Round Peg in a Square Hole

Introduction #

This post is a prologue to a forthcoming post on survival analytics and featucres the exploratory analysis of a self-generated data set that I will use for another demonstration post in the next few weeks.

Let me give you the background quickly, because I will go deeper with the next post: I have a really chronic problem with insomnia, since I was a teenager. I had a sense that certain foods were triggering bad nights’ sleep so I kept a food diary of what I’d eaten for dinner and recorded my quality sleep hours with a fitness tracker for a year.

This post will show a couple of visual charts, and won’t dwell on the findings so much. I have a different point I want to make today, which was triggered by a look through some Tableau examples.

Pies, Radial and Circular Charts - Are They Always a Poor Choice? #

It’s an old debate, which I thought was settled a long time ago. Edward Tufte makes the point so well in his book “The Visual Display of Quantitative Information.” Humans struggle to compare angles and arc lengths with any kind of estimation accuracy. They often take up excessive screen real estate because most rendering tools require a square space of diameter^2, which is then often hard to fit in around other elements.

Let’s take a look at the examples that triggered me to write this post and then I’ll show my personal dataset with a similar treatment:

Common Birthdays Dataset #

The circular version:

That’s a very cute Tableau Viz with a lot of care and attention on aesthetics. The circular layout is very eye-catching and provides a visual harmony with the large text information block on the right, which left a free square area for the viz itself. The placement of filter drop down in the centre is an excellent use of the available white space.

Based on aesthetic choices alone, I can see why the circular layout was used, but my data analyst eye wants every pixel choice to mean something. Size matters, so why are the areas for days in January so much bigger than December? The answer is no reason at all apart from the geometry of a circle.

Secondly, there’s the matter of eye-tracking and pattern discovery. The science of data communication tells me that I am probably in the majority when I am struggling to follow the cells around the circle. I’m trying to identify the months with the largest population density. Is it September or October? My eye keeps falling off the circular track.

The block version:

So, this is much more boring layout and less attractive. The author hasn’t tried to add any aesthetic elements however, so it’s not a completely fair comparison.

Nevertheless, it answers the points I made about the previous plot. No confusion over size/area of cells and horizontal pattern finding is intuitive and instantaneous.

I know which one I prefer. What do you think? Given it is so much more work to create the radial/polar transformations, is it ever worth it?

Insomnia and Food Triggers Data Set #

So, onto my new self-generated data set. I wanted to do a simple EDA to begin with so I popped the csv file into a new Tableau Viz. To test my assertions about radial charts, I built a dashboard with two vizualizations of the same data.

Unsurprisingly, I ended up in the same place with my thoughts as with the Birthday dataset. Speaking purely aesthetically, the radial view is really eye-catching, while the bars are heinously ugly.

It’s apparent, on the other hand, that the bars chart is just easier to interpret. I couldn’t keep the scales (Time Sleeping) on the radial chart because they’re only valid for strict horizontal and veritcal bars. Dropping reference lines onto the bar chart is a breeze, while it’s a whole additional set of calculated fields for angles and lengths and careful configuration of transparency and placement for additional elements. Frankly, who has the time and the patience, especially in a proprietary tool like Tableau where options are limited (I’d prefer to program it out in R graphics).

Conclusions #

Circular charts look nice and show that you’re clever enough to set up the transformation from cartesian to polar co-ordinates, but that serves one purpose; to highlight the analyst’s prowess, and not to help the consumer of the data product.

To put it another way, just because you can, doesn’t mean that you should.