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.


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:


MakeoverMonday: World’s biggest data breaches

This week we’re tackling one of the interactives from Information Is Beautiful.

I struggled with this week’s makeover. I couldn’t find any great way to retell the story to the level of detail of the original. In the end I decided to exclude detail and focus on just the growth the hacking and what that means for me and you. Personally, I am a huge fan of Lastpass and recommend everyone to use it or an equivalent.

I spent a long time trying to do a remake but in a “better” way than using circles.

I tried a stacked bar:


The problem? There’s less detail than in the original and it’s not engaging.

I tried a treemap bar (which you can interact with here):


The problem? A treemap is a part-to-whole, and this dataset is only selected breaches. I do like this chart, but because the tree implies part-to-whole it’s not acceptable.

In the end, after more time than I had available to spend on the makeover this week, I figured I’d have to find a simpler, different story and focus on hacks alone:


My conclusion? The original is a very good way to prioritise access to all the data over ease and accuracy of comparing each breach.


What I like

  • It’s engaging. That makes me want to explore it.
  • There is detail, in the form of a short sentence to add context, when you click on a circle.
  • It works well on mobile (the vertical timeline is becoming more prevalent as we move to mobile).
  • I like the interactivity: switching bubble size and color for different categories reveals different insights.

What I dislike

  • It isn’t easy to accurately compare the difference in size of different circles. If the prime purpose is to show differences accurately, then you’d need to use bars. Since that wasn’t the prime purpose here, this isn’t too big a problem.
  • There’s a lot of overlapped marks. A border appears around each circle as you move your mouse over it, making this less of a problem. Making the marks transparent is another possible solution.
  • You can choose to colour the bubble by year, but the “Interesting story” color overrides that, confusingly.


MakeoverMonday: global shipping companies

Pareto: do you understand what this is showing?

This week’s original looked so simple, I thought it would take just a few moments to whip up a bar chart. Then I figured that was too obvious. And then I got lost in a fascinating voyage of discovery finding out about the global shipping industry. I never knew capacity was measured in TEU (ie a container). And who can resist looking at images of enormous container ships? Maybe you can, but not me.

Reading about the industry soon pointed out just how dominant those top 20 companies are. Which led me to the Pareto. I’d enjoyed Andy K’s tip on Paretos last week, so they’re on my mind. I don’t actually think we really need a pareto here. In this case, the more compelling view is  the basic bar chart. A simple annotation saying that the top 20 companies account for 90% is enough. It’s easier to read that information than understand it from a pareto.

Or is the bar easier and more compelling?

Download my workbook here.


#MakeoverMonday: Alan Rickman

Alan Rickman

Just a quick one this week. Alan Rickman’s career was lauded, justifiably, when he died earlier this year. However, I hadn’t realised until tackling this week’s makeover just how much it had been dominated by Harry Potter.

I only have time for a very quick post this week. One thing I did do was to orient the bar chart the opposite way to normal (header on right, bars pointing to the left). Why? Because the photo of Severus was facing to the right – I wanted the makeover to look like Severus was looking at the chart itself.

#MakeoverMonday: Tax Havens

tax havens

$2.1 trillion dollars? We are used to hearing monetary values like that, but what does 2.1 trillion dollars look like? It’s such a huge number, I thought it would be good to relate it to other types of expenditure.

It is astonishing that the offshore money is 2/3 the size of the entire US federal budget.

This weeks source was in need of a makeover. Pie charts aren’t a great way to show this data (read Stephen Few’s classic “Save the pies for dessert” for a great explanation). Making a pie with so many slices renders everything virtually unreadable.