14 Relational data

14.1 Reading

This hands-on exercise draws heavily on the following sources:

Another resource with a good explanation of the types of joins can be found at [Tidy Animated Verbs}(https://www.garrickadenbuie.com/project/tidyexplain/)(Aden‑Buie 2021)

For some additional examples of table joins, see William Surles, Joining Data in R with dplyr

14.2 Joining two data tables

14.3 Objectives

  • understand the function of keys in relational databases

  • understand how to join tables

  • understand the primary types of mutating and filtering joins

14.4 Relational data

Often, the data you are working with are spread across multiple tables. This allows for efficient database storage (there’s an entire discipline dedicated to database theory and practical implementations of those theories.)

This requires you, the data analyst, to join tables, so that the information held in multiple tables can be used to answer the research question at hand.

Earlier you worked with the {nycflights13} package; for this hands-on exercise we will return to it.

In addition to flights, there are four other tables in the package:

  • airlines

  • airports

  • planes

  • weather

The tables are related to flights by the fact that they have variables in common. These are known as the “key” variables.

This diagram shows the relationships:

nycflights13

14.5 Keys

  1. Primary: identifies a unique observation in the table.

  2. Foreign: a unique observation in another table, but not this one.

An example: tailnum

  • primary in planes – there is only one observation for each aircraft

  • foreign in flights – a plane could have multiple flights in and out of NYC airports

Having the same key in two tables forms the “relation” – hence “relational database”.

14.5.0.1 Your turn

Use count to check that planes$tailnum is a primary key

Solution
# solution (in _R4DS_)
planes %>% 
  count(tailnum) %>% 
  filter(n > 1)
## # A tibble: 0 × 2
## # … with 2 variables: tailnum <chr>, n <int>
# alternate solution
planes %>% 
  count(tailnum) %>% 
  summarise(max(n))
## # A tibble: 1 × 1
##   `max(n)`
##      <int>
## 1        1
flights %>%
  group_by(tailnum) %>% 
  tally()
## # A tibble: 4,044 × 2
##    tailnum     n
##    <chr>   <int>
##  1 D942DN      4
##  2 N0EGMQ    371
##  3 N10156    153
##  4 N102UW     48
##  5 N103US     46
##  6 N104UW     47
##  7 N10575    289
##  8 N105UW     45
##  9 N107US     41
## 10 N108UW     60
## # … with 4,034 more rows

Sometimes tables don’t have a primary key! When that happens, it can be useful to create one: mutate() and row_number() is one approach. This is a surrogate key.

14.6 Mutating joins

The first kind of joins are “mutating joins”—new variables are added to one data frame from matching observations in another.

14.6.1 Understanding keys

First, we will make two small tables, table_one and table_two

table_one <- tribble(
  ~key_var, ~colour_t1,
     "key1", "red",
     "key2", "blue",
     "key3", "yellow"
)

table_two <- tribble(
  ~key_var, ~fruit_t2,
     "key1", "apple",
     "key2", "blueberry",
     "key4", "banana"
)

14.6.2 Inner joins

An inner join keeps only the observations where there is a match on both sides. You will see a message in the console (and your R markdown thumbnail) letting you know what variables are used for the join.

inner_join(x = table_one, y = table_two, key = "key_var")
## # A tibble: 2 × 3
##   key_var colour_t1 fruit_t2 
##   <chr>   <chr>     <chr>    
## 1 key1    red       apple    
## 2 key2    blue      blueberry

More commonly, you will see the key variables coded as by =:

inner_join(table_one, table_two, by = "key_var")
## # A tibble: 2 × 3
##   key_var colour_t1 fruit_t2 
##   <chr>   <chr>     <chr>    
## 1 key1    red       apple    
## 2 key2    blue      blueberry

Note that in the circumstances where we are confident that the key variables are named the same in both tables, we can use a bit of coding short-hand and omit the by = argument. This the arrangement of arguments in the left_join function (and the other joins, too): by = is third, after x and y.

inner_join(table_one, table_two)
## # A tibble: 2 × 3
##   key_var colour_t1 fruit_t2 
##   <chr>   <chr>     <chr>    
## 1 key1    red       apple    
## 2 key2    blue      blueberry

14.6.3 Left join

Left, right, and full joins are varieties of “outer joins”.

