Rename the “Film” column to “movie_title” and “Year” to “release_year”
Q1 <- movies %>%
rename(movie_title = Film, release_year = Year)
print(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>Create a new dataframe with only the columns: movie_title, release_year, Genre, Profitability
Q2 <- Q1 %>%
select(movie_title, release_year, Genre, Profitability)
print(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 3.08Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 80
Q3 <- Q1 %>%
select(movie_title, release_year, Genre, Profitability,`Rotten Tomatoes %`) %>%
filter(release_year > 2000 & `Rotten Tomatoes %` > 80)
print(head(Q3))## # 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
Q4 <- Q3 %>%
mutate(Profitability_millions = Profitability * 1000000)
print(head(Q4))## # 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. five <- four %>% arrange(desc(Rotten Tomatoes %) , desc(Profitability_millions))
Q5 <- Q4 %>%
arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))
print(head(Q5))## # 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
Q6 <- 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 * 1000000) %>%
arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions)) %>%
head(6)
print(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>No, the best movie from the data set was Waitress with profitability of $11,089,742 but only sits at the 5th most popular with a rating of 89. The most popular movie is Wall-E with a rating of 96 but, profitability of only $2,896,019.
Create a summary data frame 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 %>%
mutate(Profitability_millions = Profitability * 1000000) %>%
group_by(Genre) %>%
summarize(
Avg_Rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
Avg_Profitability = mean(Profitability_millions, na.rm = TRUE)
) %>%
arrange(desc(Avg_Rating), desc(Avg_Profitability))
print(summary_df)## # A tibble: 10 × 3
## Genre Avg_Rating Avg_Profitability
## <chr> <dbl> <dbl>
## 1 Romence 93 8744706.
## 2 comedy 87 8096000
## 3 Animation 74.2 3759414.
## 4 Fantasy 73 1783944.
## 5 romance 54 652603.
## 6 Drama 51.5 8407218.
## 7 Comedy 42.7 3776946.
## 8 Romance 42.1 3984790.
## 9 Comdy 13 2649068.
## 10 Action 11 1245333.