Term Project
Cameron Mello
This is an extension of the tidytuesday assignment you have already done. Complete the questions below, using the screencast you chose for the tidytuesday assigment.
Import data
library(tidyverse)
theme_set(theme_light())
media_franchises <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-02/media_franchises.csv")Description of the data and definition of variables
media_franchises %>%
count(franchise, sort = TRUE)
## # A tibble: 103 x 2
## franchise n
## <chr> <int>
## 1 Spider-Man 7
## 2 Dragon Ball 6
## 3 Final Fantasy 6
## 4 Neon Genesis Evangelion 6
## 5 Star Wars 6
## 6 Superman 6
## 7 Wizarding World / Harry Potter 6
## 8 A Song of Ice and Fire / Game of Thrones 5
## 9 Aladdin 5
## 10 Detective Conan / Case Closed 5
## # … with 93 more rows
franchises <- media_franchises %>%
group_by(franchise, original_media, year_created, creators, owners) %>%
summarize(categories = n(),
total_revenue = sum(revenue),
most_profitable = revenue_category[which.max(revenue)]) %>%
ungroup()
franchises
## # A tibble: 103 x 8
## franchise original_media year_created creators owners categories
## <chr> <chr> <dbl> <chr> <chr> <int>
## 1 A Song o… Novel 1996 George … Rando… 5
## 2 Aladdin Animated film 1992 Walt Di… The W… 5
## 3 Angry Bi… Video game 2009 Jaakko … Rovio 4
## 4 Anpanman Manga 1973 Takashi… Froeb… 2
## 5 Assassin… Video game 2007 Patrice… Ubiso… 3
## 6 Avengers Comic book 1963 Stan Le… Marve… 4
## 7 Barbie Animated film 1987 Ruth Ha… Mattel 3
## 8 Batman Comic book 1939 Bob Kan… DC En… 4
## 9 Ben 10 Animated seri… 2005 Man of … Carto… 1
## 10 Beyblade Manga 1999 Takao A… Shoga… 2
## # … with 93 more rows, and 2 more variables: total_revenue <dbl>,
## # most_profitable <chr>
media_franchises
## # A tibble: 321 x 7
## franchise revenue_category revenue year_created original_media creators
## <chr> <chr> <dbl> <dbl> <chr> <chr>
## 1 A Song o… Book sales 0.9 1996 Novel George …
## 2 A Song o… Box Office 0.001 1996 Novel George …
## 3 A Song o… Home Video/Ente… 0.28 1996 Novel George …
## 4 A Song o… TV 4 1996 Novel George …
## 5 A Song o… Video Games/Gam… 0.132 1996 Novel George …
## 6 Aladdin Box Office 0.76 1992 Animated film Walt Di…
## 7 Aladdin Home Video/Ente… 1 1992 Animated film Walt Di…
## 8 Aladdin Merchandise, Li… 0.5 1992 Animated film Walt Di…
## 9 Aladdin Music 0.447 1992 Animated film Walt Di…
## 10 Aladdin Video Games/Gam… 2.2 1992 Animated film Walt Di…
## # … with 311 more rows, and 1 more variable: owners <chr>Visualize data
Hint: One graph of your choice.
library(glue)
top_franchises <- franchises %>%
mutate(franchise = glue("{ franchise } ({ year_created })")) %>%
top_n(20, total_revenue)
media_franchises %>%
mutate(franchise = glue("{ franchise } ({ year_created })")) %>%
semi_join(top_franchises, by = "franchise") %>%
mutate(franchise = fct_reorder(franchise, revenue, sum),
revenue_category = fct_reorder(revenue_category, revenue, sum)) %>%
ggplot(aes(franchise, revenue)) +
geom_col(aes(fill = revenue_category)) +
geom_text(aes(y = total_revenue,
label = paste0(scales::dollar(total_revenue, accuracy = 1), "B")),
data = top_franchises,
hjust = 0) +
scale_y_continuous(labels = scales::dollar) +
expand_limits(y = 100) +
coord_flip() +
theme(panel.grid.major.y = element_blank()) +
guides(fill = guide_legend(reverse = TRUE)) +
labs(title = "What are the most profitable franchises of all time?",
fill = "Category",
x = "",
y = "Revenue (Billions)")
What is the story behind the graph?
The graph desplays the biggest franchises of all time. The colors in the graph are the categorys in withch they are broken down to make a profit, with the number being desplayed to the right in billions. Categorys are, merchandise, video games, box office, comic, home/entertainment, book sales, tv, music.