Assignment 3 Mowerman

movies <- read_csv(
  "https://gist.githubusercontent.com/tiangechen/b68782efa49a16edaf07dc2cdaa855ea/raw/0c794a9717f18b094eabab2cd6a6b9a226903577/movies.csv",
  show_col_types = FALSE
)

Question 1

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>

Question 2

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

Question 3

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

Question 4

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>

Question 5

q5 <- q4 %>% 
  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>

Question 6

final_movies <- 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_movies)

## # 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

Based on the filtered results, the highest-rated movies are not necessarily the most profitable

Extra Credit Question

summary_genre <- q4 %>% 
  group_by(Genre) %>% 
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
    avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE)
  )

head(summary_genre)

## # A tibble: 6 × 3
##   Genre     avg_rating avg_profitability_millions
##   <chr>          <dbl>                      <dbl>
## 1 Animation       92.5                 0.00000213
## 2 Comedy          89.3                 0.00000504
## 3 Drama           85.7                 0.00000220
## 4 Romance         87                   0.00000554
## 5 Romence         93                   0.00000874
## 6 comedy          87                   0.00000810