library(dplyr) library(readr)

Load the movies dataset

movies <- read_csv(“https://gist.githubusercontent.com/tiangechen/b68782efa49a16edaf07dc2cdaa855ea/raw/0c794a9717f18b094eabab2cd6a6b9a226903577/movies.csv”)

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) print(head(q1))

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)

print(head(q2))

3. filter(): (4 points) use results from question 1

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)

print(head(q3))

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 / 1e6) print(head(q4))

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_millions)) print(select(q5))

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.

final_dataframe <- movies %>% rename(movie_title = Film , release_year = Year) %>% select(movie_title, release_year, Genre, Profitability) %>% filter(release_year > 2000 & Rotten Tomatoes % > 80) %>% mutate(Profitability_millions = Profitability / 1e6) %>% arrange(desc(Rotten Tomatoes %), desc(Profitability_millions)) %>%

print(head(final_dataframe))

7. Interpret question 6 (1 point)