Assignment 3

Load the movies dataset

1. rename(): (4 points)

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

Question1  <- movies %>%  
  rename(movie_title = Film , release_year = Year)


head(Question1)

## # 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,

Question2 <- Question1 %>% 
  select(movie_title , release_year, Genre , Profitability)
print(head(Question2))

## # 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.

Question3 <- Question1 %>%  
  filter(release_year > 2000 & `Rotten Tomatoes %` > 80)
head(Question3)

## # A tibble: 6 × 8
##   movie_title            Genre    `Lead Studio` `Audience score %` Profitability
##   <chr>                  <chr>    <chr>                      <dbl>         <dbl>
## 1 WALL-E                 Animati… Disney                        89         2.90 
## 2 Waitress               Romance  Independent                   67        11.1  
## 3 Tangled                Animati… Disney                        88         1.37 
## 4 Rachel Getting Married Drama    Independent                   61         1.38 
## 5 My Week with Marilyn   Drama    The Weinstei…                 84         0.826
## 6 Midnight in Paris      Romence  Sony                          84         8.74 
## # ℹ 3 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   release_year <dbl>

4. mutate(): (4 points)

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

Question4 <- Question3 %>% 
  mutate(Profitability_millions = Profitability)
head(Question4)

## # A tibble: 6 × 9
##   movie_title            Genre    `Lead Studio` `Audience score %` Profitability
##   <chr>                  <chr>    <chr>                      <dbl>         <dbl>
## 1 WALL-E                 Animati… Disney                        89         2.90 
## 2 Waitress               Romance  Independent                   67        11.1  
## 3 Tangled                Animati… Disney                        88         1.37 
## 4 Rachel Getting Married Drama    Independent                   61         1.38 
## 5 My Week with Marilyn   Drama    The Weinstei…                 84         0.826
## 6 Midnight in Paris      Romence  Sony                          84         8.74 
## # ℹ 4 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   release_year <dbl>, 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))

Question5 <- Question4 %>%  
  arrange(desc(`Rotten Tomatoes %`), Profitability)
head(Question5)

## # A tibble: 6 × 9
##   movie_title       Genre     `Lead Studio` `Audience score %` Profitability
##   <chr>             <chr>     <chr>                      <dbl>         <dbl>
## 1 WALL-E            Animation Disney                        89          2.90
## 2 Enchanted         Comedy    Disney                        80          4.01
## 3 Midnight in Paris Romence   Sony                          84          8.74
## 4 Knocked Up        Comedy    Universal                     83          6.64
## 5 Tangled           Animation Disney                        88          1.37
## 6 A Serious Man     Drama     Universal                     64          4.38
## # ℹ 4 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   release_year <dbl>, 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.

Question6 <- movies %>%
  rename(movie_title = Film , release_year = Year) %>% 
  filter(release_year > 2000 & `Rotten Tomatoes %` > 80) %>%
  select(movie_title , release_year, Genre, Profitability , `Rotten Tomatoes %`) %>%
  mutate(Profitability_millions = Profitability) %>%
  arrange(desc(`Rotten Tomatoes %`), Profitability)

head(Question6)

7. Interpret question 6 (1 point)

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

No, the best movies aren’t 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().

Extra_credit <- movies %>%
  group_by(Genre) %>%
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
    avg_profitability = mean(Profitability*1000000, na.rm = TRUE)
  )
print(Extra_credit)

## # A tibble: 10 × 3
##    Genre     avg_rating avg_profitability
##    <chr>          <dbl>             <dbl>
##  1 Action          11            1245333.
##  2 Animation       74.2          3759414.
##  3 Comdy           13            2649068.
##  4 Comedy          42.7          3776946.
##  5 Drama           51.5          8407218.
##  6 Fantasy         73            1783944.
##  7 Romance         42.1          3984790.
##  8 Romence         93            8744706.
##  9 comedy          87            8096000 
## 10 romance         54             652603.