Week 9: Apply it to your data 8

1. Import your data

Import two related datasets from TidyTuesday Project.

sevens <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-05-24/sevens.csv')

## Rows: 7966 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (10): team_1, score_1, score_2, team_2, venue, tournament, stage, winne...
## dbl   (5): row_id, t1_game_no, t2_game_no, series, margin
## date  (1): date
## 
## 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.

fifteens <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-05-24/fifteens.csv')

## Rows: 1468 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): team_1, team_2, venue, tournament, home_away_win, winner, loser
## dbl  (7): test_no, score_1, score_2, home_test_no, away_test_no, series_no, ...
## date (1): date
## 
## 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

Describe the two datasets:

Data1: Fifteens

• Columns: test_no, team_1, winner
• Rows: 10 rows

Data 2: sevens

• Columns: , row_id, team_1, winner
• Rows: 10 rows

set.seed(1234)
fifteens_small <- fifteens %>% select(test_no, team_1 , winner) %>% sample_n(10)
sevens_small <- sevens %>% select(margin, team_1, winner) %>% sample_n(10)

fifteens_small

## # A tibble: 10 × 3
##    test_no team_1         winner        
##      <dbl> <chr>          <chr>         
##  1    1308 Scotland       Wales         
##  2    1018 Kazakhstan     Kazakhstan    
##  3    1125 Switzerland    Switzerland   
##  4    1004 New Zealand    New Zealand   
##  5     623 Italy          Ireland       
##  6     905 England        England       
##  7     645 France         France        
##  8     934 Ireland        Ireland       
##  9     400 Germany        Netherlands   
## 10     900 Cayman Islands Cayman Islands

sevens_small

## # A tibble: 10 × 3
##    margin team_1      winner     
##     <dbl> <chr>       <chr>      
##  1     38 China       China      
##  2     19 Australia   Australia  
##  3      3 France      France     
##  4      5 Australia   New Zealand
##  5     12 Taiwan      Taiwan     
##  6     29 Australia   Australia  
##  7      5 Ireland     Ireland    
##  8      5 Spain       Spain      
##  9     30 New Zealand New Zealand
## 10     21 China       China

3. inner_join

Describe the resulting data:

• Columns: test_no, team_1, winner, margin
• Rows: 3 rows

How is it different from the original two datasets?

• 3 rows compared to 10 rows from the other datasets
• all columns from the two datasets

fifteens_small %>% inner_join(sevens_small, by = c("team_1", "winner"))

## # A tibble: 3 × 4
##   test_no team_1      winner      margin
##     <dbl> <chr>       <chr>        <dbl>
## 1    1004 New Zealand New Zealand     30
## 2     645 France      France           3
## 3     934 Ireland     Ireland          5

4. left_join

Describe the resulting data:

• Columns: test_no, team_1, winner, margin
• Rows: 10 rows

How is it different from the original two datasets?

• In terms of rows, this one had the same number of rows as the original dataset, but differed from the inner join dataset

fifteens_small %>% left_join(sevens_small, by = c("team_1", "winner"))

## # A tibble: 10 × 4
##    test_no team_1         winner         margin
##      <dbl> <chr>          <chr>           <dbl>
##  1    1308 Scotland       Wales              NA
##  2    1018 Kazakhstan     Kazakhstan         NA
##  3    1125 Switzerland    Switzerland        NA
##  4    1004 New Zealand    New Zealand        30
##  5     623 Italy          Ireland            NA
##  6     905 England        England            NA
##  7     645 France         France              3
##  8     934 Ireland        Ireland             5
##  9     400 Germany        Netherlands        NA
## 10     900 Cayman Islands Cayman Islands     NA

5. right_join

Describe the resulting data:

• Columns: test_no, team_1, winner, margin
• Rows: 10 rows

How is it different from the original two datasets?

• The right join dataset differs from the original two because it provides different results. However, the right join dataset is similar to the original two datasets because they have the same number of rows.

fifteens_small %>% right_join(sevens_small, by = c("team_1", "winner"))

## # A tibble: 10 × 4
##    test_no team_1      winner      margin
##      <dbl> <chr>       <chr>        <dbl>
##  1    1004 New Zealand New Zealand     30
##  2     645 France      France           3
##  3     934 Ireland     Ireland          5
##  4      NA China       China           38
##  5      NA Australia   Australia       19
##  6      NA Australia   New Zealand      5
##  7      NA Taiwan      Taiwan          12
##  8      NA Australia   Australia       29
##  9      NA Spain       Spain            5
## 10      NA China       China           21

6. full_join

Describe the resulting data:

• Columns: test_no, team_1, winner, margin
• Rows: 17 rows

How is it different from the original two datasets?

• The full join dataset is different from the original two datasets because it has 17 rows, while the original two datasets only had 10 rows.

fifteens_small %>% full_join(sevens_small, by = c("team_1", "winner"))

## # A tibble: 17 × 4
##    test_no team_1         winner         margin
##      <dbl> <chr>          <chr>           <dbl>
##  1    1308 Scotland       Wales              NA
##  2    1018 Kazakhstan     Kazakhstan         NA
##  3    1125 Switzerland    Switzerland        NA
##  4    1004 New Zealand    New Zealand        30
##  5     623 Italy          Ireland            NA
##  6     905 England        England            NA
##  7     645 France         France              3
##  8     934 Ireland        Ireland             5
##  9     400 Germany        Netherlands        NA
## 10     900 Cayman Islands Cayman Islands     NA
## 11      NA China          China              38
## 12      NA Australia      Australia          19
## 13      NA Australia      New Zealand         5
## 14      NA Taiwan         Taiwan             12
## 15      NA Australia      Australia          29
## 16      NA Spain          Spain               5
## 17      NA China          China              21

7. semi_join

Describe the resulting data:

• Columns: test_no, team_1, winner
• Rows: 3 rows

How is it different from the original two datasets?

• The semi join dataset differs from the original two datasets because it only has 3 rows, while the original had 10. This dataset also had one less column with only 3 columns.

fifteens_small %>% semi_join(sevens_small, by = c("team_1", "winner"))

## # A tibble: 3 × 3
##   test_no team_1      winner     
##     <dbl> <chr>       <chr>      
## 1    1004 New Zealand New Zealand
## 2     645 France      France     
## 3     934 Ireland     Ireland

8. anti_join

Describe the resulting data:

• Columns: test_no, team_1, winner
• Rows: 7 rows

How is it different from the original two datasets?

• The anti join dataset differs from the original two datasets because it only has seven rows, while the original two datasets had 10 rows.

fifteens_small %>% anti_join(sevens_small, by = c("team_1", "winner"))

## # A tibble: 7 × 3
##   test_no team_1         winner        
##     <dbl> <chr>          <chr>         
## 1    1308 Scotland       Wales         
## 2    1018 Kazakhstan     Kazakhstan    
## 3    1125 Switzerland    Switzerland   
## 4     623 Italy          Ireland       
## 5     905 England        England       
## 6     400 Germany        Netherlands   
## 7     900 Cayman Islands Cayman Islands