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_Percent' > 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 / 1e6)
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 <- q4 %>%
arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))
head (q5)
## # A tibble: 6 × 9
## 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 3 more variables: `Worldwide Gross` <chr>, Year <dbl>,
## # Profitability_millions <dbl>
6. Combining functions: (3 points)
7. Interpret question 6 (1 point)
From the resulting data, are the best movies the most popular?
No, the movies with the highest Rotten Tomato scores are not the
most profitable. This means that if “best” equates to most profitable,
this statement is not true.
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().
library(dplyr)
library(stringr)
movies$Genre <- str_to_title(trimws(movies$Genre))
movies$Genre <- recode(movies$Genre,
"Comdy" = "Comedy",
"Romence" = "Romance",
"comedy" = "Comedy",
"romance" = "Romance")
q8 <- movies %>%
group_by(Genre) %>%
summarize(
`Rotten Tomatoes %` = mean(`Rotten Tomatoes %`, na.rm = TRUE),
avg_profitability = mean(Profitability, na.rm = TRUE)
)
print(q8)
## # A tibble: 6 × 3
## Genre `Rotten Tomatoes %` avg_profitability
## <chr> <dbl> <dbl>
## 1 Action 11 1.25
## 2 Animation 74.2 3.76
## 3 Comedy 43.0 3.85
## 4 Drama 51.5 8.41
## 5 Fantasy 73 1.78
## 6 Romance 46.3 4.08