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(): Rename columns “Film” 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(): Create a new dataframe with selected columns
q2 <- q1 %>%
select(movie_title, release_year, Genre, Profitability)
head(q2)
## # A tibble: 6 × 4
## movie_title release_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(): Filter movies released after 2000 with Rotten Tomatoes
% higher than 80
q3 <- q1 %>%
filter(release_year > 2000, `Rotten Tomatoes %` > 80)
head(q3)
## # A tibble: 6 × 8
## movie_title Genre `Lead Studio` `Audience score %` Profitability
## <chr> <chr> <chr> <dbl> <dbl>
## 1 WALL-E Animati… Disney 89 2.90
## 2 Waitress Romance Independent 67 11.1
## 3 Tangled Animati… Disney 88 1.37
## 4 Rachel Getting Married Drama Independent 61 1.38
## 5 My Week with Marilyn Drama The Weinstei… 84 0.826
## 6 Midnight in Paris Romence Sony 84 8.74
## # ℹ 3 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## # release_year <dbl>
4. Mutate(): Add a column converting Profitability to millions
q4 <- q1 %>%
mutate(Profitability_millions = Profitability / 1e6)
head(q4)
## # A tibble: 6 × 9
## 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 4 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## # release_year <dbl>, Profitability_millions <dbl>
5. Arrange(): Sort by Rotten Tomatoes % (descending) and then
Profitability (descending)
q5 <- q4 %>%
arrange(desc(`Rotten Tomatoes %`), desc(Profitability_millions))
head(q5)
## # A tibble: 6 × 9
## movie_title Genre `Lead Studio` `Audience score %` Profitability
## <chr> <chr> <chr> <dbl> <dbl>
## 1 WALL-E Animation Disney 89 2.90
## 2 Midnight in Paris Romence Sony 84 8.74
## 3 Enchanted Comedy Disney 80 4.01
## 4 Knocked Up Comedy Universal 83 6.64
## 5 Waitress Romance Independent 67 11.1
## 6 A Serious Man Drama Universal 64 4.38
## # ℹ 4 more variables: `Rotten Tomatoes %` <dbl>, `Worldwide Gross` <chr>,
## # release_year <dbl>, Profitability_millions <dbl>
6. Combining functions: Chain operations together
q6 <- movies %>%
filter(Year > 2000, `Rotten Tomatoes %` > 80) %>%
select(Film, Year, Genre, Profitability, `Rotten Tomatoes %`) %>%
mutate(Profitability_millions = Profitability / 1e6) %>%
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. Interpretation of results
High-rated movies (Rotten Tomatoes % > 80) are not always the
most profitable. Some lower-rated movies have higher profitability due
to audience appeal, marketing, or franchise success.
EXTRA CREDIT: Group movies by Genre and calculate average ratings
and profitability
movies <- movies %>%
mutate(Profitability_millions = Profitability / 1e6)
Genre_Summary <- movies %>%
group_by(Genre) %>%
summarize(
Avg_Rotten_Tomatoes = mean(`Rotten Tomatoes %`, na.rm = TRUE),
Avg_Profitability_millions = mean(Profitability_millions, na.rm = TRUE)
)
head(Genre_Summary)
## # A tibble: 6 × 3
## Genre Avg_Rotten_Tomatoes Avg_Profitability_millions
## <chr> <dbl> <dbl>
## 1 Action 11 0.00000125
## 2 Animation 74.2 0.00000376
## 3 Comdy 13 0.00000265
## 4 Comedy 42.7 0.00000378
## 5 Drama 51.5 0.00000841
## 6 Fantasy 73 0.00000178