Week 9: Apply it to your data 8

1. Import your data

Import two related datasets from TidyTuesday Project.

olympics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-07-27/olympics.csv')

## Rows: 271116 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): name, sex, team, noc, games, season, city, sport, event, medal
## dbl  (5): id, age, height, weight, year
## 
## 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.

youtube <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-03-02/youtube.csv')

## Rows: 247 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (10): brand, superbowl_ads_dot_com_url, youtube_url, id, kind, etag, ti...
## dbl   (7): year, view_count, like_count, dislike_count, favorite_count, comm...
## lgl   (7): funny, show_product_quickly, patriotic, celebrity, danger, animal...
## dttm  (1): published_at
## 
## 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: olympics

• Columns: age, height, weight
• Rows:120

Data 2: youtube

• Columns: brand, year, funny
• Rows: 10

set.seed(1234)
olympics_small <- olympics %>% select(age, height, weight) %>% sample_n(10)
youtube_small <- youtube %>% select(brand, year, funny) %>% sample_n(10)

olympics_small

## # A tibble: 10 × 3
##      age height weight
##    <dbl>  <dbl>  <dbl>
##  1    37    187    110
##  2    30    178     67
##  3    20    161     52
##  4    27    170     63
##  5    21     NA     NA
##  6    18    152     49
##  7    17    185     85
##  8    27     NA     NA
##  9    29    184     85
## 10    28    179     NA

youtube_small

## # A tibble: 10 × 3
##    brand      year funny
##    <chr>     <dbl> <lgl>
##  1 Doritos    2008 TRUE 
##  2 Pepsi      2018 FALSE
##  3 NFL        2018 TRUE 
##  4 Bud Light  2020 TRUE 
##  5 Toyota     2014 TRUE 
##  6 Budweiser  2016 FALSE
##  7 Kia        2013 TRUE 
##  8 Doritos    2013 TRUE 
##  9 Kia        2015 TRUE 
## 10 Budweiser  2012 FALSE

3. inner_join

Describe the resulting data:

• Columns: age, height, weight
• Rows: 1

How is it different from the original two datasets? * 1 row compared to 10

library(dplyr)

olympics_small <- olympics_small %>% mutate(id = row_number())
youtube_small <- youtube_small %>% mutate(id = row_number())

joined_data <- inner_join(olympics_small, youtube_small, by = "id") %>% select(-id)

4. left_join

Describe the resulting data:

• Columns:
• Rows:

How is it different from the original two datasets?

olympics_small <- olympics_small %>% mutate(id = row_number())
youtube_small <- youtube_small %>% mutate(id = row_number())

left_joined_data <- left_join(olympics_small, youtube_small, by = "id") %>% select(-id)

5. right_join

Describe the resulting data:

• Columns:
• Rows:

How is it different from the original two datasets?

olympics_small <- olympics_small %>% mutate(id = row_number())
youtube_small <- youtube_small %>% mutate(id = row_number())

right_joined_data <- right_join(olympics_small, youtube_small, by = "id") %>% select(-id)

6. full_join

Describe the resulting data:

• Columns:
• Rows:

How is it different from the original two datasets?

olympics_small <- olympics_small %>% mutate(id = row_number())
youtube_small <- youtube_small %>% mutate(id = row_number())

full_joined_data <- full_join(olympics_small, youtube_small, by = "id") %>% select(-id)

7. semi_join

Describe the resulting data:

• Columns:
• Rows:

How is it different from the original two datasets?

olympics_small <- olympics_small %>% mutate(id = row_number())
youtube_small <- youtube_small %>% mutate(id = row_number())

semi_joined_data <- semi_join(olympics_small, youtube_small, by = "id") %>% select(-id)

8. anti_join

Describe the resulting data:

• Columns:
• Rows:

How is it different from the original two datasets?

olympics_small <- olympics_small %>% mutate(id = row_number())
youtube_small <- youtube_small %>% mutate(id = row_number())

anti_joined_data <- anti_join(olympics_small, youtube_small, by = "id") %>% select(-id)