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.

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
q3 <- movies %>% #then
  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 = 'millions of dollars')

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 <chr>

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))

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) %>%
  arrange(desc(`Rotten Tomatoes %`), desc(Profitability)) 
head(q6)
## # A tibble: 6 × 6
##   movie_title       release_year Genre     Profitability `Rotten Tomatoes %`
##   <chr>                    <dbl> <chr>             <dbl>               <dbl>
## 1 WALL-E                    2008 Animation          2.90                  96
## 2 Midnight in Paris         2011 Romence            8.74                  93
## 3 Enchanted                 2007 Comedy             4.01                  93
## 4 Knocked Up                2007 Comedy             6.64                  91
## 5 Waitress                  2007 Romance           11.1                   89
## 6 A Serious Man             2009 Drama              4.38                  89
## # ℹ 1 more variable: Profitability_millions <dbl>

7. Interpret question 6 (1 point)

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().

q7 <- movies %>%
  mutate(Profitability_millions = Profitability) %>%
  group_by(Genre) %>%
  summarize(
    avg_rating = mean(`Rotten Tomatoes %`, na.rm = TRUE),
    avg_profitability_millions = mean(Profitability_millions, na.rm = TRUE))

head(q7)
## # A tibble: 6 × 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