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 <- movies %>%
  select(Film, Year, Genre, Profitability)

head(q2)

## # A tibble: 6 × 4
##   Film                                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)

head(q3)

## # A tibble: 6 × 8
##   Film  Genre `Lead Studio` `Audience score %` Profitability `Rotten Tomatoes %`
##   <chr> <chr> <chr>                      <dbl>         <dbl>               <dbl>
## 1 WALL… Anim… Disney                        89         2.90                   96
## 2 Wait… Roma… Independent                   67        11.1                    89
## 3 Tang… Anim… Disney                        88         1.37                   89
## 4 Rach… Drama Independent                   61         1.38                   85
## 5 My W… Drama The Weinstei…                 84         0.826                  83
## 6 Midn… Rome… Sony                          84         8.74                   93
## # ℹ 2 more variables: `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(q4)

## # A tibble: 6 × 9
##   Film  Genre `Lead Studio` `Audience score %` Profitability `Rotten Tomatoes %`
##   <chr> <chr> <chr>                      <dbl>         <dbl>               <dbl>
## 1 Zack… Roma… The Weinstei…                 70          1.75                  64
## 2 Yout… Come… The Weinstei…                 52          1.09                  68
## 3 You … Come… Independent                   35          1.21                  43
## 4 When… Come… Disney                        44          0                     15
## 5 What… Come… Fox                           72          6.27                  28
## 6 Wate… Drama 20th Century…                 72          3.08                  60
## # ℹ 3 more variables: `Worldwide Gross` <chr>, Year <dbl>,
## #   Profitability_millions <dbl>

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 / 1e6))

head(q5)

## # A tibble: 6 × 8
##   Film  Genre `Lead Studio` `Audience score %` Profitability `Rotten Tomatoes %`
##   <chr> <chr> <chr>                      <dbl>         <dbl>               <dbl>
## 1 Fire… Drama Independent                   51         66.9                   40
## 2 High… Come… Disney                        76         22.9                   65
## 3 The … Drama Summit                        78         14.2                   27
## 4 Wait… Roma… Independent                   67         11.1                   89
## 5 Twil… Roma… Summit                        82         10.2                   49
## 6 Mamm… Come… Universal                     76          9.23                  53
## # ℹ 2 more variables: `Worldwide Gross` <chr>, Year <dbl>

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.

q6  <- movies %>%  
  filter(Year > 2000, `Rotten Tomatoes %`> 80) %>%
  select(Film, Year, Genre, Profitability) %>%
  mutate(Profitability_millions = Profitability / 1e6) %>%
  arrange(desc('Rotten Tomatoes %'), desc(Profitability / 1e6)) 
  
head(q6)

## # A tibble: 6 × 5
##   Film                  Year Genre   Profitability Profitability_millions
##   <chr>                <dbl> <chr>           <dbl>                  <dbl>
## 1 Waitress              2007 Romance         11.1              0.0000111 
## 2 Midnight in Paris     2011 Romence          8.74             0.00000874
## 3 (500) Days of Summer  2009 comedy           8.10             0.00000810
## 4 Knocked Up            2007 Comedy           6.64             0.00000664
## 5 Beginners             2011 Comedy           4.47             0.00000447
## 6 A Serious Man         2009 Drama            4.38             0.00000438

7. Interpret question 6 (1 point)

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

The best movies are usually the most popular based on the data.

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().

q8  <- movies %>%  
  mutate(Profitability_millions = Profitability / 1e6) %>%
  group_by(Genre) %>%
  summarize(avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE), avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE))

head(q8)

## # A tibble: 6 × 3
##   Genre     avg_rating avg_profitability_millions
##   <chr>          <dbl>                      <dbl>
## 1 Action          11                   0.00000125
## 2 Animation       74.2                 0.00000376
## 3 Comdy           13                   0.00000265
## 4 Comedy          42.7                 0.00000378
## 5 Drama           51.5                 0.00000841
## 6 Fantasy         73                   0.00000178

Assignment 3

sam gondelman

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?

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().