoin because the keys are matched using the equality operator. Since most joins are equijoins we usually drop that specification.)

The output of an inner join is a new data frame that contains the key, the x values, and the y values. We use by to tell dplyr which variable is the key:

x %>% 
  inner_join(y, by = "key")
#> # A tibble: 2 x 3
#>     key val_x val_y
#>   <dbl> <chr> <chr>
#> 1     1 x1    y1   
#> 2     2 x2    y2

The most important property of an inner join is that unmatched rows are not included in the result. This means that generally inner joins are usually not appropriate for use in analysis because it’s too easy to lose observations.

13.4.3 Outer joins

An inner join keeps observations that appear in both tables. An outer join keeps observations that appear in at least one of the tables. There are three types of outer joins:

A left join keeps all observations in x.
A right join keeps all observations in y.
A full join keeps all observations in x and y.

These joins work by adding an additional “virtual” observation to each table. This observation has a key that always matches (if no other key matches), and a value filled with NA.

Graphically, that looks like:

The most commonly used join is the left join: you use this whenever you look up additional data from another table, because it preserves the original observations even when there isn’t a match. The left join should be your default join: use it unless you have a strong reason to prefer one of the others.

Another way to depict the different types of joins is with a Venn diagram:

However, this is not a great representation. It might jog your memory about which join preserves the observations in which table, but it suffers from a major limitation: a Venn diagram can’t show what happens when keys don’t uniquely identify an observation.

13.4.4 Duplicate keys

So far all the diagrams have assumed that the keys are unique. But that’s not always the case. This section explains what happens when the keys are not unique. There are two possibilities:

One table has duplicate keys. This is useful when you want to add in additional information as there is typically a one-to-many relationship.

Note that I’ve put the key column in a slightly different position in the output. This reflects that the key is a primary key in y and a foreign key in x.

x <- tribble(
  ~key, ~val_x,
     1, "x1",
     2, "x2",
     2, "x3",
     1, "x4"
)
y <- tribble(
  ~key, ~val_y,
     1, "y1",
     2, "y2"
)
left_join(x, y, by = "key")
#> # A tibble: 4 x 3
#>     key val_x val_y
#>   <dbl> <chr> <chr>
#> 1     1 x1    y1   
#> 2     2 x2    y2   
#> 3     2 x3    y2   
#> 4     1 x4    y1

Both tables have duplicate keys. This is usually an error because in neither table do the keys uniquely identify an observation. When you join duplicated keys, you get all possible combinations, the Cartesian product:

x <- tribble(
  ~key, ~val_x,
     1, "x1",
     2, "x2",
     2, "x3",
     3, "x4"
)
y <- tribble(
  ~key, ~val_y,
     1, "y1",
     2, "y2",
     2, "y3",
     3, "y4"
)
left_join(x, y, by = "key")
#> # A tibble: 6 x 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   
#> 6     3 x4    y4

13.4.5 Defining the key columns

So far, the pairs of tables have always been joined by a single variable, and that variable has the same name in both tables. That constraint was encoded by by = "key". You can use other values for by to connect the tables in other ways:

The default, by = NULL, uses all variables that appear in both tables, the so called natural join. For example, the flights and weather tables match on their common variables: year, month, day, hour and origin.

flights2 %>% 
  left_join(weather)
#> Joining, by = c("year", "month", "day", "hour", "origin")
#> # A tibble: 336,776 x 18
#>    year month   day  hour origin dest  tailnum carrier  temp  dewp humid
#>   <int> <int> <int> <dbl> <chr>  <chr> <chr>   <chr>   <dbl> <dbl> <dbl>
#> 1  2013     1     1     5 EWR    IAH   N14228  UA       39.0  28.0  64.4
#> 2  2013     1     1     5 LGA    IAH   N24211  UA       39.9  25.0  54.8
#> 3  2013     1     1     5 JFK    MIA   N619AA  AA       39.0  27.0  61.6
#> 4  2013     1     1     5 JFK    BQN   N804JB  B6       39.0  27.0  61.6
#> 5  2013     1     1     6 LGA    ATL   N668DN  DL       39.9  25.0  54.8
#> 6  2013     1     1     5 EWR    ORD   N39463  UA       39.0  28.0  64.4
#> # ... with 336,770 more rows, and 7 more variables: wind_dir <dbl>,
#> #   wind_speed <dbl>, wind_gust <dbl>, precip <dbl>, pressure <dbl>,
#> #   visib <dbl>, time_hour <dttm>

