MakeoverMonday: Horizontal History

Chart of Biography
Interactive, downloadable version here

Today’s makeover sees me completing an ambition of 5 years: remake Joseph Priestley’s Chart of Biography in Tableau. Finally, all of my 5 Most Influential Vizzes have been remade in Tableau.

Here’s the source chart for this week’s Makeover:

See the others at Why Ask Why

It’s a horizontal history from the excellent site Why Ask Why. It’s a cool data experiment and exploration. The article inspired Yura Bagdanov to do a horizontal version. Of course, when I read the article, I saw only the Chart of Biography, what I think is the most influential chart of all time:

Go read more on Wikipedia.

Priestley’s chart was the first to condense time onto an x-axis which fit on a single page (Dubourg did something similar earlier, but his chart was 54ft long!). It’s also the first Gantt chart. And it directly influenced William Playfair as he created his statistical line charts. Boom! Check the blog tomorrow as my next post is all about Priestley’s own analysis of his chart.

This dataset doesn’t have the same names as Priestley’s, but it’s the same type of data: thousands of famous people with details of their life and death.

Making the chart: the 1765 version

Priestley created a Gantt chart. Could I do the same in Tableau? Well yes, but all my early efforts didn’t really work out:

Chart of biggraphy true to original
Too much detail in the Gantt

The problem was there’s just too many names! I used jittering to randomly distribute the names in each pane but I wasn’t happy with the output. Time to rethink the view with a 2016 perspective.

Making the chart: A New Chart of History

I did have a go of recreating A New Chart of History too, but it only highlighted the inaccuracy of the data. 50% of famous people born in 1950-2000 came from North America? Not sure about that.

New Chart of History

Making the chart: the 2016 version

Interactivity gives us lots of options!

Tooltips and highlighting

Priestley labelled every single bar. Can you imagine how hard and tedious that must have been? All I did was make a nice tooltip!


You can also see in the above image that the country is highlighted. Another advantage of modern interactive tools!


Just the women in this view
Just the women in this view

I also get the advantage of filtering. Above is the view with only the females in the dataset shown.

The view above also highlights the problem with the Pantheon project: it’s incomplete. Only 6 famous female explorers? 5 business women?

I am very grateful to the London Viz Club for getting the data from the Pantheon project – that was a cool little Alteryx task. I’ve waited 5 years for a dataset like this: today is a happy day!303

MakeoverMonday: Militarization of the Middle East

Oh gang, I apologise but this is a super-brief Makeover this week. Work and life have multiple demands this week. Given the time squeeze, I gave myself 15 minutes to make a viz, with the rest of the time spent on this blog post.

15 minutes? What can you possibly focus on?

With only 15 minutes, I’m clearly not going to go deep into the data (even though it looks like it has some amazing detail).

0-5 minutes

Instead, I looked for one point being made in the article.

This caught my eye
This caught my eye

The flag section caught my eye. Firstly because it was the hardest part to interpret. So many flags and arrows and words and icons. What does it all mean? Once I deciphered the meaning, it seemed pretty interesting: 3 Middle East nations are importing way more than previously.

That was interesting: is it called out in the article? It sure is: they claim that these three countries are arming themselves in response to unrest in Syria. I find this horrible and fascinating: military hardware companies will be rubbing their hands with glee at the conflicts around the world.

Could I remake this story using the data?

5-11 minutes: building the viz

I’d spent a few minutes digesting the story, so needed to see what the dataset revealed. If you only have a matter of minutes, and want to look at how a measure changes over time, go for a line chart. There’s no time for mucking around with different views.

I filtered out the countries, and there you go: Qatar, Saudi Arabia and UAE all going up.

11-15 minutes: formatting

How do you make a simple makeover look really fancy? Choose an unusual font and background colour. Instant respect from us all! Well, there’s no time for that today. All I could manage was a quick switch to Smooth theme and writing a nice title. For simple charts like this, I’ll always try to ask a question, giving the viewer the information they need to query the chart itself.

15 minutes and I’m done.

How does a 15 minute makeover feel?