Join the two tables using a left join: … all of the observations from the first table (on the left-hand side of the function), and variables from the matching records from the secon (the right-hand side of the function).

The {dplyr} function for this is left_join()

left_join(table_one, table_two, by = "key_var")
## # A tibble: 3 × 3
##   key_var colour_t1 fruit_t2 
##   <chr>   <chr>     <chr>    
## 1 key1    red       apple    
## 2 key2    blue      blueberry
## 3 key3    yellow    <NA>

Note that all 3 of the table_one values are there; because there is no table_two observation with the key value of “key3”, the fruit_t2 value is NA.

right join

Is the same as a left join, but keeps all of the observations in the right-hand table y

full join

Keeps all of the observations in both x and y

See what happens when you join the tables with a right_join(), followed by a full_join()

# solution
right_join(table_one, table_two, by = "key_var")
## # A tibble: 3 × 3
##   key_var colour_t1 fruit_t2 
##   <chr>   <chr>     <chr>    
## 1 key1    red       apple    
## 2 key2    blue      blueberry
## 3 key4    <NA>      banana
full_join(table_one, table_two, by = "key_var")
## # A tibble: 4 × 3
##   key_var colour_t1 fruit_t2 
##   <chr>   <chr>     <chr>    
## 1 key1    red       apple    
## 2 key2    blue      blueberry
## 3 key3    yellow    <NA>     
## 4 key4    <NA>      banana

14.6.3.1 Your turn

In this example, we will add the name of the airline to the flights table. First, we will make a smaller version of the flights table by selecting a few of the variables, and taking the first 100 rows with the slice() function (see https://dplyr.tidyverse.org/reference/slice.html).

flights2 <- flights %>% 
  select(year:day, hour, origin, dest, tailnum, carrier) %>% 
  slice(1:100)
flights2
## # A tibble: 100 × 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     
##  7  2013     1     1     6 EWR    FLL   N516JB  B6     
##  8  2013     1     1     6 LGA    IAD   N829AS  EV     
##  9  2013     1     1     6 JFK    MCO   N593JB  B6     
## 10  2013     1     1     6 LGA    ORD   N3ALAA  AA     
## # … with 90 more rows

To this table we will add the name of the airline, which we can find in the table airlines.

  • both tables have the variable “carrier”

  • we want a left_join: all the flight observations, and adding the “name” variable from airlines

Solution
# solution
left_join(flights2, airlines, by = "carrier")
## # A tibble: 100 × 9
##     year month   day  hour origin dest  tailnum carrier name                    
##    <int> <int> <int> <dbl> <chr>  <chr> <chr>   <chr>   <chr>                   
##  1  2013     1     1     5 EWR    IAH   N14228  UA      United Air Lines Inc.   
##  2  2013     1     1     5 LGA    IAH   N24211  UA      United Air Lines Inc.   
##  3  2013     1     1     5 JFK    MIA   N619AA  AA      American Airlines Inc.  
##  4  2013     1     1     5 JFK    BQN   N804JB  B6      JetBlue Airways         
##  5  2013     1     1     6 LGA    ATL   N668DN  DL      Delta Air Lines Inc.    
##  6  2013     1     1     5 EWR    ORD   N39463  UA      United Air Lines Inc.   
##  7  2013     1     1     6 EWR    FLL   N516JB  B6      JetBlue Airways         
##  8  2013     1     1     6 LGA    IAD   N829AS  EV      ExpressJet Airlines Inc.
##  9  2013     1     1     6 JFK    MCO   N593JB  B6      JetBlue Airways         
## 10  2013     1     1     6 LGA    ORD   N3ALAA  AA      American Airlines Inc.  
## # … with 90 more rows

Using a pipe, the same result can be achieved with the following:

