Homework_3_Movies_Data_Analysis

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)

knitr::kable(head(q1, 6))

movie_title	Genre	Lead Studio	Audience score %	Profitability	Rotten Tomatoes %	Worldwide Gross	release_year
Zack and Miri Make a Porno	Romance	The Weinstein Company	70	1.747542	64	$41.94	2008
Youth in Revolt	Comedy	The Weinstein Company	52	1.090000	68	$19.62	2010
You Will Meet a Tall Dark Stranger	Comedy	Independent	35	1.211818	43	$26.66	2010
When in Rome	Comedy	Disney	44	0.000000	15	$43.04	2010
What Happens in Vegas	Comedy	Fox	72	6.267647	28	$219.37	2008
Water For Elephants	Drama	20th Century Fox	72	3.081421	60	$117.09	2011

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)

knitr::kable(head(q2, 6))

movie_title	release_year	Genre	Profitability
Zack and Miri Make a Porno	2008	Romance	1.747542
Youth in Revolt	2010	Comedy	1.090000
You Will Meet a Tall Dark Stranger	2010	Comedy	1.211818
When in Rome	2010	Comedy	0.000000
What Happens in Vegas	2008	Comedy	6.267647
Water For Elephants	2011	Drama	3.081421

3. filter(): (4 points), Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 80.

q3 <- q2 %>%
  left_join(
    q1 %>% select(movie_title, release_year, `Rotten Tomatoes %`),
    by = c("movie_title", "release_year")
  ) %>%
  filter(release_year > 2000,
         `Rotten Tomatoes %` > 80)

knitr::kable(head(q3, 6))

movie_title	release_year	Genre	Profitability	Rotten Tomatoes %
WALL-E	2008	Animation	2.896019	96
Waitress	2007	Romance	11.089742	89
Tangled	2010	Animation	1.365692	89
Rachel Getting Married	2008	Drama	1.384167	85
My Week with Marilyn	2011	Drama	0.825800	83
Midnight in Paris	2011	Romence	8.744706	93

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)

knitr::kable(head(q4, 6))

movie_title	release_year	Genre	Profitability	Rotten Tomatoes %	Profitability_millions
WALL-E	2008	Animation	2.896019	96	2.90e-06
Waitress	2007	Romance	11.089742	89	1.11e-05
Tangled	2010	Animation	1.365692	89	1.40e-06
Rachel Getting Married	2008	Drama	1.384167	85	1.40e-06
My Week with Marilyn	2011	Drama	0.825800	83	8.00e-07
Midnight in Paris	2011	Romence	8.744706	93	8.70e-06

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))

knitr::kable(head(q5 %>% select(movie_title, `Rotten Tomatoes %`, Profitability_millions),6))

movie_title	Rotten Tomatoes %	Profitability_millions
WALL-E	96	2.90e-06
Midnight in Paris	93	8.70e-06
Enchanted	93	4.00e-06
Knocked Up	91	6.60e-06
Waitress	89	1.11e-05
A Serious Man	89	4.40e-06

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_movies <- 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))

knitr::kable(head(final_movies, 6))

movie_title	release_year	Genre	Profitability	Rotten Tomatoes %	Profitability_millions
WALL-E	2008	Animation	2.896019	96	2.90e-06
Midnight in Paris	2011	Romence	8.744706	93	8.70e-06
Enchanted	2007	Comedy	4.005737	93	4.00e-06
Knocked Up	2007	Comedy	6.636402	91	6.60e-06
Waitress	2007	Romance	11.089742	89	1.11e-05
A Serious Man	2009	Drama	4.382857	89	4.40e-06

7. Interpret question 6 (1 point)

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

From the resulting data, the movies with the highest Rotten Tomatoes scores are not always the most popular in terms of profitability. Some highly rated movies do earn strong profits, but others generate much lower profitability despite positive reviews. This suggests that being critically well reviewed does not guarantee widespread popularity or financial success.

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().

genre_summary <- final_movies %>%
  mutate(
    Genre = recode(Genre,
                   "Comdy"   = "Comedy",
                   "comedy"  = "Comedy",
                   "Romnace" = "Romance",
                   "Romence" = "Romance")
  ) %>%
  group_by(Genre) %>%
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
    avg_profit_millions = mean(Profitability_millions, na.rm = TRUE),
    .groups = "drop"
  )

knitr::kable(head(genre_summary, 6))

Genre	avg_rating	avg_profit_millions
Animation	92.50000	2.1e-06
Comedy	88.75000	5.8e-06
Drama	85.66667	2.2e-06
Romance	89.00000	6.6e-06