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: 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.

MakeoverMonday: Malaria

We’re hoping to bring attention to work helping fight malaria. Go check out how you can help #VisualizeNoMalaria here:

Malaria deaths in Africa


I found myself doing something simple and possibly too experimental. I looked at which countries are seeing growth and decline in Malaria deaths. It turns out that, for the countries with good data, there’s a balance of impriving and worsening countries. Overall, the number of deaths has been pretty static since 2001. Not great news.

My original layout was horizontal but I couldn’t get the country labels to align nicely or fit, so I went for the vertical layout, even though “you should never do vertical time series.” Well, I just did. Below is the horizontal version:

Malaria deaths in Africa (horizontal)

MakeoverMonday: triple whammy of balls, medals and lipstick

I’m back from vacation and in catch up mode, so here is 3 weeks of MakeoverMonday with super-fast summaries of each one. A lesson from catching up like this is that time pressure really helps keeping things simple and thinking about the simplest way to communicate a simple message. It forces you to condense your approach to the pure display of the data itself.

Week 31

Dashboard 1

The photo of Vinnie Jones and Paul Gascoigne is utterly fantastic. I realise that the mentions in this dataset relate to 2016 basketball, but, well, I couldn’t resist using the photo.

Week 32


I tried for a while to show the whole dataset in an interesting way, but I couldn’t find a way to make it feel interesting. I stepped back and noticed the 1920 spike in overall medals. A quick read on Wikipedia revealed the brilliant fact that 1920 Summer Olympics included a week of Winter Sports. Bonus fact!

Week 33

The UK Beauty Industry 2013-2014

Short on time, I went for a super simple nested bar chart. Looking up the information and comparing values is easy, which cannot be said of the original. The thing is, even if I had all the time in the world, I don’t think you could create a more effective display of the information than the nested bar chart.

MakeoverMonday: The Bermuda Population


I am going SUPER-SIMPLE with MakeoverMonday for the foreseeable future! There are 2 reasons for this. Firstly, I’m finishing my book and also going on family vacation.

More importantly, it’s a response to comments in Andy K’s post about how many people are downloading the Makeover datasets but not publishing vizzes.

This one sums them up:


For people who feel intimidated about posting their work: I am sorry. Our intention with MakeoverMonday is to encourage everyone to share their work, in order to help them elevate themselves, whatever their skill level. I want to see people share the vizzes that DO take only an hour. Or less. This week’s took me 20 minutes. It’s a straightforward area chart with the colour scheme copied from the original.

If someone wants to spend hours on their Makeovers then that’s FANTASTIC. If people want to take the datasets and spend hours crafting amazing pieces of work, then I applaud that. I love seeing the amazing styles and approaches people take.

One look at the MakeoverMonday Pinterest board and it does seem to an arms race towards who can do the most elaborate work. Pam G said she stopped doing MakeoverMonday because “so many posts were all about the cool graphics being used and not so much about the charts themselves.”

So with that, here’s my plan for the next few months: I’m going to keep it simple and focus on the charts themselves. If you want to join me, then please do! I recommend you read Chris Love’s Keep It Simple post for more reinforcement of this idea.

Why did I choose an area chart this week?

The original viz was titled “Bermuda Population Growth“. I like unit charts, but this one made it hard to compare each year being shown. Taking the title as my cue, I wanted to make it easier to see the growth. I decided seeing total population was more important than the actual Female/Male breakdown. Therefore I chose an area chart: it shows the total at the expense of accuracy of the gender breakdown.

I’ll be keeping it simple for the next few weeks. Who else wants to join me?