# alternate version, using a pipe
flights2 %>%
  left_join(airlines, by = "carrier")
## # A tibble: 100 × 9
##     year month   day  hour origin dest  tailnum carrier name                    
##    <int> <int> <int> <dbl> <chr>  <chr> <chr>   <chr>   <chr>                   
##  1  2013     1     1     5 EWR    IAH   N14228  UA      United Air Lines Inc.   
##  2  2013     1     1     5 LGA    IAH   N24211  UA      United Air Lines Inc.   
##  3  2013     1     1     5 JFK    MIA   N619AA  AA      American Airlines Inc.  
##  4  2013     1     1     5 JFK    BQN   N804JB  B6      JetBlue Airways         
##  5  2013     1     1     6 LGA    ATL   N668DN  DL      Delta Air Lines Inc.    
##  6  2013     1     1     5 EWR    ORD   N39463  UA      United Air Lines Inc.   
##  7  2013     1     1     6 EWR    FLL   N516JB  B6      JetBlue Airways         
##  8  2013     1     1     6 LGA    IAD   N829AS  EV      ExpressJet Airlines Inc.
##  9  2013     1     1     6 JFK    MCO   N593JB  B6      JetBlue Airways         
## 10  2013     1     1     6 LGA    ORD   N3ALAA  AA      American Airlines Inc.  
## # … with 90 more rows

Here’s an example of where the utility of the pipe operator can be seen. If we want to count the number of flights be each airline, we could use the “carrier” variable, which is the short code. But a more useful table would have the airline name. In this code, the join is followed by a group_by and then tally(), which produces a table with the airline name.

flights2 %>%
  left_join(airlines, by = "carrier") %>% 
  group_by(name) %>% 
  tally()
## # A tibble: 11 × 2
##    name                            n
##    <chr>                       <int>
##  1 AirTran Airways Corporation     1
##  2 Alaska Airlines Inc.            1
##  3 American Airlines Inc.         17
##  4 Delta Air Lines Inc.           13
##  5 Envoy Air                       6
##  6 ExpressJet Airlines Inc.        3
##  7 JetBlue Airways                25
##  8 Southwest Airlines Co.          1
##  9 United Air Lines Inc.          26
## 10 US Airways Inc.                 5
## 11 Virgin America                  2

14.6.4 Duplicate Keys

In real life, tables start to get more complex. It’s often the case that you will have tables that have duplicate keys in one or both of the tables.

The chunk below creates new tables table_a and table_b, where there are duplicate keys in one.

table_a <- tribble(~ key_var, ~ day_a,
             "key1", "Mon",
             "key1", "Tue",
             "key2", "Wed",
             "key2", "Thu")

table_b <- tribble(~ key_var, ~ veg_b,
             "key1", "carrot",
             "key2", "tomato")

14.6.4.1 Your turn

Join the tables with left_join(), with a as the table on the left.

Solution
# solution

left_join(table_a, table_b, key = "key_var")
## # A tibble: 4 × 3
##   key_var day_a veg_b 
##   <chr>   <chr> <chr> 
## 1 key1    Mon   carrot
## 2 key1    Tue   carrot
## 3 key2    Wed   tomato
## 4 key2    Thu   tomato

In this example, where the duplicates are on the left, the same value “carrot” gets joined to both “key1” cases.

A situation where there are duplicate keys in both tables is usually an error—there is no unique identifier of a single observation. (A question to ask yourself is “Is there are third table?”)

14.6.4.2 Your turn

Here’s different tables, where the key “key2” is duplicated in both.

table_m <- tribble(~ key_var, ~ month_m,
             "key1", "Jan",
             "key2", "Feb",
             "key2", "Mar")

table_n <- tribble(~ key_var, ~ ball_n,
             "key1", "foot",
             "key2", "basket",
             "key2", "base")

What does a left join do? How many rows does the resulting table have?

Solution
# solution

left_join(table_m, table_n, by = "key_var")
## # A tibble: 5 × 3
##   key_var month_m ball_n
##   <chr>   <chr>   <chr> 
## 1 key1    Jan     foot  
## 2 key2    Feb     basket
## 3 key2    Feb     base  
## 4 key2    Mar     basket
## 5 key2    Mar     base

The left_join() function adds both “basket” and “base” to each of the “Feb” and “Mar” records on the left. This leads to a duplication of the rows with “key2”—so the whole table jumps from 3 rows (what we would expect with a left_join()) to 5 rows.


14.7 Filtering joins

The other sort of joins filter observations from one data frame based on whether or not they match an observation in the other table.

There are two sorts:

  • 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.

Let’s go back to our original test tables again:

table_one <- tribble(
  ~key_var, ~colour_t1,
     "key1", "red",
     "key2", "blue",
     "key3", "yellow"
)

table_two <- tribble(
  ~key_var, ~fruit_t2,
     "key1", "apple",
     "key2", "blueberry",
     "key4", "banana"
)

Semi-join: only the observations in x that have a match in y.