A character vector, by = "x". This is like a natural join, but uses only some of the common variables. For example, flights and planes have year variables, but they mean different things so we only want to join by tailnum.

flights2 %>% 
  left_join(planes, by = "tailnum")
#> # A tibble: 336,776 x 16
#>   year.x month   day  hour origin dest  tailnum carrier year.y type             
#>    <int> <int> <int> <dbl> <chr>  <chr> <chr>   <chr>    <int> <chr>            
#> 1   2013     1     1     5 EWR    IAH   N14228  UA        1999 Fixed wing multi~
#> 2   2013     1     1     5 LGA    IAH   N24211  UA        1998 Fixed wing multi~
#> 3   2013     1     1     5 JFK    MIA   N619AA  AA        1990 Fixed wing multi~
#> 4   2013     1     1     5 JFK    BQN   N804JB  B6        2012 Fixed wing multi~
#> 5   2013     1     1     6 LGA    ATL   N668DN  DL        1991 Fixed wing multi~
#> 6   2013     1     1     5 EWR    ORD   N39463  UA        2012 Fixed wing multi~
#> # ... with 336,770 more rows, and 6 more variables: manufacturer <chr>,
#> #   model <chr>, engines <int>, seats <int>, speed <int>, engine <chr>

Note that the year variables (which appear in both input data frames, but are not constrained to be equal) are disambiguated in the output with a suffix.

A named character vector: by = c("a" = "b"). This will match variable a in table x to variable b in table y. The variables from x will be used in the output.

For example, if we want to draw a map we need to combine the flights data with the airports data which contains the location (lat and lon) of each airport. Each flight has an origin and destination airport, so we need to specify which one we want to join to:

flights2 %>% 
  left_join(airports, c("dest" = "faa"))
#> # A tibble: 336,776 x 15
#>    year month   day  hour origin dest  tailnum carrier name      lat   lon   alt
#>   <int> <int> <int> <dbl> <chr>  <chr> <chr>   <chr>   <chr>   <dbl> <dbl> <dbl>
#> 1  2013     1     1     5 EWR    IAH   N14228  UA      George~  30.0 -95.3    97
#> 2  2013     1     1     5 LGA    IAH   N24211  UA      George~  30.0 -95.3    97
#> 3  2013     1     1     5 JFK    MIA   N619AA  AA      Miami ~  25.8 -80.3     8
#> 4  2013     1     1     5 JFK    BQN   N804JB  B6      <NA>     NA    NA      NA
#> 5  2013     1     1     6 LGA    ATL   N668DN  DL      Hartsf~  33.6 -84.4  1026
#> 6  2013     1     1     5 EWR    ORD   N39463  UA      Chicag~  42.0 -87.9   668
#> # ... with 336,770 more rows, and 3 more variables: tz <dbl>, dst <chr>,
#> #   tzone <chr>

flights2 %>% 
  left_join(airports, c("origin" = "faa"))
#> # A tibble: 336,776 x 15
#>    year month   day  hour origin dest  tailnum carrier name      lat   lon   alt
#>   <int> <int> <int> <dbl> <chr>  <chr> <chr>   <chr>   <chr>   <dbl> <dbl> <dbl>
#> 1  2013     1     1     5 EWR    IAH   N14228  UA      Newark~  40.7 -74.2    18
#> 2  2013     1     1     5 LGA    IAH   N24211  UA      La Gua~  40.8 -73.9    22
#> 3  2013     1     1     5 JFK    MIA   N619AA  AA      John F~  40.6 -73.8    13
#> 4  2013     1     1     5 JFK    BQN   N804JB  B6      John F~  40.6 -73.8    13
#> 5  2013     1     1     6 LGA    ATL   N668DN  DL      La Gua~  40.8 -73.9    22
#> 6  2013     1     1     5 EWR    ORD   N39463  UA      Newark~  40.7 -74.2    18
#> # ... with 336,770 more rows, and 3 more variables: tz <dbl>, dst <chr>,
#> #   tzone <chr>

