Assignment #3

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, and Rotten Tomatoes %

Q2 <- Q1 %>%
  select(movie_title , release_year , Genre , Profitability , `Rotten Tomatoes %`)
head(Q2)

## # A tibble: 6 × 5
##   movie_title               release_year Genre Profitability `Rotten Tomatoes %`
##   <chr>                            <dbl> <chr>         <dbl>               <dbl>
## 1 Zack and Miri Make a Por…         2008 Roma…          1.75                  64
## 2 Youth in Revolt                   2010 Come…          1.09                  68
## 3 You Will Meet a Tall Dar…         2010 Come…          1.21                  43
## 4 When in Rome                      2010 Come…          0                     15
## 5 What Happens in Vegas             2008 Come…          6.27                  28
## 6 Water For Elephants               2011 Drama          3.08                  60

3. filter(): (4 points)

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

Q3 <- Q2 %>%
  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 %`) , 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 Enchanted                 2007 Comedy             4.01                  93
## 3 Midnight in Paris         2011 Romence            8.74                  93
## 4 Knocked Up                2007 Comedy             6.64                  91
## 5 Tangled                   2010 Animation          1.37                  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 %>%
  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 * 1e6) %>%
  arrange(desc(`Rotten Tomatoes %`) , Profitability_millions) 

head(Q6)

## # 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 Enchanted                 2007 Comedy             4.01                  93
## 3 Midnight in Paris         2011 Romence            8.74                  93
## 4 Knocked Up                2007 Comedy             6.64                  91
## 5 Tangled                   2010 Animation          1.37                  89
## 6 A Serious Man             2009 Drama              4.38                  89
## # ℹ 1 more variable: Profitability_millions <dbl>

7. Interpret question 6 (1 point)

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

According to the resulting data from Question 6, there is a moderate positive correlation between the Rotten Tomatoes % and Profitability, meaning the best movies aren’t necessarily 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().

# Define a correction dictionary
genre_corrections <- c(
  "romence" = "romance",
  "comedy" = "comedy",   # Ensures lowercase consistency
  "drama" = "drama",
  "animation" = "animation"
)

summary_df <- Q6 %>%
  mutate(
    Genre = tolower(Genre), # Normalize case
    Genre = recode(Genre, !!!genre_corrections) # Correct known typos
  ) %>%
  group_by(Genre) %>%
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE), 
    avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE)  
  )

print(summary_df)

## # 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

Derek Williams, James Cornell

2025-02-13

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, and Rotten Tomatoes %

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().