I’m delighted to present “New Ways to Visualize Time at TC Europe in London. The content is based on one of the chapters in my book, The Big Book of Dashboards.
This post contains links to all the resources shared in the session.
The trend line is amazing. It shows peaks and troughs and trends. But if you only ever use trendlines to show time, you are missing insights in your data. What’s the best way to show time in visualisation? I cannot answer that: it depends on your data, the story you want to tell, your audience, and many other things.
Which way is time?
Time can go up and down as well as left and right. Generally in the West we point forwards or to the right, but that’s not the only way. Check out these:
For further reading on how time is considered in culture and in data, check out this wonderful article, History on the Line, by Stephen Boyd Davies.
We forget (or don’t know) that even the most common chart types were once ideas waiting to be thought of. Even though we build them every day, timelines were invented. Here are the milestones I highlight in my talk
1493: People have been making chronological charts for centuries. I love the examples from Hartmann Schendel’s Nuremberg Chronicle, published in this year
1753: Jacques Barbeu Dubourg makes a chart with a fixed x-axis. Unfortunately, his chart is 54ft long! He invents a really cool scroller to deal with this problem
One of the examples I used is based on US Road Fatalities data. I used that data to create a dashboard that was comeprehensively described and deconstructed in my “Design Month” series of posts in 2014.
I wrote about a similar topic for the Huffington Post, “New ways to see time.” It has some other examples.
Last week, Stephen Few critiqued lollipop charts and I wrote a post in their defense, in which I claimed I coined the term “lollipop chart” when I first wrote about them in 2011.
Stephen Few claims he’d seen them several times before I made them in Tableau. I accept that’s entirely reasonable. I remember at the time I thought my idea was original, and if I’d seen them prior to my post, I hadn’t consciously registered them.
Stephen also sent me a link to the lollipop graph, on Wolprham Mathworld. One of his readers had googled the term lollipop chart back when I wrote my post. The purpose of the lollipop graph is different to that of the lollipop chart, but coining cutesy names is certainly not something exclusive to me (tadpole or barbell graph, anyone?
Which all renders my claim to inventing them hanging on by a thread! I probably wasn’t the first to make a lollipop chart. I wasn’t the first to come up with a cute name. Maybe, just maybe, I can claim the ever-shrinking privilege of coining the lollipop chart!
I want to know what your favourite week was, and why. What have you learnt? What have been your highlights (and lowlights)? What’s the effect been on your community? And the wider dataviz community?
I’ll be writing a post on tableau.com before Christmas so please share you reflections with me, in the comments, on Twitter, on your blogs, or anywhere else I’ll see them!
* What does “coming to an end” mean? Andy will continue to add datasets each week. However, as of the end of the year, we will cease to update the Pinterest board and the dashboard of statistics. We hope you all still continue!
Andy Kirk and I did the 2016 #AskAndy anything webinar today. We hope you enjoyed it. Let us know your thoughts on Twitter using #AskAndy. This post contains the slides and links to the resources we shared.
We needed some Austin-related data for MakeoverMonday live at Tableau Conference. We turned to Restaurant Inspection scores from Austin’s data site.
I went in search of lunch in order to do the makeover, and found myself in Franks, home of hot dogs and cold beer. I sat down and ordered a bacon-infused Bloody Mary. Seriously? Bacon in a Bloody Mary? It was amazing.
Anyway, it got me wondering how well Frank’s had performed in recent inspections. That led my direction. I reduced the entire dataset to just Frank’s inspections. Turns out their last inspection was right on the borderline of failure.
My conclusion? Wonderful Bloody Mary. They passed my Restaurant Inspection!
Andy and I hope you all enjoyed MakeoverMonday live, wherever in Austin you ended up doing it.
I had a great time keynoting at the Crunch Conference in Budapest last week. What a great city and what a thriving tech scene!
My keynote was the Beautiful Science of Data Visualization: my favourite subject! The original content was developed by Jeff Petiross. My version has evolved from his, but they’re essentially covering the same content.
I was really impressed by Carlos’ sketchnotes. Too often, sketchnoting doesn’t actually capture info in a way I want to read it. However, Carlos creates sketchnotes which are amazing summaries. Go check out the rest of his stuff!
Another cracking day, judging by the scores. The leaders were having a good old ding-dong battle. Pasi faded at the end of the second round, but Juho stormed on through:
Players clearly learned a new approach on hole 1 today. In round 1, there were around 60 pars and over 100 over pars. Today? More people got par than any other score:
Now – what we all want to know is what are our Golden and Brown rounds? The golden round is when you take the best score of each hole and total that. The brown round is the opposite: what’s the worst score?
Here’s a static image, but the interactive version is below:
Finally well done to Team GB’s junior, Ned Morris, who had a great second round. Look at that steady-as-a-rock run of pars from holes 9-16:
Ned – don’t worry about bogeying holes 16 and 18. Derek’s done the same in both rounds:
Each screenshot has a link to the interactive version.
First of all – you can compare players against each other. Here’s team GB’s progress through round 1. Chris O’Brian did extremely well, while Derek, Dan, James and Si all ended up within 1 shot of each other:
How about the holes? Well, hole 1 was brutal. 4 birdies and >100 bogeys or worse. Ouch. Hole 4 was the opposite: 40% of all players birdied it today.
And how did the Open division do in terms of Birdies/Bogeys? Well done Pasi Koivu for getting 11 birdies! Amazing.