13.4.6 Exercises

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, c("faa" = "dest")) %>%
  ggplot(aes(lon, lat)) +
    borders("state") +
    geom_point() +
    coord_quickmap()
```
(Don’t worry if you don’t understand what semi_join() does — you’ll learn about it next.)

You might want to use the size or colour of the points to display the average delay for each airport.
Add the location of the origin and destination (i.e. the lat and lon) to flights.
Is there a relationship between the age of a plane and its delays?
What weather conditions make it more likely to see a delay?
What happened on June 13 2013? Display the spatial pattern of delays, and then use Google to cross-reference with the weather.

13.4.7 Other implementations

base::merge() can perform all four types of mutating join:

dplyr	merge
`inner_join(x, y)`	`merge(x, y)`
`left_join(x, y)`	`merge(x, y, all.x = TRUE)`
`right_join(x, y)`	`merge(x, y, all.y = TRUE)`,
`full_join(x, y)`	`merge(x, y, all.x = TRUE, all.y = TRUE)`

The advantages of the specific dplyr verbs is that they more clearly convey the intent of your code: the difference between the joins is really important but concealed in the arguments of merge(). dplyr’s joins are considerably faster and don’t mess with the order of the rows.

SQL is the inspiration for dplyr’s conventions, so the translation is straightforward:

dplyr	SQL
`inner_join(x, y, by = "z")`	`SELECT * FROM x INNER JOIN y USING (z)`
`left_join(x, y, by = "z")`	`SELECT * FROM x LEFT OUTER JOIN y USING (z)`
`right_join(x, y, by = "z")`	`SELECT * FROM x RIGHT OUTER JOIN y USING (z)`
`full_join(x, y, by = "z")`	`SELECT * FROM x FULL OUTER JOIN y USING (z)`

Note that “INNER” and “OUTER” are optional, and often omitted.

Joining different variables between the tables, e.g. inner_join(x, y, by = c("a" = "b")) uses a slightly different syntax in SQL: SELECT * FROM x INNER JOIN y ON x.a = y.b. As this syntax suggests, SQL supports a wider range of join types than dplyr because you can connect the tables using constraints other than equality (sometimes called non-equijoins).

13.5 Filtering joins

Filtering joins match observations in the same way as mutating joins, but affect the observations, not the variables. There are two types:

semi_join(x, y) keeps all observations in x that have a match in y.
anti_join(x, y) drops all observations in x that have a match in y.

Semi-joins are useful for matching filtered summary tables back to the original rows. For example, imagine you’ve found the top ten most popular destinations:

top_dest <- flights %>%
  count(dest, sort = TRUE) %>%
  head(10)
top_dest
#> # A tibble: 10 x 2
#>   dest      n
#>   <chr> <int>
#> 1 ORD   17283
#> 2 ATL   17215
#> 3 LAX   16174
#> 4 BOS   15508
#> 5 MCO   14082
#> 6 CLT   14064
#> # ... with 4 more rows

Now you want to find each flight that went to one of those destinations. You could construct a filter yourself:

flights %>% 
  filter(dest %in% top_dest$dest)
#> # A tibble: 141,145 x 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      542            540         2      923            850
#> 2  2013     1     1      554            600        -6      812            837
#> 3  2013     1     1      554            558        -4      740            728
#> 4  2013     1     1      555            600        -5      913            854
#> 5  2013     1     1      557            600        -3      838            846
#> 6  2013     1     1      558            600        -2      753            745
#> # ... with 141,139 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

But it’s difficult to extend that approach to multiple variables. For example, imagine that you’d found the 10 days with highest average delays. How would you construct the filter statement that used year, month, and day to match it back to flights?

Instead you can use a semi-join, which connects the two tables like a mutating join, but instead of adding new columns, only keeps the rows in x that have a match in y:

flights %>% 
  semi_join(top_dest)
#> Joining, by = "dest"
#> # A tibble: 141,145 x 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      542            540         2      923            850
#> 2  2013     1     1      554            600        -6      812            837
#> 3  2013     1     1      554            558        -4      740            728
#> 4  2013     1     1      555            600        -5      913            854
#> 5  2013     1     1      557            600        -3      838            846
