Assignment #3

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)


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)

(q1)

## # A tibble: 77 × 8
##    movie_title              Genre `Lead Studio` `Audience score %` Profitability
##    <chr>                    <chr> <chr>                      <dbl>         <dbl>
##  1 Zack and Miri Make a Po… Roma… The Weinstei…                 70         1.75 
##  2 Youth in Revolt          Come… The Weinstei…                 52         1.09 
##  3 You Will Meet a Tall Da… 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 
##  7 WALL-E                   Anim… Disney                        89         2.90 
##  8 Waitress                 Roma… Independent                   67        11.1  
##  9 Waiting For Forever      Roma… Independent                   53         0.005
## 10 Valentine's Day          Come… Warner Bros.                  54         4.18 
## # ℹ 67 more rows
## # 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)

(q2)

## # A tibble: 77 × 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 
##  7 WALL-E                                     2008 Animation         2.90 
##  8 Waitress                                   2007 Romance          11.1  
##  9 Waiting For Forever                        2011 Romance           0.005
## 10 Valentine's Day                            2010 Comedy            4.18 
## # ℹ 67 more rows

3. filter(): (4 points)

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)

(q3)

## # A tibble: 77 × 8
##    movie_title              Genre `Lead Studio` `Audience score %` Profitability
##    <chr>                    <chr> <chr>                      <dbl>         <dbl>
##  1 Zack and Miri Make a Po… Roma… The Weinstei…                 70         1.75 
##  2 Youth in Revolt          Come… The Weinstei…                 52         1.09 
##  3 You Will Meet a Tall Da… 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 
##  7 WALL-E                   Anim… Disney                        89         2.90 
##  8 Waitress                 Roma… Independent                   67        11.1  
##  9 Waiting For Forever      Roma… Independent                   53         0.005
## 10 Valentine's Day          Come… Warner Bros.                  54         4.18 
## # ℹ 67 more rows
## # ℹ 3 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   release_year <dbl>

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)

(q4)

## # A tibble: 77 × 9
##    movie_title              Genre `Lead Studio` `Audience score %` Profitability
##    <chr>                    <chr> <chr>                      <dbl>         <dbl>
##  1 Zack and Miri Make a Po… Roma… The Weinstei…                 70         1.75 
##  2 Youth in Revolt          Come… The Weinstei…                 52         1.09 
##  3 You Will Meet a Tall Da… 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 
##  7 WALL-E                   Anim… Disney                        89         2.90 
##  8 Waitress                 Roma… Independent                   67        11.1  
##  9 Waiting For Forever      Roma… Independent                   53         0.005
## 10 Valentine's Day          Come… Warner Bros.                  54         4.18 
## # ℹ 67 more rows
## # ℹ 4 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   release_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 <- q4 %>%
  arrange(desc('Rotten Tomatoes %'), desc(Profitability_millions))

(q5)

## # A tibble: 77 × 9
##    movie_title              Genre `Lead Studio` `Audience score %` Profitability
##    <chr>                    <chr> <chr>                      <dbl>         <dbl>
##  1 Fireproof                Drama Independent                   51         66.9 
##  2 High School Musical 3: … Come… Disney                        76         22.9 
##  3 The Twilight Saga: New … Drama Summit                        78         14.2 
##  4 Waitress                 Roma… Independent                   67         11.1 
##  5 Twilight                 Roma… Summit                        82         10.2 
##  6 Mamma Mia!               Come… Universal                     76          9.23
##  7 Mamma Mia!               Come… Universal                     76          9.23
##  8 Midnight in Paris        Rome… Sony                          84          8.74
##  9 (500) Days of Summer     come… Fox                           81          8.10
## 10 The Proposal             Come… Disney                        74          7.87
## # ℹ 67 more rows
## # ℹ 4 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## #   release_year <dbl>, Profitability_millions <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 %>%
  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))

(q6)

## # A tibble: 77 × 6
##    movie_title              release_year Genre Profitability `Rotten Tomatoes %`
##    <chr>                           <dbl> <chr>         <dbl>               <dbl>
##  1 Fireproof                        2008 Drama         66.9                   40
##  2 High School Musical 3: …         2008 Come…         22.9                   65
##  3 The Twilight Saga: New …         2009 Drama         14.2                   27
##  4 Waitress                         2007 Roma…         11.1                   89
##  5 Twilight                         2008 Roma…         10.2                   49
##  6 Mamma Mia!                       2008 Come…          9.23                  53
##  7 Mamma Mia!                       2008 Come…          9.23                  53
##  8 Midnight in Paris                2011 Rome…          8.74                  93
##  9 (500) Days of Summer             2009 come…          8.10                  87
## 10 The Proposal                     2009 Come…          7.87                  43
## # ℹ 67 more rows
## # ℹ 1 more variable: Profitability_millions <dbl>

7. Interpret question 6 (1 point)

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

The most profitable movies are not considered the most popular as seen from the data. When filtering the data to show the most profitable movies, they weren’t always the highest rated rotten tomatoes ratings. In conclusion, the movies with the highest gross aren’t the highest ratings per 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_df <- movies %>%
  group_by(Genre) %>%
  summarize(
    Avg_Rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
    Avg_Profitability_millions = mean(Profitability, na.rm = TRUE)
  )

print(summary_df)

## # A tibble: 10 × 3
##    Genre     Avg_Rating Avg_Profitability_millions
##    <chr>          <dbl>                      <dbl>
##  1 Action          11                        1.25 
##  2 Animation       74.2                      3.76 
##  3 Comdy           13                        2.65 
##  4 Comedy          42.7                      3.78 
##  5 Drama           51.5                      8.41 
##  6 Fantasy         73                        1.78 
##  7 Romance         42.1                      3.98 
##  8 Romence         93                        8.74 
##  9 comedy          87                        8.10 
## 10 romance         54                        0.653