“Chatty Women”, iterative data, and the tribulations of fitness gadgets (Andy’s Digest, vol 1)

A great pleasure in life, is conuming amazing content across the web. Here’s 3 great things I’ve read in the last two weeks which help me connect data with things happening in the wider world. I hope you like them!

(from The Economist)
  • 5min read: Chatty women and strong, silent men (The Economist)
    • Why should you read this? First because it’s data-driven diversity and second because it teaches us how to have better conversations with others.
    • tl/dr: An Uber exec resigned over a sexist comment (Paraphrased: “More women on the board? They’ll never stop talking”). The Economist looks to see if any data supports that: it doesn’t. However, the data does reveal fascinating differences between gender’s conversational styles and goals. This is great information for all of us to empathise with others.
(from Periscopic)
  • 5min read: Visualizing Spanish Migration (Periscopic)
    • Why should you read this? This is about iteration with data: the only way to insight
    • tl/dr: It is simply not possible to get the best articulation of your data without exploring it and teasing out the best view through iteration. This article excellently describes the iteration process and how it helped develop the best possible end result.
(from Fitbit)
  • 27min podcast: Human vs Machine: Fitness Gadgets (Bloomberg)
    • Why should you listen? Because AI and machine learning aren’t good enough to threaten humans yet.
    • tl/dr: Fitness gadgets suffer from Dead-End Dashboard problems but they’re getting better. This podcast pits them against a real fitness trainer. The conclusion? Like most AI applications, they’re far from fully functional and for the foreseeable future. The point is that for now, augmenting human ingenuity is the right path forward for AI and ML.

Let me know your thoughts – do you have any comments? Do you want to see more posts like this?

Who invented the lollipop chart?

Lollipop graph (WolframMathworld)

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!

Lollipop charts, revisited

Easy on the eye

Stephen Few has written about lollipop charts in his latest blog. “Malformed bar graphs,” he calls them. The poor things – lollipops are more than that.

The problem is, he didn’t consider the problem I was trying to solve when I first created them in 2011*:

Problem: A bar chart with many bars of a long length are unpleasant to look at.

My eyes! They hurt!

I believe lollipops create a visual experience which is easier to look at. In his post, Steve used an example using 7 bars with a large range. I didn’t create the lollipop for that situation. While I believe a bar or lollipop works for that kind of data, criticizing the lollipop without addressing the original intention is disingenuous. I did consider fat bars and thin lines, too, in seeking a solution. Fat bars are just too inky, and the thin lines don’t have enough definition. Lollipops are an attractive compromise to solve the problem.

*Did I invent lollipop charts? Alberto Cairo credited me with their invention in his book The Truthful Art, and I’m not going to argue against that! The images on my original post are no longer available, unfortunately.


The Book of Circles, by Manuel Lima

I highly recommend you check out Manuel Lima’s The Book of CirclesCheck out my thoughts in the 2 minute video below:

Where else would you find data visualization sat next to illustrations of starfish or works of art? I loved this juxtaposition of data and art and nature. I highly recommend this book.

Left: Martin Krzywinski, Comparing Chromosomes, 2007 (credit: Martin Krzywinski) Right: Ernst Haeckel, Drawing of an ophidea, 1904 (credit: Wikimedia Commons)

[for full disclosure, I was sent my copy by the publishers, with no obligations]

Affordances and Signifiers: applying design theory to your dashboards

When designing objects, be they hotel room taps/faucets, iPhones, or cars, the creators grapple with the concepts of affordances and signifiers. These terms were introduced into design by Don Norman, author of The Design of Everyday Things, based on earlier work by JJ Gibson.

What are these and how can we apply them to our dashboard design?

  • An affordance is something an object (or dashboard) can do. A tap/faucet can run hot or cold water, for example.
  • A signifier is an indicator of some sort. In our tap example, this might be red/blue dots signifying which way to turn the tap to get hot or cold water.

How many times have you tried to use a tap in a bathroom and not known which way to turn it for hot or cold? This is a frustration of modern life: apparently a sleek design is more important than signifying (red/blue) the affordance (hot/cold).

Dashboards have affordances and signifiers. How you implement them will influence their success. Let’s use an example. I’m going to use an excellent dashboard by Eric Brown. It allows you to compare gestation periods of different animals.

