This is a post is a small thought about one piece of Data Visualisation best-practice.
“Jeez Chris, get a life”. Yes, I know, here I am again. Get over it. I happen to think this stuff is quite important.
This is a reply to and critique of the chart made by Jeff Plattner and recreated by Andy Kriebel in his blog post Makeover Monday: Restricted Dietary Requirements Around the World | Dot Plot Edition
I’d originally fed back on best practice to Jeff on Twitter but given Andy has chosen to recreate the chart and has a huge audience for his blog I felt it was worth a blog post in reply to point out, what I think, is a small best practice failings in the chart.
Let’s compare the three charts below, the last being Jeff’s original:
I love Jeff’s take on this subject. I immediately fell in love with the design and loved the “slider ” plot which I’d not seen used so effectively.
However there is a subtle difference between this last chart and the two above.
All three have axis that shows the range of the bars / lollipops / sliders at 50%. This is a design choice which both Andy and Jeff (for the first and third charts) both said came from wishing to make the chart look better.
Now here comes the rub for me. In the first two the shrinking of the axis doesn’t take away from the audiences understanding of the chart. However in the last “slider” chart it does. Why? Because the chart has an end.
Why “The End” matters…
The end of a visualisation mark / chart is important for me, because if it exists then it implies something to the reader. It implies that
a. the data has a limit
b. you know where the limit is and can define it
c. you have ended the chart at the same place as the limit of the data
Let’s look at the three aspects here with our data
a. ✔ the limit of the data is 100%
b. ✔ no region can be more than 100% of a given diet
c. ✖ the line ends at 50% in Jeff’s chart
Why doesn’t this matter for the first two charts? Well these two charts don’t have a limit set by the chart. Yes, the bar and lollipops end, but we’re forced to look elsewhere to see the scale. With the “slider” chart, then in my opinion, the reader feels safe to assume that a dot half-way along a line means that half the people in that area follow the diet. They don’t go further to look for the scale – despite the fact Jeff has clearly marked the limits.
This perceptual difference between the charts is important for me, and a good reason not to limit the axis at any other value than 100%, as I have done below by remaking Andy’s Remake.
It this the biggest faux paux in the history of Data Visualisation? On a scale from 0 to 3D Exploding Pie Chart then we’re at 0.05. So no, not really, but I thought it was interesting enough to share my thoughts on what was an excellent Viz by Jeff.
As ever these are only my thoughts, they’re subjective, and many of the experts may not agree. Let me know what you think. Is the visual improvement worth the sacrifice of best practice?
Comment here or tweet me @ChrisLuv