A data set with scores of critical and public response.
Format
A data frame with 31 rows and 17 columns:
- film
name of film
- rotten_tomatoes_score
score from the American review-aggregation website Rotten Tomatoes; scored out of 100
- rotten_tomatoes_counts
number of critics contributing to Rotten Tomatoes score
- metacritic_score
score from Metacritic where scores are weighted average of reviews; scored out of 100
- metacritic_counts
number of critics contributing to Metacritic score
- cinema_score
score from market research firm CinemaScore; scored by grades A, B, C, D, and F
- imdb_score
score from IMDb where scores are weighted average of reviews; scored out of 100
- imdb_counts
number of critics contributing to IMDb score
- rotten_tomatoes_score_top_critics
score for only top critics from Rotten Tomatoes; scored out of 100
- rotten_tomatoes_score_top_critics_votes
number of top critics contributing to Rotten Tomatoes score
- rotten_tomatoes_popcorn_meter_score
percentage of users who rated this 3.5 stars or higher; scored out of 100
- rotten_tomatoes_popcorn_meter_votes
number of users contributing to the Popcorn Meter score on Rotten Tomatoes
- rotten_tomatoes_popcorn_meter_verified
percentage of users who made a verified movie ticket purchase rating this 3.5 stars or higher
- rotten_tomatoes_popcorn_meter_verified_votes
number of users who made a verified movie ticket purchase rating this 3.5 stars or higher contributing to the Popcorn Meter score on Rotten Tomatoes
- letterboxd_rating
score from Letterboxd where scores are weighted average of reviews; scored out of 5
- letterboxd_counts
number of users contributing to Letterboxd score
- letterboxd_num_fans
number of users this film as one of their four favorites on Letterboxd
Examples
public_response
#> # A tibble: 31 × 17
#> film rotten_tomatoes_score rotten_tomatoes_counts metacritic_score
#> <chr> <dbl> <dbl> <dbl>
#> 1 Toy Story 100 163 96
#> 2 A Bug's Life 92 90 78
#> 3 Toy Story 2 100 176 88
#> 4 Monsters, Inc. 96 191 79
#> 5 Finding Nemo 99 266 90
#> 6 The Incredibles 97 248 90
#> 7 Cars 74 198 73
#> 8 Ratatouille 96 251 96
#> 9 WALL-E 95 258 95
#> 10 Up 98 291 88
#> # ℹ 21 more rows
#> # ℹ 13 more variables: metacritic_counts <dbl>, cinema_score <chr>,
#> # imdb_score <dbl>, imdb_counts <dbl>,
#> # rotten_tomatoes_score_top_critics <dbl>,
#> # rotten_tomatoes_score_top_critics_votes <dbl>,
#> # rotten_tomatoes_popcorn_meter_score <dbl>,
#> # rotten_tomatoes_popcorn_meter_votes <dbl>, …