Import data
## # A tibble: 603 × 75
## index series_name network season title imdb engagement date_aired
## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dttm>
## 1 1 Scooby Doo, … CBS 1 What… 8.1 556 1969-09-13 00:00:00
## 2 2 Scooby Doo, … CBS 1 A Cl… 8.1 479 1969-09-20 00:00:00
## 3 3 Scooby Doo, … CBS 1 Hass… 8 455 1969-09-27 00:00:00
## 4 4 Scooby Doo, … CBS 1 Mine… 7.8 426 1969-10-04 00:00:00
## 5 5 Scooby Doo, … CBS 1 Deco… 7.5 391 1969-10-11 00:00:00
## 6 6 Scooby Doo, … CBS 1 What… 8.4 384 1969-10-18 00:00:00
## 7 7 Scooby Doo, … CBS 1 Neve… 7.6 358 1969-10-25 00:00:00
## 8 8 Scooby Doo, … CBS 1 Foul… 8.2 358 1969-11-01 00:00:00
## 9 9 Scooby Doo, … CBS 1 The … 8.1 371 1969-11-08 00:00:00
## 10 10 Scooby Doo, … CBS 1 Bedl… 8 346 1969-11-15 00:00:00
## # ℹ 593 more rows
## # ℹ 67 more variables: run_time <dbl>, format <chr>, monster_name <chr>,
## # monster_gender <chr>, monster_type <chr>, monster_subtype <chr>,
## # monster_species <chr>, monster_real <chr>, monster_amount <dbl>,
## # caught_fred <chr>, caught_daphnie <chr>, caught_velma <chr>,
## # caught_shaggy <chr>, caught_scooby <chr>, captured_fred <chr>,
## # captured_daphnie <chr>, captured_velma <chr>, captured_shaggy <chr>, …
Apply the following dplyr verbs to your data
Filter rows
## # A tibble: 25 × 75
## index series_name network season title imdb engagement date_aired
## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dttm>
## 1 1 Scooby Doo, … CBS 1 What… 8.1 556 1969-09-13 00:00:00
## 2 2 Scooby Doo, … CBS 1 A Cl… 8.1 479 1969-09-20 00:00:00
## 3 3 Scooby Doo, … CBS 1 Hass… 8 455 1969-09-27 00:00:00
## 4 4 Scooby Doo, … CBS 1 Mine… 7.8 426 1969-10-04 00:00:00
## 5 5 Scooby Doo, … CBS 1 Deco… 7.5 391 1969-10-11 00:00:00
## 6 6 Scooby Doo, … CBS 1 What… 8.4 384 1969-10-18 00:00:00
## 7 7 Scooby Doo, … CBS 1 Neve… 7.6 358 1969-10-25 00:00:00
## 8 8 Scooby Doo, … CBS 1 Foul… 8.2 358 1969-11-01 00:00:00
## 9 9 Scooby Doo, … CBS 1 The … 8.1 371 1969-11-08 00:00:00
## 10 10 Scooby Doo, … CBS 1 Bedl… 8 346 1969-11-15 00:00:00
## # ℹ 15 more rows
## # ℹ 67 more variables: run_time <dbl>, format <chr>, monster_name <chr>,
## # monster_gender <chr>, monster_type <chr>, monster_subtype <chr>,
## # monster_species <chr>, monster_real <chr>, monster_amount <dbl>,
## # caught_fred <chr>, caught_daphnie <chr>, caught_velma <chr>,
## # caught_shaggy <chr>, caught_scooby <chr>, captured_fred <chr>,
## # captured_daphnie <chr>, captured_velma <chr>, captured_shaggy <chr>, …
Arrange rows
## # A tibble: 603 × 75
## index series_name network season title imdb engagement date_aired
## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dttm>
## 1 553 Supernatural The CW Cross… Scoo… 9.6 6929 2018-03-29 00:00:00
## 2 483 Scooby-Doo M… Cartoo… 2 Come… 9.3 260 2013-04-05 00:00:00
## 3 451 Scooby-Doo M… Cartoo… 1 All … 9.2 272 2011-07-26 00:00:00
## 4 464 Scooby-Doo M… Cartoo… 2 Nigh… 9.1 202 2012-08-10 00:00:00
## 5 482 Scooby-Doo M… Cartoo… 2 Thro… 9 184 2013-04-05 00:00:00
## 6 467 Scooby-Doo M… Cartoo… 2 Wrat… 8.9 207 2012-08-15 00:00:00
## 7 478 Scooby-Doo M… Cartoo… 2 The … 8.9 176 2013-03-29 00:00:00
## 8 479 Scooby-Doo M… Cartoo… 2 Nigh… 8.9 187 2013-04-02 00:00:00
## 9 442 Scooby-Doo M… Cartoo… 1 Esca… 8.8 250 2011-05-24 00:00:00
## 10 450 Scooby-Doo M… Cartoo… 1 Pawn… 8.8 212 2011-07-19 00:00:00
## # ℹ 593 more rows
## # ℹ 67 more variables: run_time <dbl>, format <chr>, monster_name <chr>,
## # monster_gender <chr>, monster_type <chr>, monster_subtype <chr>,
## # monster_species <chr>, monster_real <chr>, monster_amount <dbl>,
## # caught_fred <chr>, caught_daphnie <chr>, caught_velma <chr>,
## # caught_shaggy <chr>, caught_scooby <chr>, captured_fred <chr>,
## # captured_daphnie <chr>, captured_velma <chr>, captured_shaggy <chr>, …
Select columns
## # A tibble: 603 × 2
## monster_type monster_subtype
## <chr> <chr>
## 1 Possessed Object Suit
## 2 Ghost Suit
## 3 Ghost Phantom
## 4 Ancient Miner
## 5 Ancient Witch Doctor
## 6 Ghost Phantom
## 7 Animal Half-Human
## 8 Mechanical Humanoid
## 9 Ghost Pupeteer
## 10 Ghost Clown
## # ℹ 593 more rows
Add columns
Summarize by groups