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,

q2  <- q1 %>% 
select(movie_title, release_year, Genre, Profitability)

head(q2)

## # 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.

q3  <- q1 %>%  
filter(release_year > 2000 , `Rotten Tomatoes %` > 80)

head(q3)

## # 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.

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

head(select(q4, Profitability_millions, Profitability))

## # A tibble: 6 × 2
##   Profitability_millions Profitability
##                    <dbl>         <dbl>
## 1               2896019.         2.90 
## 2              11089742.        11.1  
## 3               1365692.         1.37 
## 4               1384167.         1.38 
## 5                825800          0.826
## 6               8744706.         8.74

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 %`), desc (Profitability))

head(select(q5,`Rotten Tomatoes %`, Profitability))

## # A tibble: 6 × 2
##   `Rotten Tomatoes %` Profitability
##                 <dbl>         <dbl>
## 1                  96          2.90
## 2                  93          8.74
## 3                  93          4.01
## 4                  91          6.64
## 5                  89         11.1 
## 6                  89          4.38

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)  %>%  
  filter(release_year > 2000 & `Rotten Tomatoes %` > 80) %>%
  select(movie_title, release_year, Genre, Profitability, `Rotten Tomatoes %`) %>%
  mutate(Profitability_millions = Profitability * 1000000) %>%
  arrange(desc(`Rotten Tomatoes %`), desc (Profitability)) 

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 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>

7. Interpret question 6 (1 point)

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

Based on the data from question 6, the best movies, if speaking in terms of profitability, are not necessarily the most popular. If best is determined by profitability, then movies with the highest rotten tomatoes ratings do not always have the highest profitability. For example, WALL-E has the highest rotten tomato rating of 96%, but has lower profitability than Midnight in Paris, which has rotten tomatoes of 93%. This appears to hint that the best movies, those with highest profitability, are not necessarily the most popular, those with the highest rotten tomato rating.

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

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