Tidy Tuesday screencast: analyzing franchise revenue

I analyze a dataset on franchises (Pokémon, Harry Potter, etc) as an example of exploratory data analysis in R, performed without looking at the data in advance. This includes reconstructing a stacked bar plot to explore the top money-making franchises of all time, and examining how they've changed over time.

[vebashininaidoo]

At around 50:50 you use a paste0 to tag on the "B" - there are many ways to do things in R which is part of the beauty of the language! I wanted to post another way, which is to use the `scales::dollar()` suffix argument ;)
As in:
label = scales::dollar(total_revenue, accuracy = 1, suffix = "B"))

[vebashininaidoo]

Thanks for the screencast! It's excellent.
At around 6:20 you create the most_profitable using the nifty which.max() trick. The problem is the function doesn't pick up the right category (for e.g. the Song of Ice and Fire franchise should show 'TV' instead it shows 'Book sales'; the Aladdin franchise should show Video Games/Games); it seems to just return the first revenue_category instead of the most profitable :|. I think it may have to do with the fact that we're not using a summary function here (this is more like a mutate), but we're doing it in a `summarise()`.
This can be fixed using the `first()` summary function:
most_profitable =
Thanks again for sharing your knowledge in such a wonderful way!

[howardbaik]

THANK YOU SO MUCH. I've waited so long for a new vid

[stoianandreimircea]

Welcome back prince of darkness:) you went to the dark side of the moon? We missed you. Well the greater we. I certainly did. Thank you for posting. Keep up the good work. Can us little ones help this comunity in any way?

[xiaoranmo]

use guides(size = F) to remove size legend.

[ianmkos]

Nice analysis. But you're conflating revenue and profitability. They are not the same.