Let’s play a game. Here are the rules: take a look at Eric’s dashboard and, without using your mouse, identify all the ways in which you can interact with the dashboard?

Click here for the interactive version. Creidt: Eric Brown

How many did you count?

There are eight intentional affordances Eric built into this dashboard. Did you spot them all?

How many of those eight affordances have a signifier?

Here are the affordances:

8 affordances (there are further affordances in the tooltips)
  • 1, 5 and 6 are drop down filters. Drop-downs are a staple of interacting with dashboards, and web pages. But why put the three filters in different places?
  • 2, the light bulb, is a hover-help tooltip. Hover over the light bulb and you see a description explaining how the compatibility score is calculated. That’s great if you’re familiar with the “hover-for-help” trope, but if you’re not, then, well, it’s just a light-bulb. How would you know it contains an explanation?
  • 3 and 4 allow you to click on the animal to highlight it in the scatterplot and see more details.
  • 7 is the colour legend. If you click on one of the colours, it highlights all animals in the scatterplot in that category. Does the dashboard tell you you can click on the legend?
  • 8 allows you to click and see the datasources.
  • Note – did some of y ou think you could click on the silhouettes of the whales in the top right? If you’re a Tableau expert, you might have thought you could. But, no, that is just a legend. It has no interactivity.

That’s a lot of stuff you can do with this dashboard. But only some have signifiers.

We could solve the problem by ensuring every affordance has a signifier. Here’s what that could look like:

All the affordances!

Here are the main changes I made:

  1. Move all drop-down filters into one place
  2. Added instructional text to the scatterplot and colour legend
  3. Removed the hover-help tooltip and placed the calculation explanation in the bottom right, above the data source links

I could now claim to have fixed the “problems” with Eric’s dashboard. Anyone now coming to the dashboard with no prior training in it, or dashboards in general, now has a signifier for every affordance. If they invest the time in reading the dashboard, they will be able to interact fully.

Should I always create a visible signifier for every affordance?

No. Sometimes you are designing dashboards for an internal audience. They may be familiar with dashboard interaction, or you can train them. In which case you could remove the signifiers.

The thing you need to do when designing a dashboard is consider your audience, and how you can communicate to them that they can interact with the dashboard. Skilled users know to click and experiment, or can be trained to do so. New users don’t have that knowledge or confidence. Your job is to make these decisions consciously, not by accident.

Although this example isn’t in my upcoming book, The Big Book of Dashboards is pakced full of successful dashboard designs and tips. Sign up for details here.

Note: Eric’s dashboard is excellent, it looks super and is a pleasure to explore. He’s graciously given me permission to use in this post, and I thank him for that. 

Changing the message without changing the data

Two formats, two messages. Time for a new example?

If you’ve seen me present in the last 3 years, you’ll probably have seen me show the Iraq Bloody Toll chart. Then you’ve seen me turn it upside down to create an entirely different message (full post here).

I still love showing this example to new audiences. I love seeing the light bulb go off as they realise that a data and a chart is just a method of communicating a message: facts are not neutral.

But it’s time to find a new example and for that I turn to you for help.

Have you got any other great examples of charts where the message can be transformed in as simple a way as this one? 

(Note: I’m only looking for examples that stay true to good practice. Truncating the y-axis doesn’t count!)

There are some older examples. Obama’s bikini chart was a cracker, described very well by Robert Kosara in 2012.

Do you know any others? If I can find enough, we could turn this into an entire blog post or webinar. Let me know in the comments or on Twitter.

Is Trump signing more executive orders than anyone else?

[UPDATE: I will be delving into my motivation for building this viz, and how I did it, in a #MyRecentViz webinar on Feb 7th]

Click to see an interactive version

As a left-leaning citizen, I watch in horror as Donald Trump dismantles Obama’s legacy. As a British person, the reports of Trump’s signing of a multitude of executive orders, actions, and presidential memoranda leave me in shock. How can a nation have a system where a president can pass laws without any checks or balances?

“Surely this level of activity is unprecedented,” I think.

Wary that drawing a conclusion based on media reports alone is risky, I sought the data to compare Trump to previous presidents. I got the data from the excellent American Presidency Project.

