library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(readr)

# Load the movies dataset
movies <- read_csv("https://gist.githubusercontent.com/tiangechen/b68782efa49a16edaf07dc2cdaa855ea/raw/0c794a9717f18b094eabab2cd6a6b9a226903577/movies.csv")

## Rows: 77 Columns: 8

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Film, Genre, Lead Studio, Worldwide Gross
## dbl (4): Audience score %, Profitability, Rotten Tomatoes %, Year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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)

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>

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)

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            3.08

3. filter(): (4 points)

Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 80.

q3 <- movies %>%  
  filter(Year > 2000 & `Rotten Tomatoes %` > 80)

print(q3)

## # A tibble: 12 × 8
##    Film                   Genre   `Lead Studio` `Audience score %` Profitability
##    <chr>                  <chr>   <chr>                      <dbl>         <dbl>
##  1 WALL-E                 Animat… Disney                        89         2.90 
##  2 Waitress               Romance Independent                   67        11.1  
##  3 Tangled                Animat… Disney                        88         1.37 
##  4 Rachel Getting Married Drama   Independent                   61         1.38 
##  5 My Week with Marilyn   Drama   The Weinstei…                 84         0.826
##  6 Midnight in Paris      Romence Sony                          84         8.74 
##  7 Knocked Up             Comedy  Universal                     83         6.64 
##  8 Jane Eyre              Romance Universal                     77         0    
##  9 Enchanted              Comedy  Disney                        80         4.01 
## 10 Beginners              Comedy  Independent                   80         4.47 
## 11 A Serious Man          Drama   Universal                     64         4.38 
## 12 (500) Days of Summer   comedy  Fox                           81         8.10 
## # ℹ 3 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   Year <dbl>

4. mutate(): (4 points)

Add a new column called “Profitability_millions” that converts the Profitability to millions of dollars.

q4 <- movies %>% 
  mutate(Profitability_millions = Profitability * 1e6 )
head(select(q4, Profitability, Profitability_millions))

## # A tibble: 6 × 2
##   Profitability Profitability_millions
##           <dbl>                  <dbl>
## 1          1.75               1747542.
## 2          1.09               1090000 
## 3          1.21               1211818.
## 4          0                        0 
## 5          6.27               6267647.
## 6          3.08               3081421.

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 <- movies %>%
 arrange(desc(`Rotten Tomatoes %`), desc(Profitability))
head(select(q5, `Rotten Tomatoes %`, Profitability))

## # A tibble: 6 × 2
##   `Rotten Tomatoes %` Profitability
##                 <dbl>         <dbl>
## 1                  96          2.90
## 2                  93          8.74
## 3                  93          4.01
## 4                  91          6.64
## 5                  89         11.1 
## 6                  89          4.38

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.

library(dplyr)
library(readr)

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 * 1e6) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))

print(q6)

## # A tibble: 12 × 6
##    movie_title            release_year Genre   Profitability `Rotten Tomatoes %`
##    <chr>                         <dbl> <chr>           <dbl>               <dbl>
##  1 WALL-E                         2008 Animat…         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
##  7 Tangled                        2010 Animat…         1.37                   89
##  8 (500) Days of Summer           2009 comedy          8.10                   87
##  9 Rachel Getting Married         2008 Drama           1.38                   85
## 10 Jane Eyre                      2011 Romance         0                      85
## 11 Beginners                      2011 Comedy          4.47                   84
## 12 My Week with Marilyn           2011 Drama           0.826                  83
## # ℹ 1 more variable: Profitability_millions <dbl>

7. Interpret question 6 (1 point)

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

I would say that there isnt a correlation between the most successful movies, some may be popular but not make any money, but some movies may be very profitable but not popular in Rotten Tomatoes %

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().

summary_fixed <- q6 %>%
  mutate(Genre = tolower(Genre)) %>%  
  mutate(Genre = recode(Genre, "romence" = "romance", "comedy" = "comedy")) %>%  
  group_by(Genre) %>%
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
    avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE)
  )

print(summary_fixed)

## # A tibble: 4 × 3
##   Genre     avg_rating avg_profitability_millions
##   <chr>          <dbl>                      <dbl>
## 1 animation       92.5                   2130856.
## 2 comedy          88.8                   5802503.
## 3 drama           85.7                   2197608.
## 4 romance         89                     6611482.

Assignment 3

Ryan Maiorano

2025-02-10

1. rename(): (4 points)

Rename the “Film” column to “movie_title” and “Year” to “release_year”.

2. select(): (4 points)

Create a new dataframe with only the columns: movie_title, release_year, Genre, Profitability,

3. filter(): (4 points)

Filter the dataset to include only movies released after 2000 with a Rotten Tomatoes % higher than 80.

4. mutate(): (4 points)

Add a new column called “Profitability_millions” that converts the Profitability to millions of dollars.

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))

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.

7. Interpret question 6 (1 point)

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

I would say that there isnt a correlation between the most successful movies, some may be popular but not make any money, but some movies may be very profitable but not popular in Rotten Tomatoes %

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().