###1. rename(): (4 points) Rename the “Film” column to “movie_title” and “Year” to “release_year”.
question_one <- movies %>%
rename(movie_title = Film , release_year = Year)
head(question_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>###2. select(): (4 points) Create a new dataframe with only the columns: movie_title, release_year, Genre, Profitability,
question_two <- question_one %>%
select(movie_title, release_year, Genre, Profitability, `Rotten Tomatoes %`)
head(question_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###3. filter(): (4 points) Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 80.
question_three <- question_two %>%
filter(release_year > 2000 , `Rotten Tomatoes %` > 80)
head(question_three,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###4. mutate(): (4 points) Add a new column called “Profitability_millions” that converts the Profitability to millions of dollars.
question_four <- question_three %>%
mutate(Profitability_millions = Profitability *1e6)
head(question_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>###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))
question_five <- question_four %>%
arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))
head(question_five, 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>###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.
question_six <- 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(question_six, 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>###7. 7. Interpret question 6 (1 point) From the resulting data, are the best movies the most popular? #No, There is no clear pattern #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 <- question_four %>%
group_by(Genre) %>%
summarize(
avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE),
n = n()
) %>%
arrange(desc(avg_rating), desc(avg_profitability_millions))
head(extra_credit, 6) ## # A tibble: 6 × 4
## Genre avg_rating avg_profitability_millions n
## <chr> <dbl> <dbl> <int>
## 1 Romence 93 8744706. 1
## 2 Animation 92.5 2130856. 2
## 3 Comedy 89.3 5038005. 3
## 4 comedy 87 8096000 1
## 5 Romance 87 5544871. 2
## 6 Drama 85.7 2197608. 3