This data comes from Wikipedia and includes franchises that have grossed at least $4 billion usd. How do different media franchises stack up with their revenue streams?
I took a stab at cleaning up the data in R directly from the source - I made some opinionated decisions about how to group categories (there were > 60 distinct categories), if you'd like a deeper dive on data cleaning try working with the data purely from the source.
I have included my script in this repo so you can take a peek at how we got here.
A popular reddit/dataisbeautiful post examined this data with ggplot2.
media_franchises <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-02/media_franchises.csv")
| variable | class | description |
|---|---|---|
| franchise | character | Franchise name |
| revenue_category | character | Revenue category |
| revenue | double | Revenue generated per category (in billions) |
| year_created | integer/date | Year created |
| original_media | character | Original source of the franchise |
| creators | character | Creators of the franchise |
| owners | character | Current owners of the franchise |