The main feeling is of fraud. I didn’t do much detailed checking of other countries. I don’t feel like I have really tested the hypothesis that “Middle Eastern Countries are spending more because of Syria.” I feel like I’ve just accepted the point made by the journalist and made a chart which, kind of, supports that opinion.

I feel like I’ve used the data to support the story, rather than use the data to find the story.



MakeoverMonday: Global Warming is Spiralling Out of Control

[Note 1: Yesterday I had a classic MakeoverMonday experience. I wrote this post, and was ready to hit publish. I then realised I just needed to tweak one of my images. I went back to Tableau, had a brainwave, and ended up with a completely new idea. I NEVER would have come up with that idea had I not been able to drag drop and experiment so readily. I chose to keep this post for Tuesday]

[Note 2: The CORRECT baseline is 1961-1990. The charts in this post have incorrect titles. Download the workbook to see correct versions]

The world is getting hotter indeed. The data comes from the UK MetOffice’s HADCrut4 data: a global, gridded dataset of surface anomaly temperatures.

The science behind the dataset is complex, but the data’s straightforward: the measure is going up over time. How should you best show an upward trend?

This week, three ideas came to mind before I explored the data. I implemented each one.

1. Straight line (with politics)

Which presidential candidate do you trust on climate change?

You can’t beat a trend line. It’s visually the most straightforward and effective for displaying an upward trend. I chose to emphasise the moving average (red) with the actual anomalies in grey in the background.

The rising line chart conveniently leaves white space into which you can insert objects to further make your point. In this case I found two representative tweets from the likely US presidential candidates.

2. Bloomberg-inspired animation

global temps
This is a GIF – click the image to see the animation if it doesn’t begin

Bloomberg did an amazing visualisation with this data last year. Here was my excuse to recreate it. I think this is an especially good way to show the data because the animation brings drama to numbers. As the hottest year creeps ever upwards you have a sense of dread. “Wow, 1995 was hot. It can’t get hotter, can it? Oh. It did, 1998. Ouch. And again. And again. Yikes.”

This week’s chart was essentially exactly the same idea, spiralised. Personally, I think the radial display makes it much harder to see the extremes creeping ever higher.

3. The highlight table

Click image to see a bigger, hi-res version

I love a highlight table. This one lets you look up each month, should you wish to, but shows, right at the top, just how common the broken records are happening. It was fact-checking the rank calculation which led me to the idea of histograms for my actual MakeoverMonday, published yesterday.

I also quite like tall and thin, but in this case, I think there’s just too much detail. We’re really making the point that the most recent months are super-hot. The highlight table takes a lot of vertical space to make that point.

top 10 labels
Detail from the top of the chart: the most important stuff.

Click here to see the final Makeover post.

MakeoverMonday: Global temperature is spiralling out of control

Click the image to see a larger version. 10.0 only this week – click here to download a copy (when it asks you to locate the extract, point it to this one)

[Today I had a classic MakeoverMonday experience. I wrote my original post, and was ready to hit publish. I then realised I just needed to check my calculations were correct. I went back to Tableau. While checking the data, I came upon a completely new idea. I NEVER would have come up with that idea had I not been able to drag, drop and experiment so readily. I will publish the original post tomorrow.]

The world is getting hotter. This week’s data comes from the UK MetOffice’s HADCrut4 data: a global, gridded dataset of surface anomaly temperatures. Note: the baseline for HADCrut4 is 1961-1990, not 1850-1900 as stated in the original article. See the MetOffice page for more details. 

The science behind the dataset is complex, but the data’s straightforward: the measure is going up over time. How should you best show an upward trend? I had three ideas, which I implemented, and will publish tomorrow.

A final check of the data, though, led me to the idea of a histogram.

Are histograms good charts?

I really like my chart this week. It shows just how much the 21st century has been above average in an unusual way. The challenge with histograms though is that they aren’t as immediately understandable as a line chart. You’ll see in tomorrow’s posts that I was initially riffing on line charts. If you’re sharing your findings with people who don’t usually see many charts, or have much time, you might want to show a simpler chart. Or you could trust that your audience is in fact intelligent and go with this design.


