Joining Data

Lecture 7

Author
Affiliation

Katie Solarz

Duke University
STA 199 Summer 2026: Session I

Published

May 22, 2026

Announcements / Reminders

  • Office hours are today from 1:00-3:00 on Zoom; I will share a link via a Canvas announcement

  • Lab 2 is now due Monday, 5/25 at 11:59pm to Gradescope

  • Questions about / feedback on lab yesterday?

Outline

  • Last Time: Started learning about data transformation via pivoting!

  • Today:

    • Review from last time

    • Joining data (working with multiple data frames)

AE-06

Recap: Pivoting

Recap: Pivot Functions

Pivot longer…

data_set |>
  pivot_longer(
    cols = colums_to_pivot,
    names_to = "var_name_for_former_column_names", 
    values_to = "var_name_for_values"
  )

… or wider

data_set |>
  pivot_wider( 
    names_from = var_name_with_desired_cols, 
    values_to = var_name_with_desired_vals
  )

Joining Data

Joining Data

What happens if we want to access information from two different data sets at the same time (e.g., for EDA / plotting purposes)?

Joining Data: Sample Scenario

population
# A tibble: 217 × 3
   country              year population
   <chr>               <dbl>      <dbl>
 1 Afghanistan          2022    41129. 
 2 Albania              2022     2778. 
 3 Algeria              2022    44903. 
 4 American Samoa       2022       44.3
 5 Andorra              2022       79.8
 6 Angola               2022    35589. 
 7 Antigua and Barbuda  2022       93.8
 8 Argentina            2022    46235. 
 9 Armenia              2022     2780. 
10 Aruba                2022      106. 
# ℹ 207 more rows

  • We want to know about population in different continents.

  • We could use mutate to create a continent variable, but that would be painful… we would have to manually tell R which continent each country in our df belongs to

Joining Data: Sample Scenario

population
# A tibble: 217 × 3
   country              year population
   <chr>               <dbl>      <dbl>
 1 Afghanistan          2022    41129. 
 2 Albania              2022     2778. 
 3 Algeria              2022    44903. 
 4 American Samoa       2022       44.3
 5 Andorra              2022       79.8
 6 Angola               2022    35589. 
 7 Antigua and Barbuda  2022       93.8
 8 Argentina            2022    46235. 
 9 Armenia              2022     2780. 
10 Aruba                2022      106. 
# ℹ 207 more rows
continent
# A tibble: 285 × 4
   entity                code      year continent    
   <chr>                 <chr>    <dbl> <chr>        
 1 Abkhazia              OWID_ABK  2015 Asia         
 2 Afghanistan           AFG       2015 Asia         
 3 Akrotiri and Dhekelia OWID_AKD  2015 Asia         
 4 Aland Islands         ALA       2015 Europe       
 5 Albania               ALB       2015 Europe       
 6 Algeria               DZA       2015 Africa       
 7 American Samoa        ASM       2015 Oceania      
 8 Andorra               AND       2015 Europe       
 9 Angola                AGO       2015 Africa       
10 Anguilla              AIA       2015 North America
# ℹ 275 more rows

Joining: Example Data

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

Joining: Left Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

left_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2
3 X3 NA

Joining: Left Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

left_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2
3 X3 NA

Joining: Right Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

right_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2
4 NA Y4

Joining: Right Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

right_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2
4 NA Y4

Joining: Full Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

right_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2
3 X3 NA
4 NA Y4

Joining: Full Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

right_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2
3 X3 NA
4 NA Y4

Joining: Inner Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

inner_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2

Joining: Inner Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

inner_join(x, y)

id X Y
1 X1 Y1
2 X2 Y2

Joining: Semi Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

semi_join(x, y)

id X
1 X1
2 X2

Joining: Semi Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

semi_join(x, y)

id X
1 X1
2 X2

Joining: Anti Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

anti_join(x, y)

id X
3 X3

Joining: Anti Join

x

id X
1 X1
2 X2
3 X3

y

id Y
1 Y1
2 Y2
4 Y4

anti_join(x, y)

id X
3 X3

Summary of Join Types

More Notes on Join

  • In the following examples, I use left_join() to demonstrate

  • The same concept holds for other types of joins!

  • Idea: how do we specify which columns we want to join with?

Join: Which columns?

How can I specify which column to join by?

x

id_X X
1 X1
2 X2
3 X3

y

id_Y Y
1 Y1
2 Y2
4 Y4

goal: left join

id_X X Y
1 X1 Y1
2 X2 Y2
3 X3 NA 
left_join(x, y, 
          by = join_by(id_X == id_Y))

What about the pipe?

