MakeoverMonday live in Austin!


Andy and I are very excited to be doing MakeoverMonday live in Austin at the Tableau Customer Conference, on Monday November 7th. This is our chance to say thanks to everyone involved, welcome some new friends, and play with data.

Here’s everything you need to know.

Where and when is it?


It’ll be at Level 2, Westin. We start at 2.30pm and finish at 4pm. Full details are in the data16 app (available on Android, iOS and Windows phone)

Will it be full?

YES. Due to many other factors, we could only secure a room for 100 people.

If you want a spot, get there early!

What if there’s no room?


We’re sorry we couldn’t get a bigger room. But all is not lost! Here’s what we recommend you do:

  1. Say hello to the three people standing nearest to you.
  2. Invite them all to the nearest bar or cafe (there is no shortage of choice)
  3. Have a coffee/beer/cocktail and do MakeoverMonday wherever you end up. Cheers!
  4. Share a photo on twitter with #MakeoverMonday hashtag.

We will have flyers to hand out with the info you need to do the Makeover wherever you end up.

What’s the data?

The data will be shared live on Monday and on Twitter. Here’s the link to all the MakeoverMonday datasets.

Safe travels, and see you in Austin!

MakeoverMonday: Scottish Index of Multiple Deprivation


My first thought on seeing this week’s original was to try another way to show distribution. I turned to the boxplot, an under-appreciated chart. Steve Wexler, friend and co-author of The Big Book of Dashboards, really dislikes them, suggesting that laypeople don’t understand them. I disagree, and think that a lack of understanding is only caused by lack of exposure to them.

Hopefully my “How to read a boxplot” instructional image at the top helps those unfamiliar with them!

Boxplots pack a large amount of useful info:

  1. The whiskers spread to show outliers. Glasgow has a high SIMD score, but the data is very spread.
  2. Comparing location is much easier. Consider Glasgow/Dundee in the original and the boxplot:
    glasgow-dundeeIt’s much easier to compare the two cities in the boxplot.

My boxplot still needs more work, which I would do with more time. I think it’s important to know how many data points are in each category. The Shetland Islands has a really narrow box, but that’s partly because there are only 7 items, compared to, say, 133 in Glasgow.

MakeoverMonday: US National Debt


We threw you a curveball today. Only two numbers? This is a great challenge.

I did play in Tableau for a while, but then began to think about what these numbers really mean, and what the goal of the original infographic was. The problem is that comparing national debt to anything else is like comparing apples to oranges. And if you do compare it to other things, you run the risk of suggesting that because it’s so large relative to other things that there’s a problem.

That isn’t necessarily true. National debt isn’t like household debt, or currency, or assets. It’s a funny old beast. Here’s 3 of many amazing articles about this:

All of which isn’t too say that high levels of national debt aren’t a problem: they are.

My makeover’s goal was to make it clear that the different values aren’t the same kind of thing. Since there were only two numbers, it seemed right to pull out the pen and paper!

This week’s original

All of the above is one thing, but at the same time, I concede that the original wasn’t explicitly trying to say that US National Debt and, say, all the currency in the world, are similar. They were just trying to give you an idea of what the value represents. I do think there’s a lot of scaffolding around just a few numbers, but as an infographic to sit down and digest, it was a compelling read.



MakeoverMonday: US Election Poll

I got some real insight this week: all states ebb and flow for/against each candidate at pretty much the same pace. If that’s the case, why do the candidates pour resources into swing states, since all changes are reflected at the same level on the national scene?

This is reflected in the GIF below:

Click here for an interactive version
Click the image to see the animated GIF.

The US election is around the corner and this week we turned our attention to the polling data. We’d like to thank Drew Linzer for allowing us to use the data from the Daily Kos site.

My goal was this: how could you tell a story in a different way to all the poll trackers, without a map? I decided to drop the data on the independent candidates (sorry Johnson and Stein) and focus not on the actual polling percentage of Clinton and Trump, but the difference between their polling percentages. The gap is more interesting to me.

