Media Franchise Data Visualization

This blog post will analyze franchise revenue, and the data is from TidyTuesday Data Science online learning community. You can get the data from this link.

library(tidyverse)
library(tidytext)
library(scales)

media <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-02/media_franchises.csv") %>%
  mutate(original_media = str_to_title(original_media),
         revenue_category = str_to_title(revenue_category))

media

## # A tibble: 321 x 7
##    franchise    revenue_category  revenue year_created original_media creators  
##    <chr>        <chr>               <dbl>        <dbl> <chr>          <chr>     
##  1 A Song of I~ Book Sales          0.9           1996 Novel          George R.~
##  2 A Song of I~ Box Office          0.001         1996 Novel          George R.~
##  3 A Song of I~ Home Video/Enter~   0.28          1996 Novel          George R.~
##  4 A Song of I~ Tv                  4             1996 Novel          George R.~
##  5 A Song of I~ Video Games/Games   0.132         1996 Novel          George R.~
##  6 Aladdin      Box Office          0.76          1992 Animated Film  Walt Disn~
##  7 Aladdin      Home Video/Enter~   1             1992 Animated Film  Walt Disn~
##  8 Aladdin      Merchandise, Lic~   0.5           1992 Animated Film  Walt Disn~
##  9 Aladdin      Music               0.447         1992 Animated Film  Walt Disn~
## 10 Aladdin      Video Games/Games   2.2           1992 Animated Film  Walt Disn~
## # ... with 311 more rows, and 1 more variable: owners <chr>

media %>%
  mutate(original_media = fct_lump(original_media, n = 9),
         franchise = fct_reorder(franchise, revenue, sum, na.rm = T),
         original_media = fct_reorder(original_media, -revenue, sum)) %>%
  ggplot(aes(revenue, franchise, fill = revenue_category)) +
  geom_col() +
  facet_wrap(~original_media, scales = "free_y") +
  scale_x_continuous(labels = dollar)  +
  labs(x = "revenue (in billions)",
       fill = "revenue category",
       title = "Revenue of All Franchises") +
  theme(legend.position = c(0.7, 0.15))

media %>%
  group_by(franchise) %>%
  mutate(total_revenue = sum(revenue, na.rm = T)) %>%
  ungroup() %>%
  arrange(desc(total_revenue)) %>%
  head(150) %>%
  mutate(franchise = paste0(franchise, " (", year_created, ")"),
         franchise = fct_reorder(franchise, revenue, sum)
         ) %>% 
  ggplot(aes(revenue, franchise)) +
  geom_col(aes(fill = revenue_category)) +
  geom_text(aes(x = total_revenue,
                y = franchise,
                label = paste0(round(total_revenue), "B")), hjust = 0, check_overlap = T,
            color = "grey40") +
  scale_x_continuous(labels = dollar) +
  labs(x = "revenue (billions)",
       fill = "original media",
       title = "Total Revenue of the Top Franchises")

media %>%
  mutate(owners = str_remove(owners, " \\(.+"),
         owners = str_replace(owners, "  ", " ")) %>%
  mutate(owners = fct_lump(owners, n = 8)) %>%
  filter(owners != "Other") %>%
  group_by(revenue_category, owners) %>%
  summarize(total_revenue = sum(revenue)) %>%
  ungroup() %>%
  mutate(owners = fct_reorder(owners, -total_revenue, sum)) %>%
  ggplot(aes(owners, revenue_category, fill = total_revenue)) +
  geom_tile() +
  scale_fill_gradient2(low = "red",
                       high = "green",
                       mid = "pink",
                       midpoint = 20,
                       label = dollar) +
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 90)) +
  labs(y = "revenue category",
       fill = "revenue",
       title = "Revenue of Top 8 Owners from Various Categories") 

media %>%
  group_by(original_media) %>%
  mutate(total_revenue = sum(revenue)) %>%
  filter(total_revenue > 20) %>%
  ungroup() %>%
  group_by(original_media, revenue_category) %>%
  summarize(revenue = sum(revenue)) %>%
  ungroup() %>%
  mutate(revenue_category = reorder_within(revenue_category, revenue, original_media),
         original_media = fct_reorder(original_media, -revenue, sum)) %>%
  ggplot(aes(revenue, revenue_category, fill = revenue_category)) +
  geom_col(show.legend = F) +
  facet_wrap(~original_media, scales = "free_y") +
  scale_y_reordered() +
  scale_x_continuous(labels = dollar) +
  labs(y = "revenue category",
       title = "Where does original media make revenue?")

media %>%
  group_by(year_created, franchise) %>%
  mutate(total_revenue = sum(revenue)) %>%
  distinct(franchise, .keep_all = T) %>%
  ungroup() %>%
  ggplot(aes(year_created, total_revenue, color = franchise)) +
  geom_point() +
  geom_text(aes(label = franchise), vjust = 1, hjust = 1, check_overlap = T) +
  expand_limits(x = 1920) +
  scale_y_continuous(labels = dollar) +
  theme(legend.position = "none",
        panel.grid = element_blank()) +
  labs(x = "year created",
       y = "revenue (in billions)",
       title = "Franchise Total Revenue and Year Created")