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)

# 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 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 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 / 1000000)

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 %>%
  select(Film, Year, Genre, Profitability, `Rotten Tomatoes %`) %>%
  filter(Year > 2000 & `Rotten Tomatoes %` > 80) %>%
  mutate(Profitability_millions = Profitability / 1000000) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))

head(q6)

## # A tibble: 6 × 6
##   Film       Year Genre Profitability `Rotten Tomatoes %` Profitability_millions
##   <chr>     <dbl> <chr>         <dbl>               <dbl>                  <dbl>
## 1 WALL-E     2008 Anim…          2.90                  96             0.00000290
## 2 Midnight…  2011 Rome…          8.74                  93             0.00000874
## 3 Enchanted  2007 Come…          4.01                  93             0.00000401
## 4 Knocked …  2007 Come…          6.64                  91             0.00000664
## 5 Waitress   2007 Roma…         11.1                   89             0.0000111 
## 6 A Seriou…  2009 Drama          4.38                  89             0.00000438

7. Interpret question 6 (1 point)

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

From the resulting data in question 6, the best movies are not always the most popular. Using profitabilty as a metric to indicate the popularity of a movie and the Rotten Tomatoes score to indicate the best movies, there is variation in how these two factors align. Some films such as Midnight in Paris have a high Rotten Tomatoes score (93%) and high profitability (8.74), while others such as Enchanted have a high Rotten Tomatoes score (93%) but a lower profitabilty (4.01). The movies are not ordered in a way where profitability consistently decreases as Rotten Tomatoes scores decrease, indicating the highest-rated movies do not always achieve the greatest popularity.