Rename the “Film” column to “movie_title” and “Year” to “release_year”
question_1 <- movies %>%
rename(movie_title = Film , release_year = Year)
head(question_1)## # 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>Create a new dataframe with only the columns: movie_title, release_year, Genre, Profitability, and Rotten Tomatoes %
question_2 <- question_1 %>%
select(movie_title , release_year , Genre , Profitability)
head(question_2)## # 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 3.08Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 8
question_3 <- question_1 %>%
select(movie_title , release_year , Genre , Profitability , `Rotten Tomatoes %`) %>%
filter(release_year > 2000 , `Rotten Tomatoes %` > 80)
head(question_3)## # 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 93Add a new column called “Profitability_millions” that converts the Profitability to millions of dollars.
question_4 <- question_3 %>%
mutate(Profitability_millions = Profitability * 1000000)
head(question_4)## # 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>Sort the filtered dataset by Rotten Tomatoes % in descending order, and then by Profitability in descending order.
question_5 <- question_4 %>%
arrange(desc(`Rotten Tomatoes %`) , desc(Profitability))
head(question_5)## # 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>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.
Everything <- movies %>%
rename(release_year = Year , movie_title = Film) %>%
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))
head(Everything)## # 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>From the resulting data, are the best movies the most popular?
# No, there is no clear pattern between profitability and rotten tomato score.Create a summary dataframe that shows the average rating and Profitability_millions for movies by Genre.
Extra_Credit <- movies %>%
mutate(Profitability_millions = Profitability * 1000000) %>%
mutate(Genre = recode(Genre,
"Comdy" = "Comedy",
"comedy" = "Comedy",
"Romence" = "Romance",
"romance" = "Romance")) %>%
group_by(Genre) %>%
summarise(
Avg_Profitability = mean(Profitability_millions),
Avg_Rating = mean(`Rotten Tomatoes %`)
)
head(Extra_Credit)## # A tibble: 6 × 3
## Genre Avg_Profitability Avg_Rating
## <chr> <dbl> <dbl>
## 1 Action 1245333. 11
## 2 Animation 3759414. 74.2
## 3 Comedy 3851160. 43.0
## 4 Drama 8407218. 51.5
## 5 Fantasy 1783944. 73
## 6 Romance 4079972. 46.3