Note that no variables from y appear in the result.

semi_join(table_one, table_two, by = "key_var")
## # A tibble: 2 × 2
##   key_var colour_t1
##   <chr>   <chr>    
## 1 key1    red      
## 2 key2    blue

Anti-join: returns the observations in x that don’t have a key match in y. Again, no values from y appear in the result.

anti_join(table_one, table_two, by = "key_var")
## # A tibble: 1 × 2
##   key_var colour_t1
##   <chr>   <chr>    
## 1 key3    yellow

14.8 More complex scenarios

14.8.1 Keys with different names

In some circumstances, you will encounter a situation where your key variables are named one thing in one table, and something quite different in another.

Here is a solution. In this example, the key variable in one table is named key_x and in the other it is key_y.

left_tbl <- tribble(
  ~key_x, ~val_x,
     1, "x1",
     2, "x2",
     3, "x3"
)

right_tbl <- tribble(
  ~key_y, ~val_y, ~val_y2,
     1, "y1", "Monday",
     2, "y2", "Tuesday",
     4, "y3", "Wednesday"
)

Here are two different ways of writing the same code:

# to specify key variables with different names:
left_join(left_tbl, right_tbl, by = c("key_x" = "key_y"))
## # A tibble: 3 × 4
##   key_x val_x val_y val_y2 
##   <dbl> <chr> <chr> <chr>  
## 1     1 x1    y1    Monday 
## 2     2 x2    y2    Tuesday
## 3     3 x3    <NA>  <NA>
left_tbl %>% 
  left_join(right_tbl, by = c("key_x" = "key_y"))
## # A tibble: 3 × 4
##   key_x val_x val_y val_y2 
##   <dbl> <chr> <chr> <chr>  
## 1     1 x1    y1    Monday 
## 2     2 x2    y2    Tuesday
## 3     3 x3    <NA>  <NA>

14.8.2 Join on multiple keys

In the {nycflights13} database (that is, the multiple related tables), we see that the “flights” table is linked to the “weather” table on five different variables. The “weather” table holds the primary key, where there is a unique row by the five variables “year”, “month”, “day”, “hour”, and “origin” (with the airport code).

head(weather)
## # A tibble: 6 × 15
##   origin  year month   day  hour  temp  dewp humid wind_dir wind_speed wind_gust precip pressure
##   <chr>  <int> <int> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>     <dbl>  <dbl>    <dbl>
## 1 EWR     2013     1     1     1  39.0  26.1  59.4      270      10.4         NA      0    1012 
## 2 EWR     2013     1     1     2  39.0  27.0  61.6      250       8.06        NA      0    1012.
## 3 EWR     2013     1     1     3  39.0  28.0  64.4      240      11.5         NA      0    1012.
## 4 EWR     2013     1     1     4  39.9  28.0  62.2      250      12.7         NA      0    1012.
## 5 EWR     2013     1     1     5  39.0  28.0  64.4      260      12.7         NA      0    1012.
## 6 EWR     2013     1     1     6  37.9  28.0  67.2      240      11.5         NA      0    1012.
## # … with 2 more variables: visib <dbl>, time_hour <dttm>

If we were analyzing the relationship between flight departure delays (in the “flights” table) and weather conditions, we would need to link these tables.

The first solution is to name all of the variables inside a by = c() argument.

# solution

left_join(flights, weather, by = c("year", "month", "day", "hour", "origin"))
## # A tibble: 336,776 × 29
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
##  1  2013     1     1      517            515         2      830            819        11 UA     
##  2  2013     1     1      533            529         4      850            830        20 UA     
##  3  2013     1     1      542            540         2      923            850        33 AA     
##  4  2013     1     1      544            545        -1     1004           1022       -18 B6     
##  5  2013     1     1      554            600        -6      812            837       -25 DL     
##  6  2013     1     1      554            558        -4      740            728        12 UA     
##  7  2013     1     1      555            600        -5      913            854        19 B6     
##  8  2013     1     1      557            600        -3      709            723       -14 EV     
##  9  2013     1     1      557            600        -3      838            846        -8 B6     
## 10  2013     1     1      558            600        -2      753            745         8 AA     
## # … with 336,766 more rows, and 19 more variables: flight <int>, tailnum <chr>, origin <chr>,
## #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour.x <dttm>,
## #   temp <dbl>, dewp <dbl>, humid <dbl>, wind_dir <dbl>, wind_speed <dbl>, wind_gust <dbl>,
## #   precip <dbl>, pressure <dbl>, visib <dbl>, time_hour.y <dttm>

