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) 

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)

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)

4. mutate(): (4 points)

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

q4 <- movies %>%
  mutate(Profitability_millions = Profitability / 1e6)

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 %`))
q5_2 = q4 %>% 
  arrange(desc(Profitability_millions))

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) %>%  ###  Filter first
  select(movie_title, release_year, Genre, Profitability, `Rotten Tomatoes %`) %>%  ###  Select after filtering
  mutate(Profitability_millions = Profitability / 1e6) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))  ###  Sort after selecting

7. Interpret question 6 (1 point)

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

####From the resulting data, I can tell that the best movies are not necessarily the most popular. The highest rated movies (via rotten tomatoes) dont have the highest profitability. The highest profits are around 11 or 8 mil but have sub-90 rotten tomato scores. The highest rotten tomatoes scores have between 2-8 million in profits.

print(q6)

## # A tibble: 12 × 6
##    movie_title            release_year Genre   Profitability `Rotten Tomatoes %`
##    <chr>                         <dbl> <chr>           <dbl>               <dbl>
##  1 WALL-E                         2008 Animat…         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
##  7 Tangled                        2010 Animat…         1.37                   89
##  8 (500) Days of Summer           2009 comedy          8.10                   87
##  9 Rachel Getting Married         2008 Drama           1.38                   85
## 10 Jane Eyre                      2011 Romance         0                      85
## 11 Beginners                      2011 Comedy          4.47                   84
## 12 My Week with Marilyn           2011 Drama           0.826                  83
## # ℹ 1 more variable: Profitability_millions <dbl>

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().

summary_df <- movies %>%
  group_by(Genre) %>%
  summarize(
    average_audience_score = mean(`Audience score %`, na.rm = TRUE),  # Assuming column 4 is 'Audience_score'
    average_profitability = mean(Profitability, na.rm = TRUE)  # Assuming column 5 is 'Profitability_millions'
  )
# Display the summary dataframe
print(summary_df)

## # A tibble: 10 × 3
##    Genre     average_audience_score average_profitability
##    <chr>                      <dbl>                 <dbl>
##  1 Action                      45                   1.25 
##  2 Animation                   70.2                 3.76 
##  3 Comdy                       61                   2.65 
##  4 Comedy                      61.0                 3.78 
##  5 Drama                       67.2                 8.41 
##  6 Fantasy                     81                   1.78 
##  7 Romance                     62.8                 3.98 
##  8 Romence                     84                   8.74 
##  9 comedy                      81                   8.10 
## 10 romance                     84                   0.653