Assignment 3

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>

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

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

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

q4 <- q3 %>%
  mutate(Profitability_millions = Profitability*1000000) 
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>

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 %>%
  mutate(Profitability_millions = Profitability*1000000) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))
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>

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

final_dataset <- movies %>%
  rename(movie_title = Film, 
         release_year = Year) %>%
  select(movie_title, release_year, Genre, Profitability , `Rotten Tomatoes %`) %>%
  filter(release_year > 2000, `Rotten Tomatoes %` > 80) %>%
  mutate(Profitability_millions = Profitability*1000000) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))
head(final_dataset)

## # 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>

Q.7

I don’t think the best movies are the most profitable. For example, “WALL-E” has the highest rating of 96 but has a lower profit than the movie “Waitress” with a rating of 89.

Extra Credit

First, clean the dataset - specifically the Genre column

# Function to standardize Genre names
clean_genre <- function(genre) {
  genre <- str_trim(genre) %>%      # Remove extra spaces
    str_to_title() %>%              # Convert to Title Case
    str_replace_all("/", ", ") %>%  # Replace '/' with ', ' for clarity
    str_replace_all("&", "and")     # Replace '&' with 'and'
  
  # Standardize inconsistent genre names
  genre <- case_when(
    str_detect(genre, "Comdy") ~ "Comedy",
    str_detect(genre, "Romence") ~ "Romance",
    str_detect(genre, "comedy") ~ "Comedy",
    str_detect(genre, "romance") ~ "Romance",
    TRUE ~ genre  # Keep original if no match
  )
  
  return(genre)
  
  # Apply cleaning to the Genre column
cleaned_movies <- final_dataset %>%
  mutate(Genre = clean_genre(Genre))  # Clean Genre names
}

Second, create the dataset and arrange it by average rating in descending order

extra_credit <- final_dataset %>%
  mutate(Genre = clean_genre(Genre),   # Clean Genre names
         Profitability_millions = Profitability * 1e6) %>%
  group_by(Genre) %>%  # Group by Genre
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),  # Average rating
    avg_profitability = mean(Profitability_millions, na.rm = TRUE)  # Average profitability
  ) %>%
arrange(desc(avg_rating))  # Optionally, sort by average rating
head(extra_credit)

## # A tibble: 4 × 3
##   Genre     avg_rating avg_profitability
##   <chr>          <dbl>             <dbl>
## 1 Animation       92.5          2130856.
## 2 Romance         89            6611482.
## 3 Comedy          88.8          5802503.
## 4 Drama           85.7          2197608.

Assignment 3

Rhynner Franz Rodriguez

2025-02-09

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

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

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

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

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))

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

Q.7

I don’t think the best movies are the most profitable. For example, “WALL-E” has the highest rating of 96 but has a lower profit than the movie “Waitress” with a rating of 89.

Extra Credit

First, clean the dataset - specifically the Genre column

Second, create the dataset and arrange it by average rating in descending order

P.S. I got help from ChatGPT and the code for loading the libraries and the “movies” dataset is hidden.

Libraries used were dplyr, readr, and stringr