I built a histogram initially just to check whether one of my calculations was correct. I immediately realised it was an interesting way of showing the data. But which chart shape and at what level of granularity?

histogram bar with month detail orange

My first version showed every month as a separate mark (because that’s what I was trying to validate). However, it’s just too much detail and nobody really wants to know the specific value for a particular month in the 1990s. It’s the trend that’s important.

histrogram area orange

I tried an area chart too. I like this as it shows the waves of the different time periods. However, it’s just one level of complexity too far. A histogram’s challenging enough without colouring it by groups and using area instead of bars.

histogram bars1 orange

Bars it was! My final step was to tell the story. I turned to colour here. My story is about the years since 2000, so I changed the palette to emphasise those years. Red for the recent colours, greys for everything else:colours

Unstacked area?

Finally, does an unstacked area work best of all? I think it might…

Helping the user understand a histogram

Here’s my biggest challenge with histograms: how do you help a reader understand it in as short a time as possible?

Custom labelling to aid the user
Custom labeling to aid the user
  • I created custom axis labeling as shown above
  • I annotated one of the marks
  • I used colour in the title to further explain what each mark showed

Did that work? How easy was it for you to interpret the chart?

The original chart

With 12k retweets at time of writing, people clearly love spirally climate data!

What I like
  • If you watch the animation, it clearly expands outwards
  • The colours pop out (although they seem arbitrary)
  • Spirals fit into a small space, like a tweet
What I would improve


This is a straightforward timeline and the radial nature simply does not show the growth over time. Bloomberg did a much more exciting animated version. A simple trendline shows growth better, too, in my opinion. Growth in a sprial is only visible by a vague awareness of an expanding circumference. Spikes in months or years are lost in the noise and confusion of the sprial.

But…. Twelve Thousand Retweets? For all the problems of spirals, people engage with them. Is it better to get people thinking about the data, or be a chart purist? Bloomberg, when it tweeted about it’s story with a map, got only 192 retweets. From an account with THREE MILLION FOLLOWERS.

Conclusion? Spirals aren’t the “best” way to show the data, but they make people look at it.

MakeoverMonday: American women work way more than their European counterparts. [Really?]

TL;DR - just look at this chart. More details below.
TL;DR – just look at this chart. More details below.

[This week you have multiple ways to see my Makeover. It’s available here as a Tableau Story. Or read below for the story rendered as a post. Notes on the original chart are at the end, too]

Go see this as a Story in Tableau
Go see this as a Story in Tableau

The Makeover

Business Insider make a bold claim in their headline

Click here to see the article

Actually, of the 21 EU countries in the dataset, women work more than the Americans in 9 of them.


The Netherlands is an interesting outlier


What is about the Dutch?

Click here for to see the articleCheck out these great articles from The Economist and Slate on why Dutch women don’t work so much as other nations.

Finally, should we trust Labour Force Statistics which involve gender?

more or less2

Check out this week’s fantastic episode of More or Less for more information.

The Analysis

You can download my workbook here. It’s using v10 of Tableau. (in beta at time of writing)

As you’ve gathered, this week my makeover was inspired by questioning the orignal chart. The chart itself is ok as far as stacked bar charts go. I question the boldness of the claim, though.

First of all, lots of the EU countries have higher levels of work than the US.

Secondly, as More or Less discussed this week, there are many reasons why gender data in employment statistics might be incorrect. Or, if not incorrect, the surveys are bias against female employees. For example, surveys often ask about “primary” employment. This ignores second jobs, which more women have than men. Uganda changed its surveys and female employment numbers went up by hundreds of thousands!

What did I like about the original?

  • A stacked bar is pretty clear.
  • I can easily find the categories on the legend and compare countries

What didn’t I like?

  • Everything’s got the same intensity. I’d have softened the borders, axis lines and labels, so that the data is more clear
  • It’s a good job I know my country abbreviations. GER, ITA? Some people might not know what they mean. They may think OECD is a country of its own.
  • There are too many tick marks on the axis. I don’t need all that information.
  • The title, “Female hours worked relatively low” doesn’t make much sense. I don’t mind using abbreviated language in titles, but this one seems to have gone too far.


