The Rosetta is about to attempt to land on a comet. This is astonishing and exciting. Here’s some incredible photos of the comet on the New York Times. In honour of this event, here’s a post about comet charts:
If only I’d gone vertical and not stayed with horizontal.
“Comet chart”? But… But…. But…. I came up with that idea in 2012. How dare they steal my idea.
What? You’ve not heard about my comet charts before?
That’s fair enough: they were a doomed experiment several years ago and only ever seen in a thread on our Tableau Community. Below, in its non-intuitive glory is my comet chart:
(Before I continue I’m happy to acknowledge other reasons you might decide the above dashboard doesn’t work)
Zen Armstrong’s version succeeds where I failed. Her up-down orientation fits in with ones mental image of growth/decline and gravity. If only I’d thought about trying that. In order to make my chart more readable, all I needed to do was orient the marks differently:
It might come as a surprise then that I thought his talk was entertaining, inspiring, well-structured, well-meant and funny. I agreed with virtually everything he said.
I was very impressed with his new model for the pursuit of data-led knowledge. Raw data are the atoms. They become structured data (molecules) which become linked information (chromosomes). And so on to inter-connected knowledge (organisms). David applied this model to the field of climate science and it fits. It’s a model I could use when talking to companies and journalists.
When talking about his motivation, it’s the same we all feel: all he wants to do is tell engaging stories with data and design.
McCandless focused on how he got into being a graphiste: accidentally. He was a programmer, then a journalist, then in advertising, and finally it all came together as a visual designer. This resonates with me. I never planned to get into dataviz, but my history as budding comic artist (I was young!), geographer, journalist, software engineer and database admin gave me the tools that, when data viz turned up, were all I needed to pursue a passion.
He’s also modest, funny and engaging as a speaker. He doesn’t preach his thoughts, he shares them. He doesn’t say his way is the right way, he just explains the way he does it and what happens when he does. His ultimate advice is to just get out and play with the data.
What then, are the criticisms? These days I measure a visualisation against Alberto Cairo‘s 5 values of visualisation:
Functional? This has always been the most contentious for those of us who first learnt our craft from people like Stephen Few (who also has harsh words to say on the topic). McCandless’ charts are not designed to best align to visual perception. In so many cases, boring bar charts would be better if the aim is to aid fast comparison of values.
What I realized tonight is that there’s a problem trying to critique McCandless. I suspect his response to any criticism would be “Yes, you’re right. I’m just trying to communicate a story and somehow people seem to like the way I do it. I’m not saying I’m right. I’m not saying I’m the best. I just do it my way.”
As for truthfulness, he’d probably agree and say: “Every single data source is linked to on my site. I may not be wholly accurate but I am open with where I get my data.”
I do believe he needs some more practical advice for people who look to him not just for inspiration but for practical ideas of how to get started. Currently he makes it sound harder than I believe it is.
Where does that leave me? I used to be a disciple of the Stephen Few world, but am no longer so zealous. Data visualization needn’t be polarizing and McCandless’ motivation and humility earns my fullest respect.
One can always expect visually attractive stuff from David McCandless on his Information is Beautiful blog. Sometimes though, the visual displays potentially confuse viewers. Today’s great post on Wikipedia Banner Tests is an example. Representing the size of donations using a square is pretty, for sure. However, we tend to perceive the relationship of one area to another pretty badly. A linear representation is more effective:
Think back to David’s orginal squares view – would you really have guessed that the Jimmy Appeal raised fifteen times much cash? If so, you’re one of the few – most people would have said it was around 8-10 times larger. The linear view above makes that difference much more clear.
There’s also some interactivity on my view – you can change the measures being displayed in order to explore the data a little more.
There’s a chart doing the rounds that shows who is suing whom in the telecoms industries (eg at the Guardian). David McCandelish has improved it nicely here, and asked the question of whether companies who’s revenue is going down are more litigious than those on the rise. Well, I took his data from here and created the charts below.
There are lots of caveats: I excluded Smartphone Technologiess LLC because the data didn’t say if their revenue was up or down. That’s a shame, because they’ve got the most lawsuits open. Also, we don’t know if the revenue was going up or down when the lawsuit was started.