Working with multiple data frames

Lecture 5

Dr. Benjamin Soltoff

Cornell University
INFO 5001 - Fall 2024

September 12, 2024

Announcements

Announcements

• Complete the team team matching survey by noon today
• Lab tomorrow
• Homework 02

fill vs. color

• Use fill when a geom is filled
• Use color when a geom is outlined

Relational joins

Introduction to relational data

• Multiple tables of data that when combined together accomplish goals
• Relations define the important element, not just the individual tables
• Relations are defined between a pair of tables
• Relational verbs
  • Mutating joins
  • Filtering joins

Comic book characters

Name	Alignment	Gender	Publisher
Deadpool	Chaotic	Male	Marvel
Batman	Good	Male	DC
Sabrina	Good	Female	Archie Comics

Publishers

Publisher	Year founded
DC	1934
Marvel	1939
Image	1992

Mutating joins

inner_join()

inner_join()

inner_join(x = superheroes, y = publishers, by = join_by(Publisher))

# A tibble: 2 × 5
  Name     Alignment Gender Publisher `Year\nfounded`
  <chr>    <chr>     <chr>  <chr>               <dbl>
1 Deadpool Chaotic   Male   Marvel               1939
2 Batman   Good      Male   DC                   1934

left_join()

left_join()

left_join(x = superheroes, y = publishers, by = join_by(Publisher))

# A tibble: 3 × 5
  Name     Alignment Gender Publisher        `Year\nfounded`
  <chr>    <chr>     <chr>  <chr>                      <dbl>
1 Deadpool Chaotic   Male   "Marvel"                    1939
2 Batman   Good      Male   "DC"                        1934
3 Sabrina  Good      Female "Archie\nComics"              NA

right_join()

right_join()

right_join(x = superheroes, y = publishers, by = join_by(Publisher))

# A tibble: 3 × 5
  Name     Alignment Gender Publisher `Year\nfounded`
  <chr>    <chr>     <chr>  <chr>               <dbl>
1 Deadpool Chaotic   Male   Marvel               1939
2 Batman   Good      Male   DC                   1934
3 <NA>     <NA>      <NA>   Image                1992

right_join() reversed

full_join()

full_join()

full_join(x = superheroes, y = publishers, by = join_by(Publisher))

# A tibble: 4 × 5
  Name     Alignment Gender Publisher        `Year\nfounded`
  <chr>    <chr>     <chr>  <chr>                      <dbl>
1 Deadpool Chaotic   Male   "Marvel"                    1939
2 Batman   Good      Male   "DC"                        1934
3 Sabrina  Good      Female "Archie\nComics"              NA
4 <NA>     <NA>      <NA>   "Image"                     1992

Filtering joins

semi_join()

semi_join()

semi_join(x = superheroes, y = publishers, by = join_by(Publisher))

# A tibble: 2 × 4
  Name     Alignment Gender Publisher
  <chr>    <chr>     <chr>  <chr>    
1 Deadpool Chaotic   Male   Marvel   
2 Batman   Good      Male   DC       

anti_join()

anti_join()

anti_join(x = superheroes, y = publishers, by = join_by(Publisher))

# A tibble: 1 × 4
  Name    Alignment Gender Publisher       
  <chr>   <chr>     <chr>  <chr>           
1 Sabrina Good      Female "Archie\nComics"

Application exercise

Goal

Join data from multiple data frames, summarize it, and create this plot.

ae-03

• Go to the course GitHub org and find your ae-03 (repo name will be suffixed with your GitHub name).
• Clone the repo in RStudio, run renv::restore() to install the required packages, open the Quarto document in the repo, and follow along and complete the exercises.
• Render, commit, and push your edits by the AE deadline – end of the day

Recap of AE

• Use the *_join() function appropriate for your analysis
• Leverage the difference between mutating and filtering joins

TidyTuesday