Show me the gap!

I goofed around with just making the chart of the gap, which was interesting. You can see that while Trump’s been in the lead a few times, he’s never pulled out a big gap, or held onto it for long.


Once I’d drawn that chart, it was then a simple case of adding State to the column shelf and realising that there was beauty and insight in the pulse of all 50 states. By adding the animation, I hope I’ve emphasised that all states go up and down at the same pace.

One thing, though – my chart is an elaborate way of showing the same thing as this line chart:

Each line represents polling position of each candidate in each state over time.
Each line represents polling position of each candidate in each state over time.

But where’s the fun in just doing a simple line chart? 🙂

I built an interactive version too.

The original

The Daily Kos tracker is great. There’s a challenge with poll trackers: how do you make them interesting? The Daily Kos tracker is pretty similar to the ones on FiveThirtyEight, HuffPo, WSJ, etc. The good news is that in this election, the data was volatile, so the trackers were interesting charts to look at.

Boring data makes for boring charts (this is from the 205 UK Election)

As I wrote after the UK Election last year, the poll trackers used by the media were unsuccessful (in terms of drawing in audience) because the data didn’t change.


It’s the small dataviz things: Paragraph Legends

How do you communicate what the dots, marks, and lines on your chart show? Most often, you’ll use a legend. They work well, but check out the this from the Huffington Post. They created a Paragraph Legend (as I’m going to call it).


Why’s this great? I mocked up what this might look like if we used a regular legend. Try and decipher the chart using the “traditional” approach:


In order to decipher the chart you need to read the paragraph. Then the chart. Then go to the legend. Then back to the paragraph. Then back to the chart. Finally you might understand what’s on show.

Now look at the Paragraph Legend. Read the paragraph, look at the chart, and then maybe back to the paragraph once more. I found it much much easier to decode the chart with the Paragraph Legend. Like all small things, this is harder for the designer, but an improved experience for the audience.

[This is the second time I’ve reused Andy Kirk’s amazing idea to blog short posts on great things they see in dataviz. All credit goes to Andy for the idea. I’m going to call my series “It’s the small things….”]

Beautiful Science of Data Visualization

Go see Carlos’ original tweet

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!

Someone else who does amazing sketchnotes is Catherine Madden.

MakeoverMonday: Satisfaction with Public Transport

Click to download my workbook from Tableau Public.
Click to download my workbook from Tableau Public.

This week’s Makeover features a simple, effective stacked chart from Rather than find multiple new stories in the dataset, I focussed only on the original story: satisfaction in 5 European cities.

Decision 1: if you show % satisfaction, you can get away with not showing % dissatisfaction since they’re almost binary. (that’s not entirely true: there is nuance in the differences between “very” and “rather” satisfied/unsatisfied) but I do think it’s valid.

Decision 2: bring in the delta for extra context. Berlin is not only the city with the highest satisfaction rate, it’s overtaken London in the last 3 years. That story is not visible in the original.

Decision 3: improve on the comet chart. Once I’d made the first two decisions, I figure it would be a good time to draw a comet chart. However, they are hard to read, as I’ve written about before. I think I’ve solved the problem by fading the trail. Do you agree?

Decision 4: set the x-axis to go from 0-100%. Berlin’s the highest satisfaction but it’s still below 50%. Setting the x-axis at 100% is intended to highlight the hidden levels of unsatisfaction.

Decision 5: Formatting. I didn’t see a reason to format it any differently to the FT’s original. I love their background colour! I did move the title to the blank space created by the long x-axis though.

The original chart

The original chart is great.

What do I like?

  1. It’s sorted by “Very satisfied” which is a good way to rank the cities
  2. The title shows the metric rather than an unsightly label on the x-axis. Jon Schwabish wrote a great post on this recently.
  3. Stacked bars let me easily compare to categories: the ones at each end. In this case, they are “Very satisfied” and “Not at all satisfied”. They’re the most important.

