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?


Scribbles and lines: what does iteration look like?

Capital Punishment in the US

What’s the goal of analysing data? It’s about finding insight, and communicating that insight  in an optimal, engaging way.  

How do you reach that goal?

You don’t get there by first deciding which chart types you want and then adding data to them. Saying “I will start with a bar chart” and adding data to it  is the equivalent of trying to create a masterpiece with a paint-by-numbers set.

You do it by starting off with freeform exploration, improvisation, and shifting perspectives. We might try out 100 different views and keep only one. That single output will be powerful, but you’ll know it’s right because all the other views provide small contextual validations that you gained on the journey to the final output.

What does this look like? How about this squiggle?

squiggle narrow

The start is fast, messy and unpredictable. You’re failing fast and moving on. Over time you focus on the insights you’ve found and gradually hone in on the best articulation of the data.

What does the squiggle look like with real data? I got a chance to find out during MakeoverMonday, week 29, when we looked at data on executions in the USA. For a project being run by Tableau’s research team, I captured a screenshot of every view I made in Tableau (this was automated!). My analysis took 90 minutes. In that time, I built 302 different views of the data. The final dashboard is shown at the top of this post.

Here’s what the exploration looked like:

iteration 300ms

You can see all phases of the squiggle in the animation.

  • Rapid exploration at the start. During this phase I discovered the data had a similar shape to Simon Scarr’s Iraq’s Bloody Toll and decided that, based on that insight, I could base my final output on the design of that infographic
  • In the middle phase, there are periods of formatting the time chart interspersed with exploring afresh. I’m not restricted into doing one thing or another. As I play with the data, I get new ideas to go try out. I’m honing in on something, but still exploring.
  • Finally I spend a big chunk of time finessing the main chart. This is the phase when I’m trying to find the absolute best way to represent the data.

I created tables, bubbles, area charts, pie charts, sideways charts, ugly charts, and more. I threw most of them away. What the animation shows is what happens when an analyst is in the flow with their data.

I found this week’s MakeoverMonday fascinating. I have written many times before about exploration and iteration (e.g. here and here) but this GIF is the first time I’ve been able to truly see what exploration looks like. It’s the first time I’ve been able to validate that exploration can look messy from the outside but is actually hugely productive for the person doing it.


  1. The Squiggle is based on Damien Newman’s Design Squiggle. Newman applied the squiggle to design.
  2. The screen captures from Tableau were captured automatically. Only changes to the views themselves are captured. Titles, annotations, and laying out dashboards aren’t captured.

MakeoverMonday: The Next to die (capital punishment in the US)


Capital Punishment in the US

Anyone who follows my blog will immediately recognise my inspiration for this week’s makeover: Simon Scarr’s incredible Iraq’s Bloody Toll infographic from the South China Morning Post in 2011. I’ve written and spoken about this post many times in recent years.

The original source for this week’s makeover (“The Next to Die”) is a great project. It puts a new perspective on this topic, focusing not on the past but on the future. One thing it doesn’t emphasise, which I learnt by examining the dataset, is how the number of executions is dropping across the US.

Given I learnt that the number of executions is dropping, let’s go back to my makeover. I chose red bars and made them face down: evoking a smear of blood. But if I my biggest learning point was that numbers are going down, surely orienting the bars in a normal way (ie up) would make it clearer?

Well, here you go:

Capital Punishment pointing up

Regular readers will, of course, recognise this is what I also did with the Iraq’s Bloody Toll infographic. Here’s the Iraq’s Bloody Toll and my Capital Punishment story told in both ways.

side by side


The perils of bubbles

Another day, and another tragic event. The events in Nice horrified us all. One excellent response is to reinforce that, should this turn out to be a terrorist attack, the individual does not represent all Muslims. The number of terrorist-minded Muslims is tiny. We can make that point using data.

This afternoon I saw the following chart tweeted by Ian Bremmer, which makes this point:

The point being made is great, but look at those circles: if you put that ISIS circle inside the Muslim circle, it’s kinda big. So I took the data from that chart, and redrew it in Tableau. Turns out, the circles were the wrong size. Here’s what the size really look like, if you draw the circles so that their AREA represents the value:



Can you see the circles for Al Qaeda, ISIS, or the Taliban? They’re up there in the top right. Tiny, aren’t they?

The lesson here is that, if you’re going to use circles, size them according to the area. The people behind the original chart, MIIM Design, were trying to make a valid point, but the circle size misrepresented the sizes, which could cause confusion.

Note: I used the same numbers MIIM used in their chart, taking the upper estimate of each category. I have not done research to check the validity of these numbers. My goal is to make a point about circle sizes, not a political point about the size of different religious or terrorist groups.

Update: I replaced the original image with one with a new title. One commenter suggested, rightly, that I might have implied “Muslim” is a terrorist organisation. That absolutely was not my intention or my belief.


