I wanted to put this post together because it’s something I’ve been thinking about too. My solution is slightly different. Consider the side-by-side bar chart at the top showing sales of Product A and B over ten years. Too much ink! It’s confusing and impossible to interpret. It’s really hard to see anything.
How else can we show this info and ask “in which years did B outsell A?” Simple. Do something heretical and connect the dots using a line (what? Use a line to connect discrete values? But you can’t do that!):
Because we’re so well evolved to see slopes, we quickly and easily see the three years in which B outsold A:
In this example, because it’s sales over time, I kept the years as separate panes.
With slightly different data, you can acheive the same results using a categorical slope chart. I’m doing this as part of my analytics based around the UK General Election (http://impartialityuk.tumblr.com/).
I’ve kinda always felt a little like the Ancient Mariner. He went through a crazy experience and then felt driven to share it with everyone he sees. That’s how I feel about visual analysis and Tableau.
USA and Russia dominate the Olympic Medal tables but is that simply because they are large countries? Are there countries who get more medals per million people in their country? Yes. Check out the dashboard below:
Here are two dashboards. The bottom one uses all Tableau’s default formatting settings. The top one has at least 25 formatting changes or design decisions: these changes take a few hours to implement. Why bother? What’s wrong with Tableau’s default formatting?
The short answer is: nothing. The longer answer is more nuanced.
It’s Tableau Public’s Design Month so I wanted to do a series of related posts. In these posts, I’ll be focusing on the dashboard above (click here for bigger version). This was my entry into Tableau’s annual internal “VizWhiz” competition. In this round, we were given data was about US road traffic accidents.
There are at least 25 things design decisions I’ve made to produce that viz. That’s 25 changes I have made to the default Tableau formatting: some small, some large.
But first: why change the default formatting? What’s wrong with Tableau’s defaults?
Let’s look again at the default dashboard:
Let me repeat: There is nothing inherently wrong with Tableau’s defaults.
My story can be understood from the default dashboard above. In fact, I am sure some people reading this will think the defaults are better than my design. If so, let me know in the comments below.
So why bother? Why would I spend hours tweaking what is already a perfectly-fine dashboard?
I take my inspiration from ex-British Cycling performance director Dave Brailsford. He led the British Cycling team to huge success through his “marginal gains” (click here to find out more). The principle is that you make all the small and large changes you make. Even the small changes, when aggregated, make a difference.
Each formatting tweak might only improve my viz a tiny bit.
The default formatting gives you a bronze-medal dashboard. There’s no shame in getting a bronze medal for something. If you’re in a business environment, producing dashboards for fast, iterative consumption, it is perfectly fine to leave the defaults as they are.
However, what if you want the Gold medal? In this case, you make all the changes you can. Even if they are small, the aggregate effect is significant. For that reason, I am happy to go the distance and make grab every “marginal gain” that I can.
Over the next month, I’ll be describing most of the formatting and design decisions I made.
Humblebrag alert: I am pleased with my dashboard but am wide open to criticisms about it. I am sure that what I think are good decisions might well seem like bad ones to you)
Are you looking for a gift for the data geek in your life? Well, now’s your chance to get your hands on some pretty unique data viz gear. I’ve always printed my own t-shirts for Tableau conferences and I’m now making them all available via my Zazzle store.
I’ve written before about the problem of “rules” and “laws” in data visualization. A classic one is “Thou must start your axes at zero.” If you’re reading my Brinton blog, go see what he had to say about it)
In this post I want to dispel this myth. It’s a myth that’s close to my heart. In August 2009 I went on the record (by commenting on The Guardian’s Datablog) that I disliked one of their charts because their axes didn’t start at zero.
Let’s take the data from that Guardian article in order to investigate this Rule. Here’s how it looks with zero included:
This is bad for at least 4 reasons:
It doesn’t really expose the change of the record over time.
It especially doesn’t highlight the impact Usain Bolt had on the record.
It doesn’t make great use of the space – there’s lots of dead space.
What happens when you break the rule?
All the problems are removed. It’s engaging, Usain Bolt’s impact is clear and it makes great use of space.
There’s one final reason not to include zero in this case. I do not know what the ultimate fastest time a human being will run 100m, but I can guarantee it isn’t zero seconds. My point is that not all measures you are charting have real zeros. In this case, the “zero” might be 9 seconds or so.
Once you learn the guidelines, you’ll be able to fine tune your charts by bending or breaking them according to your use case and objective. Sticking to the rules means you will satisfy the 5 criteria Alberto Cairo defines for a successful chart (he discussed these at his 2014 Tapestry Keynote):
In the 21st century, the age of big data and the Internet of Things, it’s easy to get carried away logging everything you do in databases. I find there’s a charm and happiness in doing some data logging the old-fashioned way: on pen and paper.
What’s the log book above? I write down every book/film/gig/concert/play I read or see. I add a date and a score out of 5. I started the log in 2010. I was complaining to a friend, “Oh, I wish I’d kept a record of every book and gig I’d been to in my life.” It was probably the third time I’d had this conversation with her.
She replied, “Andy, quit bitching and just start one now.”
Good point. I did.
I love the physical object, and the easy nature of browsing back a few years. Sure, I could log this on Goodreads.com and equivalents, but it’s not the same. And part of me thinks it might be something I can share more easily with my kids one day. It’s also an anti-Tableau thing. I love Tableau but, you know what, sometimes I want my data to stay away from the screen. Unlike my music habits of course.
wanted to share another one we received at work recently – this is data collected about Premier League players by someone when they were 8 years old:
Do you collect data? Post a link in the comments below or on Twitter. Let’s share our manual data logs. Geeks of the world: Unite!
The decision favours developers and hinders data enthusiasts. Why? What makes developers a better class of person in Twitter’s eyes? Why can developers with their fancy pants Python, PhP and Ruby skills get their hands on the data, but the rest of the world can’t?
Scraperwiki’s service, in my eyes, removed the middle man for those who don’t know how to code.
Apart from keeping developers in business, I don’t see the difference between the scenarios. Feel free to explain it to me in the comments.
Twitter, if you’re listening, why don’t you support data democracy?