What might I improve?

  1. Stacked bars have an inherent problem in that you can’t easily compare the middle sections. Stephen Few sparked a significant debate on this recently which is worth your time.
  2. The ordering of the colour legend is confusing. I read it left-to-right, top-to-bottom. But it’s order top-to-bottom, left-to-right. I initially thought one colour was missing from the chart until I realised this.


MakeoverMonday: Global Peace Index



A quick one for me today. As I explored this data, what struck me was the stability of the most peaceful countries, compared to the volatility of the least peaceful countries. What I hope my chart emphasizes is the depth of the tragedy for Syria and South Sudan. All of the countries at the bottom of the chart are facing terrible situations, but the descent of Syria from a largely peaceful country to the worst in the world is awful.

Vision of Humanity, our source for the week, do a great job visualizing data. Their Global Peace Index is a readable report, with some excellent charts embedded in the flow of the stories they tell.


#MakeoverMonday got shortlisted for the Kantar Information Is Beautiful Awards

What an honour! #MakeoverMonday has been shortlisted for “Best DataViz Project” in the Kantar Information Is Beautiful Awards.


If you’ve enjoyed the project, please go vote for us.

Click this link and press the grey vote button (note that you can only click the button once per category, so choose wisely! subliminal messaging: choose MakeoverMonday! )

It’s really been an astonishing year and this project has blown me away.

It’s not about us. It’s about community.

Andy and I predicted it would be a small thing and nobody would care. But then 372 people got involved over 38 weeks. WTF? 54 makeovers a week? Amazing.

Some highlights

Go and look at the Pinterest board: look at the depth and variety of ways to visualize data. When people blog about the impact the project has had, you get the sense it’s really changing the way people work. (Neil Richards and Michael Mixon are two great examples)

Kids are getting involved. Children: enthused by data.

I’ve learnt a lot about how to criticize constructively.  We’ve had some great thoughts on whether things should be complicated or simple.

Who knew that China was the world’s biggest grower of peaches?

Or that a TEU is a measure used by the shipping industry?

Each of the 38 datasets has taught us something. Whether it’s serious or not, participants are learning about the world each week.

#MakeoverMonday live at Tableau Conference in London

We’ve done this live many times and the experience is amazing. You should do it too!

Comcast put MakeoverMonday as a prerequisite in their job descriptions! What? Amazing.

The highlights are long. Andy and I would like to thank everyone who’s been involved in such a rewarding project.

Sounds great! How do I vote? Click here.

#MakeoverMonday: Peaches


When Andy and I were discussing future topics, we were considering the Global Peace Index. I mistyped it as the Global Peach Index. “Wait a minute, that sounds fun. What if there is data on the peach industry?”

And here we are with data on global peach growth.

On the first exploration of the data, the massive domination of China pops out. Below is the percentage of peaches grown in China. >30% of all peaches in 2012.


“But China’s huge. And populous,” I though. And that led me to bring in population and area. Do that and you realise that while China’s clearly growing loadsa peaches, and has been increasing its growth in the last two decade, it’s Greece that’s the biggest relative to are and population.

All of which is a good way to say that in data analytics: think about the contextual implications of each measure in your database.

The original


This week’s source from FAOSTAT is kinda standard fare. Things I think could be improved:

  1. The colour bins have very specific boundaries. I’d rather see them fitting round numbers. This mapping system has to fit all FAOSTAT datasets, so I suspect there’s some automation going on here.
  2. The map has ocean depth and land cover detail. That’s too much detail. Why should I be interested in ocean depth when looking at peach production?
  3. The line chart updates when you select a country, which is nice, but I’d rather also see the title update, otherwise it’s not obvious if you did select anything. There is a country line legend right at the bottom, but I didn’t spot that.

Here’s the horizontal version of the makeover: