1. rename(): (4 points)

Rename the “Film” column to “movie_title” and “Year” to “release_year”.

Q1  <- movies %>%  
  rename(movie_title = Film , release_year = Year)

(head(Q1))

## # A tibble: 6 × 8
##   movie_title               Genre `Lead Studio` `Audience score %` Profitability
##   <chr>                     <chr> <chr>                      <dbl>         <dbl>
## 1 Zack and Miri Make a Por… Roma… The Weinstei…                 70          1.75
## 2 Youth in Revolt           Come… The Weinstei…                 52          1.09
## 3 You Will Meet a Tall Dar… Come… Independent                   35          1.21
## 4 When in Rome              Come… Disney                        44          0   
## 5 What Happens in Vegas     Come… Fox                           72          6.27
## 6 Water For Elephants       Drama 20th Century…                 72          3.08
## # ℹ 3 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   release_year <dbl>

2. select(): (4 points)

Create a new dataframe with only the columns: movie_title, release_year, Genre, Profitability,

Q2 <- Q1 %>% 
  select(movie_title, release_year, Genre, Profitability)

(head(Q2))

## # A tibble: 6 × 4
##   movie_title                        release_year Genre   Profitability
##   <chr>                                     <dbl> <chr>           <dbl>
## 1 Zack and Miri Make a Porno                 2008 Romance          1.75
## 2 Youth in Revolt                            2010 Comedy           1.09
## 3 You Will Meet a Tall Dark Stranger         2010 Comedy           1.21
## 4 When in Rome                               2010 Comedy           0   
## 5 What Happens in Vegas                      2008 Comedy           6.27
## 6 Water For Elephants                        2011 Drama            3.08

3. filter(): (4 points)

Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 80.

Q3 <- Q1 %>% 
  select(movie_title, release_year, Genre, Profitability , `Rotten Tomatoes %`) %>%  
  filter(release_year > 2000 & `Rotten Tomatoes %` > 80)

head(Q3)

## # A tibble: 6 × 5
##   movie_title            release_year Genre    Profitability `Rotten Tomatoes %`
##   <chr>                         <dbl> <chr>            <dbl>               <dbl>
## 1 WALL-E                         2008 Animati…         2.90                   96
## 2 Waitress                       2007 Romance         11.1                    89
## 3 Tangled                        2010 Animati…         1.37                   89
## 4 Rachel Getting Married         2008 Drama            1.38                   85
## 5 My Week with Marilyn           2011 Drama            0.826                  83
## 6 Midnight in Paris              2011 Romence          8.74                   93

4. mutate(): (4 points)

Add a new column called “Profitability_millions” that converts the Profitability to millions of dollars.

Q4 <- Q3 %>% 
  mutate(Profitability_millions = Profitability * 1e6)

head(Q4)

## # A tibble: 6 × 6
##   movie_title            release_year Genre    Profitability `Rotten Tomatoes %`
##   <chr>                         <dbl> <chr>            <dbl>               <dbl>
## 1 WALL-E                         2008 Animati…         2.90                   96
## 2 Waitress                       2007 Romance         11.1                    89
## 3 Tangled                        2010 Animati…         1.37                   89
## 4 Rachel Getting Married         2008 Drama            1.38                   85
## 5 My Week with Marilyn           2011 Drama            0.826                  83
## 6 Midnight in Paris              2011 Romence          8.74                   93
## # ℹ 1 more variable: Profitability_millions <dbl>

5. arrange(): (3 points)

Sort the filtered dataset by Rotten Tomatoes % in descending order, and then by Profitability in descending order. five <- four %>% arrange(desc(Rotten Tomatoes %) , desc(Profitability_millions))

Q5 <- Q4 %>%  
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability*1e6))

head(Q5)

## # A tibble: 6 × 6
##   movie_title       release_year Genre     Profitability `Rotten Tomatoes %`
##   <chr>                    <dbl> <chr>             <dbl>               <dbl>
## 1 WALL-E                    2008 Animation          2.90                  96
## 2 Midnight in Paris         2011 Romence            8.74                  93
## 3 Enchanted                 2007 Comedy             4.01                  93
## 4 Knocked Up                2007 Comedy             6.64                  91
## 5 Waitress                  2007 Romance           11.1                   89
## 6 A Serious Man             2009 Drama              4.38                  89
## # ℹ 1 more variable: Profitability_millions <dbl>

6. Combining functions: (3 points)

Use the pipe operator (%>%) to chain these operations together, starting with the original dataset and ending with a final dataframe that incorporates all the above transformations.

