This visualization was constructed using the vega-lite-api. It was originally forked from one of Curran Kelleher's Vega-Lite API templates and modified to include Franchise Revenue data which originates from R for Data Science's Tidy Tuesday weekly event for July 2, 2019.

This chart shows Total Revenue by Year Created. The goal was to improve upon the previous chart which showed individual revenue streams by the year the franchise was created, making it easier to directly compare franchises.

With grouped data, some observations become apparent:

• Many more high-value franchises have been created in the last 30 years, highlighting the greater potential of new franchises and the more rapid emergence of successful ones.
• There is a loose correlation between the age of a franchise and the revenue it generates. This directly answers the research question, "How does the age of a franchise correlate with its total revenue?" The correlation isn't very interesting on its own, but it does help with comparing franchises that meet or break the trend on the next point.
• Outliers are quite apparent. The oldest, most successful franchises (Mickey Mouse and Winnie the Pooh) are part of the trend, but the visualization makes it easy to see latecomers that lasted long enough to meet their success (Anpanman, Hello Kitty, and Star Wars) and the highest revenue-earning franchise which is impressively younger than all of these high-earners (Pokémon).
• Some older, popular franchises such as Lord of the Rings or Looney Tunes have surprisingly low revenue, but this raises the question of how those franchises perform in specific categories (e.g., Box Office vs. Merchandise) and see if their creators or owners provide a more significant comparison.

Based on these observations, I'm considering several possibilities:

• This visualization gets closer to answering the question "Is there a correlation between the original media type and the relative success of a franchise?" but it's difficult to see as-is. I want to find a better way to express the original media type, so that more conclusions can be drawn; this might be easier to digest if the types are grouped into a more general set.
• I'd like to include a view for specific franchise category earnings. This will expose franchises that may have outperformed in a certain category but do not stand out here because their total revenue is not as high.
• It would be interesting to introduce the original creator and franchise owner to get another dimension into the mix. This would provide an alternate view that will qualify some data points in this visualization.