Question 1: Rename columns

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

head(one)
## # 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 specific columns

two <- one %>%
  select(movie_title, release_year, Genre, Profitability, `Rotten Tomatoes %`)

head(two)
## # 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: Filter for movies after 2000 and RT > 80%

three <- two %>%
  filter(release_year > 2000, `Rotten Tomatoes %` > 80)

head(three)
## # 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 Profitability to millions

four <- three %>%
  mutate(Profitability_millions = Profitability / 1e6)

head(four)
## # 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 by rating and profitability

five <- four %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))

head(five)
## # 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: Combine all steps with pipes

final <- 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 %`), desc(Profitability_millions))

head(final)
## # 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: Interpretation

After reviewing the top rows of the dataset, it is clear that the highest-rated movies are not always the most profitable. For example, “WALL-E,” which has a Rotten Tomatoes score of 96%, has a profitability of only 2.90 million dollars. On the other hand, “Midnight in Paris,” with a slightly lower Rotten Tomatoes score of 93%, has a much higher profitability of 8.74 million dollars. This suggests that while Rotten Tomatoes ratings are an important indicator of movie quality, profitability can be influenced by other factors such as production cost, distribution, and audience size. Therefore, we conclude that the best-rated movies are not necessarily the most profitable.

final <- movies %>% rename( movie_title = Film, release_year = Year, rotten_tomatoes = Rotten Tomatoes% ) %>% 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), desc(Profitability_millions))