MakeoverMonday: Orlando shooting

Download the v10 workbook here
Download the v10 workbook here

As shooting after shooting occurs in the USA, this week we’re looking at politicians’ responses to these shootings. Specifically, the first response statements of members of congress following the tragic Orlando shooting a few weeks ago.

I went through quite the process this week.


I started with a side-by-side bar to examine the data. I don’t like side-by-side bars, so changed to single row with two circles. That made it too hard to see the difference so I joined them as a dumbbell. And then I thought that the GAP between the parties was most interesting.

For a long time my makeover was just the gap:

Dashboard 3

… but this was unsatisfying: the gap is interesting, but what does a 40% gap represent? 90%-50% or 40%-0%? Knowing whether 0% or 50% of a party mentioned a topic is more important than just the gap.

And so I tried the pies. I quite like them. I could have just shown the number, or a bar, or a sized circle, but I thought I’d try a pie. The problem with pie and difference bars: you still only show percentages, not the actual values. However, if you DO show numbers via circle size, you still have a problem: there aren’t equal numbers of Democrats and Republicans in Congress so the numbers don’t relate directly.

percent diff

In the circle chart above, looks like way more GOP condemned terrorism than the DEMs. It’s actually a 20% difference. Maybe a bar would work? This is one where you can go round in circles balancing most efficient visual with the most engaging. I went round in circles before running out of time and sticking with the pie. It’s not perfect!

Pie, circle, bar?
Pie, circle, bar?

The original chart this week is unconventional but does convey the point well.

What’s unclear is just how the slices are sized. By area? Radius? Diameter? The choice of semi-circle is abritrary. Despite that, it is easy to see the difference, even if accuracy is sacrificed.


MakeoverMonday: What’s the best US state to raise a child in?


50 states

This week we’re looking at quality-of-life metrics in the USA. The original article had a series of 5 maps showing how each state is ranked according to various measures.

This is an interesting challenge: how can you compare ranks among multiple metrics for so many states? My normal approach to MakeoverMonday is to make static charts, but this is one dataset where the only way to provide insight is to use interactivity.

Whenever you hover over a bar, or the map, every area of the dashboard highlights to show the rank of that state in each category. For example, it makes it easy to see that while New Hampshire is ranked number 1 for Family and Community, it’s weak in Health:

Best states to raise a child New Hampshire

You can download the workbook here (it’s built in Tableau v10 beta 4)


MakeoverMonday: MakeoverMonday!

Cohort Analysis
Download the v10 workbook here.

We’re at halfway. Over 200 of you have posted more than 1,000 makeovers over 25 weeks. This is simply amazing.

Where else could you find a growing source of charts and data to play with?

Where else could you find so many different ways of telling data stories?

When Andy and I started this, we thought it’d just be us goofing around. But it’s you, the community, who have made this something more special than we could have imagined.

Thank you!

This week we’re making over MakeoverMonday data. I wanted to do some cohort analysis (check out a great post on using LOD calcs for this here). It turns out that those of you who’ve done the first 5 weeks of Makeovers contribute about 50% of all makeovers each week. Below is a percent of total view of the chart above:

Cohort Analysis (%)

Keep it up, gang! I’m loving seeing all the incredible ideas you come up with each week.

quick snap



MakeoverMonday: Am I safe in Japan?

I’m very excited to be in Tokyo this week. I’ll be presenting the Tableau 10.0 roadshow and at a bunch of partner and customer events, too. I’m very thankful for this opportunity!

The makeover

This week, Andy K found a chart showing reported thefts in Japan. The chart we’re focusing on shows the number of reported thefts in Japan in 2012. I thought I’d make it over to make it personal. Personal to me. Could I use this to prove the reputation Japan has of being a safe country to visit?

Will I be safe in Japan dash
Download the workbook here. (Tableau v10)

I didn’t start off wanting to ask that question. My original starting point was to draw a straightforward treemap. I haven’t seen too many of these in MakeoverMonday so I thought they deserved some attention.

Treemap: only ok
Treemap: only ok

The treemap was ok, but it didn’t amaze me, and I didn’t feel that I’d really hit on anything interesting for MakeoverMonday. All I’d done was take sectors of a circle and make them sub-rectangles of a bigger one.

As I interacted with the data, though, I realized I could look for patterns relevant to me. This week is my first visit to Japan. As the original article describes, Japan has a reputation for being safe. “Well then,” I thought, “which of these crimes could I fall prey to and are they common?”

That led to my makeover and a personal story to prove the relative safety of Japan, using available data. Given there are so few crimes, it’s fair to say that this data supports the reputation Japan has of being a safe place to visit.