In this second approach, the range of variables is specified with the : operator. Note that this is now inside a pipe, and the left table (“flights”) is named at the top of the pipe sequence.

left_join(flights, weather)
## # A tibble: 336,776 × 28
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
##  1  2013     1     1      517            515         2      830            819        11 UA     
##  2  2013     1     1      533            529         4      850            830        20 UA     
##  3  2013     1     1      542            540         2      923            850        33 AA     
##  4  2013     1     1      544            545        -1     1004           1022       -18 B6     
##  5  2013     1     1      554            600        -6      812            837       -25 DL     
##  6  2013     1     1      554            558        -4      740            728        12 UA     
##  7  2013     1     1      555            600        -5      913            854        19 B6     
##  8  2013     1     1      557            600        -3      709            723       -14 EV     
##  9  2013     1     1      557            600        -3      838            846        -8 B6     
## 10  2013     1     1      558            600        -2      753            745         8 AA     
## # … with 336,766 more rows, and 18 more variables: flight <int>, tailnum <chr>, origin <chr>,
## #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
## #   temp <dbl>, dewp <dbl>, humid <dbl>, wind_dir <dbl>, wind_speed <dbl>, wind_gust <dbl>,
## #   precip <dbl>, pressure <dbl>, visib <dbl>
flights %>% 
  left_join(select(weather, origin:temp))
## # A tibble: 336,776 × 20
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
##  1  2013     1     1      517            515         2      830            819        11 UA     
##  2  2013     1     1      533            529         4      850            830        20 UA     
##  3  2013     1     1      542            540         2      923            850        33 AA     
##  4  2013     1     1      544            545        -1     1004           1022       -18 B6     
##  5  2013     1     1      554            600        -6      812            837       -25 DL     
##  6  2013     1     1      554            558        -4      740            728        12 UA     
##  7  2013     1     1      555            600        -5      913            854        19 B6     
##  8  2013     1     1      557            600        -3      709            723       -14 EV     
##  9  2013     1     1      557            600        -3      838            846        -8 B6     
## 10  2013     1     1      558            600        -2      753            745         8 AA     
## # … with 336,766 more rows, and 10 more variables: flight <int>, tailnum <chr>, origin <chr>,
## #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
## #   temp <dbl>

14.8.3 Select specific columns

Using our left_tbl and right_tbl from above, here is a solution that embeds a pipe and select function inside the right_tbl call. Note that the comma follows the select(), not the name of the table.

# to specify variables to add to new table
# one solution: put select in the table naming 
full_join(left_tbl, 
          right_tbl %>% select(key_y, val_y2), 
          by = c("key_x" = "key_y"))
## # A tibble: 4 × 3
##   key_x val_x val_y2   
##   <dbl> <chr> <chr>    
## 1     1 x1    Monday   
## 2     2 x2    Tuesday  
## 3     3 x3    <NA>     
## 4     4 <NA>  Wednesday

In the {nycflights13} case, where we are joining on multiple keys and then wanting just the temperature variable from the right (“weather”) table, here are three different left_join() solutions:

# solution

flights %>% 
  left_join(weather %>% select("year", "month", "day", "hour", "origin", "temp"),
            by = c("year", "month", "day", "hour", "origin"))
## # A tibble: 336,776 × 20
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
##  1  2013     1     1      517            515         2      830            819        11 UA     
##  2  2013     1     1      533            529         4      850            830        20 UA     
##  3  2013     1     1      542            540         2      923            850        33 AA     
##  4  2013     1     1      544            545        -1     1004           1022       -18 B6     
##  5  2013     1     1      554            600        -6      812            837       -25 DL     
##  6  2013     1     1      554            558        -4      740            728        12 UA     
##  7  2013     1     1      555            600        -5      913            854        19 B6     
##  8  2013     1     1      557            600        -3      709            723       -14 EV     
##  9  2013     1     1      557            600        -3      838            846        -8 B6     
## 10  2013     1     1      558            600        -2      753            745         8 AA     
## # … with 336,766 more rows, and 10 more variables: flight <int>, tailnum <chr>, origin <chr>,
## #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
## #   temp <dbl>
# slightly different syntax:
flights %>% 
  left_join(select(weather, "year", "month", "day", "hour", "origin", "temp"),
            by = c("year", "month", "day", "hour", "origin"))
## # A tibble: 336,776 × 20
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
##  1  2013     1     1      517            515         2      830            819        11 UA     
##  2  2013     1     1      533            529         4      850            830        20 UA     
##  3  2013     1     1      542            540         2      923            850        33 AA     
##  4  2013     1     1      544            545        -1     1004           1022       -18 B6     
##  5  2013     1     1      554            600        -6      812            837       -25 DL     
##  6  2013     1     1      554            558        -4      740            728        12 UA     
##  7  2013     1     1      555            600        -5      913            854        19 B6     
##  8  2013     1     1      557            600        -3      709            723       -14 EV     
##  9  2013     1     1      557            600        -3      838            846        -8 B6     
## 10  2013     1     1      558            600        -2      753            745         8 AA     
## # … with 336,766 more rows, and 10 more variables: flight <int>, tailnum <chr>, origin <chr>,
## #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
## #   temp <dbl>
# let {dplyr} decide the variables on which to join
flights %>% 
  left_join(select(weather, origin:temp))
## # A tibble: 336,776 × 20
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
##  1  2013     1     1      517            515         2      830            819        11 UA     
##  2  2013     1     1      533            529         4      850            830        20 UA     
##  3  2013     1     1      542            540         2      923            850        33 AA     
##  4  2013     1     1      544            545        -1     1004           1022       -18 B6     
##  5  2013     1     1      554            600        -6      812            837       -25 DL     
##  6  2013     1     1      554            558        -4      740            728        12 UA     
##  7  2013     1     1      555            600        -5      913            854        19 B6     
##  8  2013     1     1      557            600        -3      709            723       -14 EV     
##  9  2013     1     1      557            600        -3      838            846        -8 B6     
## 10  2013     1     1      558            600        -2      753            745         8 AA     
## # … with 336,766 more rows, and 10 more variables: flight <int>, tailnum <chr>, origin <chr>,
## #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
## #   temp <dbl>

14.8.4 Non-key variables with the same names

Here’s another case—let’s imagine we have two tables, called “orders” and “shipments”. They have two variables with the same names, “order_number” and “dollar_value”. The “order_number” is a unique ID (the key variable), but the dollars associated with the order might be different than the shipment—sometimes items are out of stock, so they can’t be sent, so the value of the shipment is less than what was ordered.

orders <- tribble(
  ~order_number, ~dollar_value,
  "x1", 11,
  "x2", 12,
  "x3", 13,
  "x4", 14
)

shipments <- tribble(
  ~order_number, ~dollar_value,
  "x1", 11,
  "x2", 11,
  "x3", 13,
  "x4", 4
)

When we join the tables, we will use “order_number” to join them. There will be only one column in the resulting dataframe with this name.

A variable called “dollar_value” exists in both tables, but means different things. You and I know that one could be called “dollar_value_orders” and the other “dollar_value_shipments”—but R doesn’t know that, so it renames them “dollar_value.x” and “dollar_value.y”. The one with the “.x” at the end will be the left table, and “.y” will be the right table.

orders_shipped <- orders %>% 
  full_join(shipments, by = "order_number")

orders_shipped
## # A tibble: 4 x 3
##   order_number dollar_value.x dollar_value.y
##   <chr>                 <dbl>          <dbl>
## 1 x1                       11             11
## 2 x2                       12             11
## 3 x3                       13             13
## 4 x4                       14              4

You could then rename “dollar_value.x” and “dollar_value.y” to make it clear which is which. In this example, we also then add a column showing the percentage of the original order that was shipped.

orders_shipped %>% 
  rename("dollar_value_order" = dollar_value.x,
         "dollar_value_shipment" = dollar_value.y) %>% 
  mutate(filled_pct = round((dollar_value_shipment / dollar_value_order) *100, 1))
## # A tibble: 4 x 4
##   order_number dollar_value_order dollar_value_shipment filled_pct
##   <chr>                     <dbl>                 <dbl>      <dbl>
## 1 x1                           11                    11      100  
## 2 x2                           12                    11       91.7
## 3 x3                           13                    13      100  
## 4 x4                           14                     4       28.6

-30-