Q6 <- movies %>%
  filter(Year > 2000 & `Rotten Tomatoes %` > 80) %>%
  select(Film, Year, Genre, Profitability,`Rotten Tomatoes %`) %>%
  mutate(Profitability_millions = Profitability*1e6) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability*1e6)) 

  head(Q6)

## # A tibble: 6 × 6
##   Film       Year Genre Profitability `Rotten Tomatoes %` Profitability_millions
##   <chr>     <dbl> <chr>         <dbl>               <dbl>                  <dbl>
## 1 WALL-E     2008 Anim…          2.90                  96               2896019.
## 2 Midnight…  2011 Rome…          8.74                  93               8744706.
## 3 Enchanted  2007 Come…          4.01                  93               4005737.
## 4 Knocked …  2007 Come…          6.64                  91               6636402.
## 5 Waitress   2007 Roma…         11.1                   89              11089742.
## 6 A Seriou…  2009 Drama          4.38                  89               4382857.

7. Interpret question 6 (1 point)

From the resulting data, are the best movies the most popular?

From the resulting data in question 6, the best movies are not always the most popular. Using profitability as a metric to indicate the popularity of a movie and the Rotten Tomatoes score to indicate the best movies, there is variation in how these two factors align. Some films such as Midnight in Paris have a high Rotten Tomatoes score (93%) and high profitability (8.74), while others such as Enchanted have a high Rotten Tomatoes score (93%) but a lower profitability (4.01). The movies are not ordered in a way where profitability consistently decreases as Rotten Tomatoes scores decrease, indicating the highest-rated movies do not always achieve the greatest popularity.

EXTRA CREDIT (4 points)

Create a summary dataframe that shows the average rating and Profitability_millions for movies by Genre. Hint: You’ll need to use group_by() and summarize().

extra_credit <- Q6 %>%
  mutate(
    Genre = tolower(Genre),  # Convert to lowercase
    Genre = ifelse(Genre == "romence", "romance", Genre)  # Fix "romence" typo
  ) %>%
    group_by(Genre) %>%
    summarize(avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
      avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE))

extra_credit

## # A tibble: 4 × 3
##   Genre     avg_rating avg_profitability_millions
##   <chr>          <dbl>                      <dbl>
## 1 animation       92.5                   2130856.
## 2 comedy          88.8                   5802503.
## 3 drama           85.7                   2197608.
## 4 romance         89                     6611482.

Assignment 3

Olivia Beaupre and Emma Ball

2025-02-10

1. rename(): (4 points)

Rename the “Film” column to “movie_title” and “Year” to “release_year”.

2. select(): (4 points)

Create a new dataframe with only the columns: movie_title, release_year, Genre, Profitability,

3. filter(): (4 points)

Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 80.

4. mutate(): (4 points)

Add a new column called “Profitability_millions” that converts the Profitability to millions of dollars.

5. arrange(): (3 points)

Sort the filtered dataset by Rotten Tomatoes % in descending order, and then by Profitability in descending order. five <- four %>% arrange(desc(Rotten Tomatoes %) , desc(Profitability_millions))

6. Combining functions: (3 points)

Use the pipe operator (%>%) to chain these operations together, starting with the original dataset and ending with a final dataframe that incorporates all the above transformations.

7. Interpret question 6 (1 point)

From the resulting data, are the best movies the most popular?

EXTRA CREDIT (4 points)

Create a summary dataframe that shows the average rating and Profitability_millions for movies by Genre. Hint: You’ll need to use group_by() and summarize().