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(): (4points)

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

question1<- movies %>%
  rename(movie_title = Film , release_year = Year)
print(head(question1))

## # 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,

question2 <- movies %>% 
  select(Film, Year, Genre, Profitability)
print(head(question2))

## # 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.

question3 <- movies %>%  
  filter(Year > 2000 & `Rotten Tomatoes %` > 80)
print(head(question3))

## # 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.

question4 <- movies %>% 
  mutate(Profitability_millions = Profitability * 1000000)
print(head(question4))

## # 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))

question5 <- movies %>%  
  arrange(desc(`Rotten Tomatoes %`), desc (Profitability))
print(head(question5))

## # 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 Midn… Rome… Sony                          84          8.74                  93
## 3 Ench… Come… Disney                        80          4.01                  93
## 4 Knoc… Come… Universal                     83          6.64                  91
## 5 Wait… Roma… Independent                   67         11.1                   89
## 6 A Se… Drama Universal                     64          4.38                  89
## # 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.

movies %>% 
  filter(Year > 2000 & `Rotten Tomatoes %` > 80) %>%
  select(Film, Year, Genre, Profitability, `Rotten Tomatoes %`) %>%
  mutate(Profitability_millions = Profitability * 1000000) %>%
  arrange(desc(`Rotten Tomatoes %`), desc (Profitability)) %>%
  head(6)

## # 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               2896019.
## 2 Midnight…  2011 Rome…          8.74                  93               8744706.
## 3 Enchanted  2007 Come…          4.01                  93               4005737.
## 4 Knocked …  2007 Come…          6.64                  91               6636402.
## 5 Waitress   2007 Roma…         11.1                   89              11089742.
## 6 A Seriou…  2009 Drama          4.38                  89               4382857.

7. Interpret question 6 (1 point)

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

From the resulting data, the best movies are not the most popular movies. This is because not every movie with a high rotten tomato score had a high profitability. Also, there is not a strong relation between the most popular and best movies because we can see that Wall-E had the highest rotten tomato score but it only made about two million dollars. This means that it did have a high rotten tomato score which makes it the best movie, but it did not have the best profitability compared to other movies. We can also see that Fireproof made sixty-six million dollars, but it was the forty third movie.