The following two pieces of code are equivalent:

left_join(x, y, 
          by = join_by(id_X == id_Y))



x |> left_join(y, 
               by = join_by(id_X == id_Y))

Let’s save!

Most often, you will want to save the result of a join to a new data frame.

x_y <- x |> left_join(y, 
                      by = join_by(id_X == id_Y))

Another potential scenario…

  • In some cases, it may be helpful to join a dataset with itself

  • When joining a dataset with itself, since the “left” and “right data frames are exactly the same, inner_join(), left_join(), right_join(), and full_join() will”collapse” to the same solution

  • To demonstrate the utility, I will proceed with inner_join()

Joining a data frame with… itself?

“Toy” prescription data frame

rx_data
# A tibble: 7 × 4
  patid ndc_code   rx_start   rx_end    
  <dbl> <chr>      <date>     <date>    
1  1001 00074-4333 2025-01-01 2025-01-03
2  1001 00074-4333 2025-01-04 2025-01-30
3  1001 54868-5277 2025-02-10 2025-03-05
4  1002 59011-4420 2025-03-01 2025-03-15
5  1002 59011-4420 2025-03-20 2025-04-10
6  1003 00406-0512 2025-04-05 2025-04-07
7  1003 00406-0512 2025-04-08 2025-05-01

Joining a data frame with… itself?

Why the suffix = argument? What about the warning?

# Join the data to itself by patient ID
rx_permute <- rx_data |>
  inner_join(
    rx_data,
    by = "patid",
    suffix = c("_ref", "_comp")
  )
Warning in inner_join(rx_data, rx_data, by = "patid", suffix = c("_ref", : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 1 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.

Joining a data frame with… itself?

# Join the data to itself by patient ID
rx_permute <- rx_data |>
  inner_join(
    rx_data,
    by = "patid",
    suffix = c("_ref", "_comp"),
    relationship = "many-to-many"
  )

rx_permute
# A tibble: 17 × 7
   patid ndc_code_ref rx_start_ref rx_end_ref ndc_code_comp
   <dbl> <chr>        <date>       <date>     <chr>        
 1  1001 00074-4333   2025-01-01   2025-01-03 00074-4333   
 2  1001 00074-4333   2025-01-01   2025-01-03 00074-4333   
 3  1001 00074-4333   2025-01-01   2025-01-03 54868-5277   
 4  1001 00074-4333   2025-01-04   2025-01-30 00074-4333   
 5  1001 00074-4333   2025-01-04   2025-01-30 00074-4333   
 6  1001 00074-4333   2025-01-04   2025-01-30 54868-5277   
 7  1001 54868-5277   2025-02-10   2025-03-05 00074-4333   
 8  1001 54868-5277   2025-02-10   2025-03-05 00074-4333   
 9  1001 54868-5277   2025-02-10   2025-03-05 54868-5277   
10  1002 59011-4420   2025-03-01   2025-03-15 59011-4420   
11  1002 59011-4420   2025-03-01   2025-03-15 59011-4420   
12  1002 59011-4420   2025-03-20   2025-04-10 59011-4420   
13  1002 59011-4420   2025-03-20   2025-04-10 59011-4420   
14  1003 00406-0512   2025-04-05   2025-04-07 00406-0512   
15  1003 00406-0512   2025-04-05   2025-04-07 00406-0512   
16  1003 00406-0512   2025-04-08   2025-05-01 00406-0512   
17  1003 00406-0512   2025-04-08   2025-05-01 00406-0512   
# ℹ 2 more variables: rx_start_comp <date>, rx_end_comp <date>

Joining a data frame with… itself?

  • Now, I can calculate the time-gap (in days) between each patient’s prescriptions

  • This might be helpful if I’m considering imputing (i.e., filling in missing data) data in between prescriptions

  • Or, if I have Rx fill data and I’m trying to measure adherence (i.e., is a patient picking up their refills in a timely manner, or are they going days / weeks without medication?)

# Keep only records where the comparison prescription starts later
rx_permute |>
  filter(rx_start_comp > rx_end_ref) |>
  # Calculate the gap between prescriptions
  mutate(
    gap_days = as.numeric(rx_start_comp - rx_end_ref)) |>
  select(patid, rx_end_ref, rx_start_comp, gap_days)
# A tibble: 5 × 4
  patid rx_end_ref rx_start_comp gap_days
  <dbl> <date>     <date>           <dbl>
1  1001 2025-01-03 2025-01-04           1
2  1001 2025-01-03 2025-02-10          38
3  1001 2025-01-30 2025-02-10          11
4  1002 2025-03-15 2025-03-20           5
5  1003 2025-04-07 2025-04-08           1

AE 07

Goal: Practice with joins!