Week 9: Apply it to your data 8

1. Import your data

Import two related datasets from TidyTuesday Project.

episodes <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-08-08/episodes.csv')

## Rows: 300 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): title, guest
## dbl  (4): season, episode_overall, episode_season, guest_appearance_number
## lgl  (1): finished
## date (1): original_release
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

seasons <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-08-08/seasons.csv')

## Rows: 21 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): note
## dbl  (2): season, episodes
## date (2): original_release, last_release
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

2. Make data small

set.seed(1235)
episodes_small <- episodes %>% select(season, original_release, finished) %>% sample_n(10, replace = TRUE)

seasons_small <- seasons %>% select(season, original_release, note) %>% sample_n(10, replace = TRUE)

episodes_small

## # A tibble: 10 × 3
##    season original_release finished
##     <dbl> <date>           <lgl>   
##  1      2 2016-07-29       TRUE    
##  2     13 2020-12-10       TRUE    
##  3     13 2020-10-08       TRUE    
##  4      5 2018-04-26       TRUE    
##  5     14 2021-02-04       TRUE    
##  6      4 2017-11-30       TRUE    
##  7      8 2019-02-14       TRUE    
##  8     17 2022-03-24       FALSE   
##  9     15 2021-08-05       TRUE    
## 10     19 2022-10-06       TRUE

seasons_small

## # A tibble: 10 × 3
##    season original_release note 
##     <dbl> <date>           <chr>
##  1      5 2018-01-18       <NA> 
##  2     12 2020-06-25       <NA> 
##  3      2 2015-12-10       <NA> 
##  4      8 2019-01-24       <NA> 
##  5      8 2019-01-24       <NA> 
##  6     12 2020-06-25       <NA> 
##  7     13 2020-10-01       <NA> 
##  8      4 2017-07-20       <NA> 
##  9      2 2015-12-10       <NA> 
## 10     15 2021-05-27       <NA>

Describe the two datasets:

Data1

• Columns: Season, When episode released on Youtube, If Contestant finished all sauces
• Rows: 10

Data 2

• Columns: Season, When episode released on Youtube, Notes about Season
• Rows: 10

3. inner_join

Describe the resulting data:

• Columns: Season, Original Release, Notes, Finished
• Rows: 0

How is it different from the original two datasets? 0 columns compared to original 10 4 rows out of 6 combined

seasons_small %>% inner_join(episodes_small)

## Joining with `by = join_by(season, original_release)`

## # A tibble: 0 × 4
## # ℹ 4 variables: season <dbl>, original_release <date>, note <chr>,
## #   finished <lgl>

4. left_join

Describe the resulting data:

• Columns: Season, Original Release, Finished, Note
• Rows: 10

How is it different from the original two datasets? Includes 4 columns

episodes_small %>% left_join(seasons_small)

## Joining with `by = join_by(season, original_release)`

## # A tibble: 10 × 4
##    season original_release finished note 
##     <dbl> <date>           <lgl>    <chr>
##  1      2 2016-07-29       TRUE     <NA> 
##  2     13 2020-12-10       TRUE     <NA> 
##  3     13 2020-10-08       TRUE     <NA> 
##  4      5 2018-04-26       TRUE     <NA> 
##  5     14 2021-02-04       TRUE     <NA> 
##  6      4 2017-11-30       TRUE     <NA> 
##  7      8 2019-02-14       TRUE     <NA> 
##  8     17 2022-03-24       FALSE    <NA> 
##  9     15 2021-08-05       TRUE     <NA> 
## 10     19 2022-10-06       TRUE     <NA>

5. right_join

Describe the resulting data:

• Columns: Season, Original Release, Finished, Note
• Rows:

How is it different from the original two datasets? Different numbers for season, finished has NA instead of True or False

episodes_small %>% right_join(seasons_small)

## Joining with `by = join_by(season, original_release)`

## # A tibble: 10 × 4
##    season original_release finished note 
##     <dbl> <date>           <lgl>    <chr>
##  1      5 2018-01-18       NA       <NA> 
##  2     12 2020-06-25       NA       <NA> 
##  3      2 2015-12-10       NA       <NA> 
##  4      8 2019-01-24       NA       <NA> 
##  5      8 2019-01-24       NA       <NA> 
##  6     12 2020-06-25       NA       <NA> 
##  7     13 2020-10-01       NA       <NA> 
##  8      4 2017-07-20       NA       <NA> 
##  9      2 2015-12-10       NA       <NA> 
## 10     15 2021-05-27       NA       <NA>

6. full_join

Describe the resulting data:

• Columns: Season, Original Release, Finished, Note
• Rows: 20

How is it different from the original two datasets? Has 20 rows Finished includes NA as well as True and False

episodes_small %>% full_join(seasons_small)

## Joining with `by = join_by(season, original_release)`

## # A tibble: 20 × 4
##    season original_release finished note 
##     <dbl> <date>           <lgl>    <chr>
##  1      2 2016-07-29       TRUE     <NA> 
##  2     13 2020-12-10       TRUE     <NA> 
##  3     13 2020-10-08       TRUE     <NA> 
##  4      5 2018-04-26       TRUE     <NA> 
##  5     14 2021-02-04       TRUE     <NA> 
##  6      4 2017-11-30       TRUE     <NA> 
##  7      8 2019-02-14       TRUE     <NA> 
##  8     17 2022-03-24       FALSE    <NA> 
##  9     15 2021-08-05       TRUE     <NA> 
## 10     19 2022-10-06       TRUE     <NA> 
## 11      5 2018-01-18       NA       <NA> 
## 12     12 2020-06-25       NA       <NA> 
## 13      2 2015-12-10       NA       <NA> 
## 14      8 2019-01-24       NA       <NA> 
## 15      8 2019-01-24       NA       <NA> 
## 16     12 2020-06-25       NA       <NA> 
## 17     13 2020-10-01       NA       <NA> 
## 18      4 2017-07-20       NA       <NA> 
## 19      2 2015-12-10       NA       <NA> 
## 20     15 2021-05-27       NA       <NA>

7. semi_join

Describe the resulting data:

• Columns: Season, Orignal Release, Finished
• Rows: 0

How is it different from the original two datasets? 0 Columns in this set

episodes_small %>% semi_join(seasons_small)

## Joining with `by = join_by(season, original_release)`

## # A tibble: 0 × 3
## # ℹ 3 variables: season <dbl>, original_release <date>, finished <lgl>

8. anti_join

Describe the resulting data:

• Columns: Season, Original Release, Finished
• Rows: 10

How is it different from the original two datasets? Does not include Notes

episodes_small %>% anti_join(seasons_small)

## Joining with `by = join_by(season, original_release)`

## # A tibble: 10 × 3
##    season original_release finished
##     <dbl> <date>           <lgl>   
##  1      2 2016-07-29       TRUE    
##  2     13 2020-12-10       TRUE    
##  3     13 2020-10-08       TRUE    
##  4      5 2018-04-26       TRUE    
##  5     14 2021-02-04       TRUE    
##  6      4 2017-11-30       TRUE    
##  7      8 2019-02-14       TRUE    
##  8     17 2022-03-24       FALSE   
##  9     15 2021-08-05       TRUE    
## 10     19 2022-10-06       TRUE