(e.g., perhaps more people do chess who are in their twenties, so including the whole sample pulls down the mean)

Appreciate this! The reasoning for cutting was that taking the full average would have the effect of introducing a sample size effect with different entrances into chess by age. But to your point, I wanted to make that cutting clear in the methodology.

This one was a really good episode

code is too short to make into a post, but can be found in this screenshot

or maybe marginal histograms/density plots from the `ggExtra` package?

Some stakeholders like the data output for their review as an Excel file, especially if I can have a summary sheet, and then the data underlying it in other sheets. I use the excellent `writexl` package to form these, then will go into excel to play with the column widths. #rstats

This was a great challenge for me to do a few explorations on datasets from @datavisfriendly.bsky.social, @ropensci.org and NYC Open Data. Really appreciate that this work is out there to explore and learn from!

Appreciate the comment - I found it interesting that the most common number of whorls or loops (0) is different from the most common combination (0,3), which makes me want to do Pearson's test of independence to check it out!

@datavisfriendly.bsky.social - the predicted values were added from his 1907 paper, and I wonder if it may be a nice inclusion in the HistData package

www.medicine.mcgill.ca/epidemiology...