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.
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.
Here’s a graphic of a really deep oil well by Fuel Fighter via Visual Capitalist. This is rather reminiscent (ahem) of the long, tall graphics by the Washington Post (and the eerily similar one from the Guardian a few days later which they had to admit they had nicked) about flight MH370 at the bottom of the ocean. The WP graphic works because you have to scroll down, and down, and down, and down, and down (wow, that’s deep!), and down, and down (no way), and down before you get to the sea bed. Yes, all the usual references are there, hot air balloons and Burj Khalifas and Barad-Dûrs and what have you, but they don’t matter because it’s the scrolling that does it, giving you GU2 (“Conveying the sense of the scale and complexity of a dataset”) and GU6 (“Attracting attention and stimulating interest.”) The references don’t mean anything to me (or probably you); I may have seen the Burj Khalifa and thought it was amazingly tall, but I have no grasp of how tall and that is what matters: I’d have to have an intuitive feel for what 3 BKs are compared to the height of a jet aircraft, and I don’t have that, so why should I care about the references?
My problem with the Fuel Fighter graphic is that it doesn’t have that same sense of depth. The image file is 796 x 4554 pixels, which is an aspect ratio of 1:17. The WP image (SVG FTW) is 539 x 16030 or 1:30, which is pretty extreme! It feels to me like you’d have to get past 1:20 before it started to have enough impact.
The Washington Post have an article about the US budget out by Kim Soffen and Denise Lu. It’s not long, but brings in four different graphical formats to tell different aspects of the data story. A bar showing parts of the whole (see, you don’t need a pie for this!)
then a line/dot/whatever-you-want-to-call-it chart of the change in relative terms
then a waffle of that change in absolute terms, plus a sparkline of the past.
there’s also a link to full department-specific stories under each graphic. I think this is really good stuff, though I can image some design-heads wanting to reduce it further. It shows how you can make a good data-driven story out of not many numbers.
Corinne Riddell posted this on Twitter. It’s one version of multiple time series that she tried out, one for each USA state. It’s not the finished article, but is really nice for its combination of that recognisable shape (I suppose if your country has a dull shape like Portugal — no offence — then readers won’t immediately recognise the meaning of the arrangement) and the clean, simple small multiples. Admittedly, the time series has enough signal:noise to make this possible, and only a few unusual states, and without that it might start to get like spaghetti, but it’s always worth sketching options like this out to see how they work.
Could the state names get dropped? Probably not, but they could turn into two-letter abbreviations. The whole idea of the small multiple is that it’s not a precise x-y mapping but a general impression, so the y-axis labels could go (just as there are no x-axis labels).
Overall, I really like it and would like to write up a UK function for this to add to my dataviz toolbox.
Filed under R, Visualization
Frank Harrell suggested this rather nice plot of multiple continuous variables. It’s part of his ‘rms’ R package which does some kind of linking to Plotly. I’ll have to look into it properly. There are mini sparkline-ish histograms on top of one another, and note the little lines under each x-axis: those show the location of whatever location & spread stats you want. The nice features are:
- easy to compare
- doesn’t hide the data
- shows the stats too
Note the chartjunk font; it would be great if someone extended this just a little to have the under-spikes as well.
This is in the Brexit White Paper, so a little bit important. Lots of people have been sharing this on Twitter. I can imagine the intern thinking “this is my big chance, must pay attention now, even if it’s 4 am and I’m really tired, come on now… how do I do this bar chart thing again?”
Then they hit “Send”, followed by the comedy trombones: bwaaap bwaap bwaaaaaaap.
But on the bright side, apparently everyone in Britain has been entitled to 14 weeks of holiday each year for ages. I am due some serious back-pay.