I’ve been stockpiling Opal Fruits, which young people tell me are now called Starburst, in anticipation of today’s election results.
This is like one-tenth of the stash. I don’t want to eat them though. You know what you’re going to get if you knock here at Halloween.
I took the New York Times’ hexbin cartogram, imposed a 6×8 rectangular grid and counted the most common party in each block. There was a little bit of fudging and chopping up the sweets. It is art, no? Here’s the video:
You know how people love maps with little shapes encoding some data? Hexagons, circles, squares? Jigsaw pieces? Opal Fruits?
Rip’t from the pages of the Times Higher Education magazine, some years ago.
Or small multiples?
You know how people love charts made from emojis?
Stick them together and what do you get?
This is by Lazaro Gamio. They’re not standard emojis. Six variables get cut into ordinal categories and mapped to various expressions. You can hover on the page (his page, not mine, ya dummy) for more info. Note that some of the variables don’t change much from state to state. Uninsured, college degrees, those change, but getting enough sleep — not so much. It must be in there because it seems fun to map it to bags under the eyes. But the categorisation effectively standardises the variables so small changes in sleep turn into a lot of visual impact. Anyway, let’s not be too pedantic, it’s fun.
This idea goes back to Herman Chernoff, who always made it clear it wasn’t a totally serious proposal, and has been surprised at its longevity (see his chapter in PPF). Bill Cleveland was pretty down on the idea in his ’85 book:
“not enough attention was paid to graphical perception … visually decoding the quantitative information is just too difficult”
On Twitter, @SirSandGoblin is tracking polls before the UK general election in the medium of cross-stitch.
You just have to look. This is clearly the work of a dataviz genius. I have nothing more to say.
This chart of population density across Europe by Henrik Lindberg has been very popular online this last week.
Long-standing readers will recall my stab at this but nowadays everybody just does it in ggplot2. It’s good to have options. While you’re at his Gist page, checkout his other stuff too.
This is just the greatest thing I’ve seen in a while, and definitely in the running for dataviz o’ the year already. Emoji scatterplot:
There’s also a randomisation test which I’ll leave you to discover for yourself.
This week, a chart with some Bayesian polemic behind it. Alexander Etz put this on Twitter:
He is working on an R package to provide easy Bayesian adjustments for reporting bias with a method by Guan & Vandekerchhove. Imagine a study reporting three p-values, all just under the threshold of significance, and with small-ish sample sizes. Sound suspicious?
Sounds like someone’s been sniffing around after any pattern they could find. Trouble is, if they don’t tell you about the other results they threw away (reporting bias), you don’t know whether to believe them or not. Or there are a thousand similar studies but this is the (un)lucky one and this author didn’t do anything wrong in their own study (publication bias).
Well, you have to make some assumptions to do the adjustment, but at least being Bayesian, you don’t have to assume one number for the bias, you can have a distribution. Here, the orange distribution is the posterior for the true effect once the bias has been added (in this case, p>0.05 has a 0% chance of getting published, which is not unrealistic in some circles!) This is standard probabilistic stuff but it doesn’t get done because the programming seems so daunting to a lot of people. The more easy tools – with nice helpful visualisations – the better.