Iterations and alternatives

Do I love this treemap? Not especially: in fact, with this dataset, I think a pie makes it easier to see the proportional to whole relationship than a treemap. Look at the figure below. In which chart is it easier to see that Vehicle Theft accounts for about 30% of reported theft. [Note: the previous statement comes with the normal caveats about pies and their problems. I know the problems with pies, you don’t have to tell me them; I’m just describing my thought process as I explored the data and built different views.]

Pie or Treemap?
Pie or Treemap?

If I was going for efficiency in the makeover, I’d probably have chosen a stacked bar or even a normal bar chart. These allow for the easiest lookup of data. Here they are below.

stacked bar bar

The original chart

Here’s the original chart and my thoughts on it:

What I liked

  1. Everything is labelled, so I can lookup any value I want
  2. There’s a total in the middle so I can the proportions and relate it to the entire number of reported thefts
  3. The labels are aligned making them easier to lookup than otherwise

What I didn’t like

  1. It’s a sunburst chart. There’s a certain pleasure in looking around and following shapes from the centre outwards, but it’s so slow and inefficient. A normal bar chart gets the job done quicker
  2. The inner label shows the actual total number of reported thefts, but the outer numbers show percentages. That’s not made clear.
  3. The outer level of the sunburst appears to be randomly sorted. It could have been in descending or alphabetical order.


MakeoverMonday: Women in the workplace

This week MakeoverMonday is LIVE at Tableau Conference on Tour. Check out the hastags #makeovermonday and #data16 during Monday to follow things live.

Women in the workplace

For my makeover this week, I wanted to simplift the message. The differences between 2012 and 2015 weren’t that great. There are more women at each level, but the trends themselves haven’t changed. I decided to remove 2012 from my data to focus more clearly on the Pipeline story.

I liked the quote in the first paragraph of the original so lifted that for the title.

In our makeover about women in legislature, I extended the y-axis to 100% to emphasise the distance to parity with men. In this case, I decided to end the y-axis at 50%. To make it clear that the top of the chart is 50% I made the reference line stand out, and put the title beneath it. Did that succeed? Did you see the reference line?

The original

Well, it’s a pipeline. Of sorts.

The original chart wasn’t a great one this week.

What I liked:

  • There’s a table, so I can lookup the numbers
  • The colour scheme is very easy to distinguish
  • They attempted to use a visual metaphor for a pipe

What could have been improved:

  • The mix of line chart and pipeline renders the chart pretty meaningless: it’s not possible to see what’s actually being shown in the chart
  • The designers appear to have drawn a straight line in the chart, but the data doesn’t quite drop the way it’s shown.

MakeoverMonday: Facebook’s Energy Footprint

My makeover. Click here to download the workbook (requires Tableau v10)

A first for MakeoverMonday: I ended up pretty much remaking the original chart, with only small tweaks. Once I’d locked onto the story I wanted to tell, I couldn’t escape the fact that Facebook’s original version of this chart was pretty much just right. In fact, it’s possible that I’ve complicated the message with my version.

How did I get to my version?

The original.
The original.

I thought Facebook’s whole report was fascinating. I learnt a lot from this graphical report.

As I explored the data, and cross-reference the report, it all seemed to hone in on the amount of renewable energy being used. The power usage itself is interesting, but the ambition for Facebook is to get the CaRE up to 50% by 2018 (and ultimately 100%).

Too sparse
Too sparse

I tried to draw a slope chart first, but it looked too sparse. Also, it hid what I thought was some important information – the volatility of CaRE:

Clean and renewable is highly volatile
Clean and renewable is highly volatile

I wanted to pursue this volatilty because it’s hard to say there’s a long-term trend upwards for CaRE when there was such a big trough in 2013. Unfortunately, I couldn’t find that information in the report.

Without the information on the volatility, I figured I’d accept Facebook’s word and focus on Facebook’s hitting the 25% CaRE by 2015 goal. As I drew different versions, it seemed that only a line chart or an area chart with CaRE along the baseline made the point. The other energy types are secondary information: as long as CaRE is going up, I don’t really care too much what’s happening to coal and nuclear.

What do I like about the original?
  1. Annotations on the marks explain the data
  2. Headline on the left summarises the point being made
  3. Forecast line has a different format
What don’t I like about the original?
  1. The x-axis year labels aren’t horizontal, and they don’t align very well to the marks themselves
  2. The y-axis % scale only goes up to 50%. On the one hand, this is fine, because it fits the range of the data. On the other hand, 35% means that 65% of data is still not renewable.
My changes


  • I used an area chart, with faded colours for all but CaRE to add context to the main story about CaRE usage
  • This choice also forced the y-axis to go from 0-100%. Now you can see that while goals are being hit, companies with huge data centres still have a long way to go.
  • I added a reference line for the 2015. This helps imply that the goal is continuous. The goal doesn’t stop in 2015.