Assignment 3

Question 1: rename()

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

q1  <- movies %>%
  rename(movie_title = Film, release_year = Year )
head(q1,6)

## # 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>

Question 2: select()

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, 6)

## # 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

Question 3: filter()

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, 6)

## # 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

Question 4: mutate()

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

q4 <- q3 %>% 
  mutate(Profitability_millions = Profitability * 1000000)
head(q4, 6)

## # 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>

Question 5: arrange()

Sort the filtered dataset by Rotten Tomatoes % in descending order, and then by Profitability in descending order.

q5 <- q4 %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))
head(q5, 6)

## # 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>

Question 6: Combining functions

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 <- read_csv("https://gist.githubusercontent.com/tiangechen/b68782efa49a16edaf07dc2cdaa855ea/raw/0c794a9717f18b094eabab2cd6a6b9a226903577/movies.csv") %>%
  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(q6, 6)

## # 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>

Question 7: Interpret Question 6

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

plot <- plot_ly(movies, 
                x = ~`Rotten Tomatoes %`, y = ~Profitability, 
                type = "scatter", 
                mode = "markers",
                marker = list(size = 8, color = 'blue', opacity = 0.6)) %>%
  layout(title = "Are Highly Rated Movies More Profitable?", 
         xaxis = list(title = "Rotten Tomatoes %"),
         yaxis = list(title = "Profitability"),
         hovermode = "closest")
plot

Based on the plot, it appears there is no direct correlation between Rotten Tomatoes % and Profitability, meaning the best movies are not necessarily the most popular

Extra Credit:

Create a summary dataframe that shows the average rating and Profitability_millions for movies by Genre.

extracredit <- q6 %>%
  group_by(Genre) %>%
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
    avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE)
  ) %>%
  arrange(desc(avg_profitability_millions))
head(extracredit)

## # A tibble: 6 × 3
##   Genre     avg_rating avg_profitability_millions
##   <chr>          <dbl>                      <dbl>
## 1 Romence         93                     8744706.
## 2 comedy          87                     8096000 
## 3 Romance         87                     5544871.
## 4 Comedy          89.3                   5038005.
## 5 Drama           85.7                   2197608.
## 6 Animation       92.5                   2130856.