I had an amazing time delivering my session about Willard Brinton at the Tableau Conference. Today we made the recordings of the session available to people who attended the conference. If you couldn’t make the session, you can go here to watch the recording.
Along with conference session, I am on a mission to bring Brinton the fame he deserves, and am also cataloguing the amazing things he talked about in his book over on my tumblr, http://100yrsofbrinton.tumblr.com/.
If you coudn’t make it, I’ve uploaded the slides for you – you can see them below. Without the talk track, I fear they are not much more than just pretty pictures. Nice pictures, though.
Have you heard of Brinton before? Will you help me make him famous?
Note: the vintage office photos used in my deck are from the excellent Office Museum.
I put together a quick example in Tableau. Try answering the question: Which region has the highest consumer sales? Is it easier to answer the question with the pie or the stacked bar?
What do you think? The horizontal stacked bar has advantages:
It’s definitely easier to answer this specific question on sales/region with the stacked bar
Comparison across Regions is much easier
If you labelled the marks, they’d be more readable in the bar chart
But even the horizontal bar is not infallible. For example, which is the biggest blue (Consumer) segment in the Regions? It’s actually easier to answer that with the pies, because the blue segment is first in the pie charts
Slope charts are cool. They emphasise change between an end date and a start date by removing the noise in between. For a much more detailed explanation and justification, go read Andy Kirk’s homage to slope charts.
This post is going to show how to build a slope chart in Tableau. It’s not the first tutorial on this: there are others by Ben Jones and Andy Kriebel. The issue with those examples is that they all start with data that has just two time points. What happens if you have lots of time data and just want to show the start/end points?
I’m building this using the Superstore sample data.
Start with a time series.
First use a standard time series chart. In my example, I have Sales by Year for each Container. It doesn’t really matter whether you have a continuous or discrete date pill on your column shelf.
Keep only the first and last values
I want this to be a dynamic slope chart; if I filter the range of dates, the chart should continue to show only the start and end values.
To do this, I create a simple calculated field [First or Last]:
This calculation returns TRUE for the first mark on each line of each Container. Here’s what happens if I put the calculated field onto the Size shelf:
Show only the ends
Why put the calculation on the Size shelf? Because we need to Hide everything that isn’t the first or last values. In other words, everything that our calculation returns as False. Dropping a pill onto the size shelf reveals a legend:
Click on False. Then right-click and choose Hide. Voila! We have our basic slope chart:
Improving the basic slope chart
It’s easy to take this much further.
The first thing you can do is move Container from the Colour shelf to the label shelf. I like this for two reasons:
It’s easier on the eye to have fewer colours
The labels are right on the line so the viewer doesn’t have to move their eyes around the canvas too much to identify which line is which
You can also switch the time pill to something else if you wish. In the example below, I’ve switched to a continuous month and added a quick filter for Month:
We’re almost done.
I don’t like the labels, though. They’re above the lines rather than next to them and they’re only at one end.
To make space on the axis for labels alongside the lines and get the alignment correct, I need to:
duplicate the SUM(Sales) pill on the Row shelf
create a dual axis chart
change the continuous Month to a discrete Month, as shown below:
In just one hour I won’t possibly be able to pack in everything there is to know. Today I am launching a new tumblr, 100 Years Of Brinton. Over the next few months, I’ll be posting snippets from the book. My hope is these will inspire and entertain you. The ultimate goal? For everyone, not just dataviz nerds like me, to know about Willard C Brinton’s amazing book.
I hope you come along to the session. If not, follow the blog, and let me know what you think using the hashtag #100yrsOfBrinton.
Last week I attended The Graphical Web in Winchester. Tableau were sponsors and I was lucky enough to get to spend time with the people at the cutting edge of open source web graphics. Here’s 7 things I learnt:
1. Google’s maps are like leaves
One key theme from all cartography sessions was that effective cartography (and, by extension, data visualization) is about taking out as much information as possible. Ed Parsons showed the iterations of Google’s maps as an example. In the past, Google feel into a typical cartographer’s trap of trying to show all the info.
Now, when you search, you get much less information. The colours are more subtle. Ed explained how they took the simplicity of a leaf as inspiration for their newer road network palettes.
2. Circular diagrams can work
I’ve never really liked chord diagrams, thinking there is always a better way to show the data. Nikola Sander changed my opinion in her explanation of the migration data she works with. Not only was the transition from a table of numbers to a chord diagram visually appealing, I came to realize that I’m not sure there is a better way of looking at this kind of data.
Chord diagrams cannot generally be digested quickly, but once the user has trained themselves to use them, they are an effective method for complex data.
3. You don’t need to be a proficient public speaker to be engaging on stage
Jason Davies, co-author of D3 led the afternoon keynote. From a public speaking perspective, he session not great: not much structure, a bit hesitant, and Jason doesn’t always project his voice well.
So how come this was one of the best sessions of the conference?
Answer: because his work speaks for itself. What Jason has done is push interactive graphics forward with great humility. A showreel of his work is enough to engage an audience as he walks through one after another amazing piece of geometric madness.
4. Twitter’s Visualisation Lab doesn’t sit still
Nicolas Garcia Belmonte led a fantastic review of the interactive work at Twitter (slides here). What amazes me is that his team comes up with such varied ideas. We often see teams have one or two great ideas and then overwork the same idea until it is no longer inspiring. One look at Twitter’s interactives page tells you this is a team with inspiration.
I get the feeling that these visualisations don’t get enough exposure outside the field of data visualisation. Do you agree? What can we do to change this?
5. “It depends” is the only right answer.
Scott Murray’s talk “The Keys to a Successful Data Design Process” (slides) generated a good debate. How should you go about your design process? It depends. How should you design your chart? It depends. What data should I use? It depends.
This is something I’ve touched on before in my post on data storytelling: you can’t have rules or laws in Data Visualisation – everything depends on something else.
6. Weather guarantees viral content
My favourite session was Cameron Beccario’s story of how he created Earth, the amazing live wind map of the globe. I love this kind of story: a project of passion that uncovered many many side projects and problems. It’s a story of data hunting, skill learning, and serendipity that ends in huge success.
7. I had dinner sat next to a cannon.
That’s cool. Alan Smith and the Office of National Statistics did an amazing job organizing a great conference. I had been suspicious of the need to drive to Portsmouth for a good evening reception, but a tour around the harbor followed by dinner on the gun deck of HMS Warrior was amazing.