Dataviz of the week, 7/6/17

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?

Gathering thoughts on best way to display trends by US state. Thoughts on this tile map? 👍🤔👎#rstats #ggplot2 pic.twitter.com/xywzBGqoRh

— Corinne Riddell (@datavisitor) February 28, 2017

You know how people love charts made from emojis?

Stick them together and what do you get?

 

 

axios.com/an-emoji-built-from-data-for-every-state-2408885674.html

Awesomesauce.

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”

Dataviz of the week, 24/5/2017

On Twitter, @SirSandGoblin is tracking polls before the UK general election in the medium of cross-stitch.

Latest pic.twitter.com/bGteFT9ZLD

— grossamund (@SirSandGoblin) May 22, 2017

You just have to look. This is clearly the work of a dataviz genius. I have nothing more to say.

Dataviz of the week, 17/5/2017

nextstrain.org is a website that offers real-time tracking of pathogens as they evolve (flu, ebola, dengue, all your favourites are here). Data gets pulled in from various monitoring systems worldwide and represented with interactive content in several pretty ways:

They have their own libraries called fauna, augur and auspice, the last of these doing the dataviz stuff, and as far as I could tell built on D3. I don’t pretend to understand the genetic and genomic work that has to go on to process the raw data but that is clearly substantial.

Dataviz of the week, 26/4/17

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.

Dataviz of the week, 19/4/17

 

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:

And another:

There’s also a randomisation test which I’ll leave you to discover for yourself.

 

Dataviz of the week, 12/4/17

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.

Dataviz of the week, 5/4/17

It’s the world’s first ever hot air balloon plot. With the emphasis on the hot air.

This is from “The Global Epidemiology and Contribution of Cannabis Use and Dependence to the Global Burden of Disease: Results from the GBD 2010 Study” by Louisa Degenhardt and colleagues. I don’t really get the rationale for the mash up of pie chart with expanded stacked bars. Each of those is common enough by itself, although pies are pretty much never acceptable to the discerning datavizzer, but they map proportions to different visual parameters: angle and length. If you combine them, they just don’t work. Maybe the reason is that the focus is cannabis, and if they included alcohol in the bars it would swamp everything else. But… the stacked bars are actually smaller in height than the pie slice they supposedly expand on! Still, they could use a waffle plot, which is always a favourite of mine:

The other worry is that to make a mash-up, you have to get some bits out of analytics / spreadsheet packages and then get creative in some graphics package. That might just distort things a little. The pie is in tell-tale Stata default colors, while the bars are not; I suppose they were bolted together from different sources.