Assingment 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')

1. rename(): (4 points)

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

q1  <- movies %>%  
  rename(movie_title = Film, release_year = Year)

head(q1)

##                          movie_title   Genre           Lead.Studio
## 1         Zack and Miri Make a Porno Romance The Weinstein Company
## 2                    Youth in Revolt  Comedy The Weinstein Company
## 3 You Will Meet a Tall Dark Stranger  Comedy           Independent
## 4                       When in Rome  Comedy                Disney
## 5              What Happens in Vegas  Comedy                   Fox
## 6                Water For Elephants   Drama      20th Century Fox
##   Audience.score.. Profitability Rotten.Tomatoes.. Worldwide.Gross release_year
## 1               70      1.747542                64         $41.94          2008
## 2               52      1.090000                68         $19.62          2010
## 3               35      1.211818                43         $26.66          2010
## 4               44      0.000000                15         $43.04          2010
## 5               72      6.267647                28        $219.37          2008
## 6               72      3.081421                60        $117.09          2011

2. select(): (4 points)

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

Select only the movie_title, release_year,genre, and Profitability

q2 <- q1 %>% 
  select(movie_title , release_year , Genre , Profitability)

head(q2)

##                          movie_title release_year   Genre Profitability
## 1         Zack and Miri Make a Porno         2008 Romance      1.747542
## 2                    Youth in Revolt         2010  Comedy      1.090000
## 3 You Will Meet a Tall Dark Stranger         2010  Comedy      1.211818
## 4                       When in Rome         2010  Comedy      0.000000
## 5              What Happens in Vegas         2008  Comedy      6.267647
## 6                Water For Elephants         2011   Drama      3.081421

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)

head(q3)

##              movie_title     Genre           Lead.Studio Audience.score..
## 1                 WALL-E Animation                Disney               89
## 2               Waitress   Romance           Independent               67
## 3                Tangled Animation                Disney               88
## 4 Rachel Getting Married     Drama           Independent               61
## 5   My Week with Marilyn     Drama The Weinstein Company               84
## 6      Midnight in Paris   Romence                  Sony               84
##   Profitability Rotten.Tomatoes.. Worldwide.Gross release_year
## 1      2.896019                96        $521.28          2008
## 2     11.089742                89         $22.18          2007
## 3      1.365692                89        $355.01          2010
## 4      1.384167                85         $16.61          2008
## 5      0.825800                83          $8.26          2011
## 6      8.744706                93        $148.66          2011

4. mutate(): (4 points)

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

q4 <- q1 %>% 
  mutate(Profitability_millions = Profitability)
head(q4)

##                          movie_title   Genre           Lead.Studio
## 1         Zack and Miri Make a Porno Romance The Weinstein Company
## 2                    Youth in Revolt  Comedy The Weinstein Company
## 3 You Will Meet a Tall Dark Stranger  Comedy           Independent
## 4                       When in Rome  Comedy                Disney
## 5              What Happens in Vegas  Comedy                   Fox
## 6                Water For Elephants   Drama      20th Century Fox
##   Audience.score.. Profitability Rotten.Tomatoes.. Worldwide.Gross release_year
## 1               70      1.747542                64         $41.94          2008
## 2               52      1.090000                68         $19.62          2010
## 3               35      1.211818                43         $26.66          2010
## 4               44      0.000000                15         $43.04          2010
## 5               72      6.267647                28        $219.37          2008
## 6               72      3.081421                60        $117.09          2011
##   Profitability_millions
## 1               1.747542
## 2               1.090000
## 3               1.211818
## 4               0.000000
## 5               6.267647
## 6               3.081421

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 <- q1 %>%
  arrange(desc(Rotten.Tomatoes..),desc(Profitability))
head(q5)

##         movie_title     Genre Lead.Studio Audience.score.. Profitability
## 1            WALL-E Animation      Disney               89      2.896019
## 2 Midnight in Paris   Romence        Sony               84      8.744706
## 3         Enchanted    Comedy      Disney               80      4.005737
## 4        Knocked Up    Comedy   Universal               83      6.636402
## 5          Waitress   Romance Independent               67     11.089742
## 6     A Serious Man     Drama   Universal               64      4.382857
##   Rotten.Tomatoes.. Worldwide.Gross release_year
## 1                96        $521.28          2008
## 2                93        $148.66          2011
## 3                93        $340.49          2007
## 4                91           $219          2007
## 5                89         $22.18          2007
## 6                89         $30.68          2009

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))

7. Interpret question 6 (1 point)

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

Yes the best movies are the most popular based on the relationship between high rotten tomatoe scores and the high levels of profitability. # 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().