#> 6  2013     1     1      558            600        -2      753            745
#> # ... with 141,139 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

Graphically, a semi-join looks like this:

Only the existence of a match is important; it doesn’t matter which observation is matched. This means that filtering joins never duplicate rows like mutating joins do:

The inverse of a semi-join is an anti-join. An anti-join keeps the rows that don’t have a match:

Anti-joins are useful for diagnosing join mismatches. For example, when connecting flights and planes, you might be interested to know that there are many flights that don’t have a match in planes:

flights %>%
  anti_join(planes, by = "tailnum") %>%
  count(tailnum, sort = TRUE)
#> # A tibble: 722 x 2
#>   tailnum     n
#>   <chr>   <int>
#> 1 <NA>     2512
#> 2 N725MQ    575
#> 3 N722MQ    513
#> 4 N723MQ    507
#> 5 N713MQ    483
#> 6 N735MQ    396
#> # ... with 716 more rows

13.5.1 Exercises

What does it mean for a flight to have a missing tailnum? 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.)
Filter flights to only show flights with planes that have flown at least 100 flights.
Combine fueleconomy::vehicles and fueleconomy::common to find only the records for the most common models.
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?
What does anti_join(flights, airports, by = c("dest" = "faa")) tell you? What does anti_join(airports, flights, by = c("faa" = "dest")) tell you?
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 above.

13.6 Join problems

The data you’ve been working with in this chapter has been cleaned up so that you’ll have as few problems as possible. Your own data is unlikely to be so nice, so there are a few things that you should do with your own data to make your joins go smoothly.

Start by identifying the variables that form the primary key in each table. You should usually do this based on your understanding of the data, not empirically by looking for a combination of variables that give a unique identifier. If you just look for variables without thinking about what they mean, you might get (un)lucky and find a combination that’s unique in your current data but the relationship might not be true in general.

For example, the altitude and longitude uniquely identify each airport, but they are not good identifiers!
```
airports %>% count(alt, lon) %>% filter(n > 1)
#> # A tibble: 0 x 3
#> # ... with 3 variables: alt <dbl>, lon <dbl>, n <int>
```
Check that none of the variables in the primary key are missing. If a value is missing then it can’t identify an observation!
Check that your foreign keys match primary keys in another table. The best way to do this is with an anti_join(). It’s common for keys not to match because of data entry errors. Fixing these is often a lot of work.

If you do have missing keys, you’ll need to be thoughtful about your use of inner vs. outer joins, carefully considering whether or not you want to drop rows that don’t have a match.

Be aware that simply checking the number of rows before and after the join is not sufficient to ensure that your join has gone smoothly. If you have an inner join with duplicate keys in both tables, you might get unlucky as the number of dropped rows might exactly equal the number of duplicated rows!

13.7 Set operations

The final type of two-table verb are the set operations. Generally, I use these the least frequently, but they are occasionally useful when you want to break a single complex filter into simpler pieces. All these operations work with a complete row, comparing the values of every variable. These expect the x and y inputs to have the same variables, and treat the observations like sets:

intersect(x, y): return only observations in both x and y.
union(x, y): return unique observations in x and y.
setdiff(x, y): return observations in x, but not in y.

Given this simple data:

df1 <- tribble(
  ~x, ~y,
   1,  1,
   2,  1
)
df2 <- tribble(
  ~x, ~y,
   1,  1,
   1,  2
)

The four possibilities are:

intersect(df1, df2)
#> # A tibble: 1 x 2
#>       x     y
#>   <dbl> <dbl>
#> 1     1     1

# Note that we get 3 rows, not 4
union(df1, df2)
#> # A tibble: 3 x 2
#>       x     y
#>   <dbl> <dbl>
#> 1     1     1
#> 2     2     1
#> 3     1     2

setdiff(df1, df2)
#> # A tibble: 1 x 2
#>       x     y
#>   <dbl> <dbl>
#> 1     2     1

setdiff(df2, df1)
#> # A tibble: 1 x 2
#>       x     y
#>   <dbl> <dbl>
#> 1     1     2

12 Tidy data

14 Strings