My data-driven, fact-based conclusion is disheartening: Trump is merely following the lead set by Obama 8 years ago. Barak Obama signed 9 executive orders in his first 10 days. He was the first president to get the pen out to dismantle the previous president’s legacy. Prior to Obama, George W signed two orders in the first ten days. Before him Clinton signed three.

8 years ago, I probably applauded those executive orders Obama signed. Little could I predict that he was setting a precedent that could be used by any future president.

For more on executive orders, Jurist has a good summary. About.com has a good summary of the difference between executive orders and actions.

MakeoverMonday: the price of your Christmas dinner

This is it! The FINAL MakeoverMonday of 2016. What a year it’s been. Thank you to everyone for making this project something exceptional.

Anyway – here’s my makeover. The light of the christmas star shines upon my data this week:


If you celebrated Christmas yesterday, I hope you enjoyed the food. Did you consider how the price of all those parts of it have changed over time? I hadn’t, and the dataset was fun to explore.

Here’s a secret, I really wanted to make the one below my “official” entry to this week:


I love the way it looks like rays of light shining down. Unfortunately, I couldn’t bring myself to make this the “official” one because when the slope lines are pointing down, you just can’t label the lines, which leaves you with a pretty viz but one without insight.

How did I get to this? This was one of the quickets Makeovers I did. Line chart, slope chart, % difference and then the idea for the star:


I’m full of turkey, and goodwill, so this week, I’m not doing any commentary on the original from the BBC.

Next week is a new year. We’re going to stop recording stats and updating the Pinterest board, but MakeoverMonday will continue with new datasets each week.

MakeoverMonday: DC Metro Scorecard

I had one goal this week: could I show all the measures in the space the orginal scorecard shows two?


The answer? Yes, and not just by using 6pt fonts!

Bullets are an easy way to see actual against a target, and they take up way less space than curvy bar charts. Missed targets are encoded twice: the bar is below the target reference line and the label is red.

One thing I don’t like about my approach is the arbitrary axis lengths. Some of the metrics are percentages, so you can set the axis range from 0-100%. That way the viewer can see three things:

  1. The actual value
  2. The distance from the target value
  3. How close actual/target are from perfection.

Where the metrics are values, how should you set the axis range? Look at the charts on the right hand side. They are all very different scales. Should I let the chart tool set the range automatically? If I do that, the bar or reference line will be right at the right hand edge of the view. Or should I artificially extend the axis, creating a nicer sense of white space?

The original

There’s lots to like about the original:

  1. There’s a thumbs up/thumbs down for good/bad performance. That makes it easy to identify which metrics are being met.
  2. The actual value is labelled in the centre of the circle.
  3. The targets are defined in text

The main thing to dislike is the curvy bars. They don’t add anything, other than a sense of colour and false excitement. Really, to fix this scorecard, all they’d need to do would be to flatten out the bars and shrink the layout.

[Note: the original was updated to fix an error in the color encoding of my makeover. Thanks to Clibo for pointing it out.]


MakeoverMonday: Safe States to Drive


This week’s original focusses on the worst states to drive in. It’s nearly Christmas so I wanted to turn that around and take a more positive approach: which state is the best. Turns out it’s Minnesota.

My makeover removes almost all detail (ie 49 out of 50 states!) but I decided after exploring the different metrics to focus on a single message: stay safe, rather than let people investigate the data in each state.

You can see that process in the GIF showing my exploration, below. I looked for correlations and patterns, but once I hit the map, I realised I wanted to focus on Minnesota, and spent about half my time getting the display just so.

Exploration followed by formatting
Exploration followed by formatting

The original

There are things to like in this week’s original:

  1. The colour bands are groups rather than every individual rank. It’s easier to identify a colour band representing 1-10 rather than trying to find the one ranked 6, for example.
  2. Maps make it relatively easy to find your state.
  3. They’ve labelled which is best and worst. Should 1 be “worst” state or “best”? They made a decision and labelled the legend.
  4. The small states have their own callout rectangles.

What don’t I like?

  1. I didn’t notice the “Next” button until writing up this blog. Turns out there are 3 charts, but I didn’t notice them.
  2. Choropleths distort values because one value covers large areas.