MakeoverMonday: US Tuition Fees

Hi gang. Unfortunately, this week has seen time get away from me. This week in the UK is a Bank Holiday. You don’t need to cry me a river, but after three days away, a long drive home, and fatigue setting in, I’ve not had time to do a finished Makeover today. I’ve also spent a good long time catching up on this week’s #MakeoverMonday tweets – what an amazing effort everyone’s put in this week!

I did try out some ideas.

I tried a table
I tried a table

I spend ages trying to create a table using panel chart techniques, but I couldn’t find a way to make it right. I submit the above for this week’s Makeover, even though it simply did not work. There are some other ideas below. Or go check out my workbook.

This week’s original (click here)

This week’s original had a mix of good and bad.

The good:

  • I like the idea of mixing map and bar chart. You get the geography and a precise way to compare states.
  • The bar chart is sorted, so you can easily find the biggest and the smallest tuition fees.
  • The map has Hawaii and Alaska included, inside a compact space.

What I’d improve:

  • It’s crying out for interactivity! I want to hover over this and see some details, and link the map to the bar.
  • The colour scheme is really saturated. It’s a bit too overwhelming, as if the whole thing HAS BEEN DESIGNED IN CAPS LOCK. I don’t know where to look first.
  • I do not like vertically oriented labels on the bar chart. If you want to label the bars, orient the bar chart vertically, too, and put it on the right-hand side of the map, not below it.

My other approaches included parameter driven highlighting, a butt-ugly small multiple state map and a small multiple area chart. They all had potential, but I’m out of time this week.

Traditional bar and percent change


A table with a map background


MakeoverMonday: Trafficking across the world

Victims of 21 century slave trade
Click here to view interactive and downloadable version.

A quick post this week!

My makeover focused only on the most recent year, 2015. I did look at the trends, but didn’t find anything particularly of note to add to or support the story. I wanted to keep things simple. I played around with versions without maps, but this data set seems challenging to show in other formats. Any time I took the map out, I found myself wanting to know the detail of each mark: WHAT country is it and WHERE is it?

This week's original view
This week’s original view

This week’s original chart came from CNBC.

Here’s what I like:

  • I happen to know this one was specifically designed for mobile. CNBC had a very specific brief that required the dashboard to render correctly on mobile devices. With that in mind, the dash is quite small and simple.
  • It has a nice “About” tooltip in the top left, where I’m likely to see it.
  • The lookup table does allow me to see individual numbers, should I wish to find them.

What I’d improve:

  • The colour palette seems arbitrary to me. I think there are nicer palettes available which could portray the categories more clearly
  • The circles in the lookup table make it hard to read the numbers.
  • I don’t like the vertically oriented labels in the upper table. There is space enough to have them horizontal

Here are a couple of ideas I rejected:

Revisiting Unit Charts seemed like a good idea.
Revisiting Unit Charts seemed like a good idea.

The timelines didn’t reveal too much and the dots left me feeling too curious about the actual country each dot represented. Hence I went back to a map.

I tried a stacked bar alongside a list.
I tried a stacked bar alongside a list.

I had a crazy idea that listing out all the countries next to a stacked bar would work. It didn’t. Or did it? Could you see that idea going anywhere?



MakeoverMonday: Women in US Legislature

Click here for the interactive version.
Click here for the interactive version.

This week my aim is to emphasize where the data isn’t. I made the y-axis scale show the full range from 0% to 100% female representation. Why? In an ideal world, states should have approximately 50% female representation.

When I first explored the data, Tableau automatically tops out the y-axis just above the highest percentage state. This is reasonable but it means you don’t immediately see how low the representation is in some states. In the image below, you can see bunch of states are high on the y-axis. You have to stop yourself and wonder about truncating the UPPER end of the y-axis when showing percentages. Colorado and Vermont are the closest to 50% so well done them. But it’s still NOT 50% female representation.

The states don't look so bad when you don't extend the axis to 100%
The states don’t look so bad when you don’t extend the axis to 100%

I decided to ignore the values relating to gender split in the population: all the states are close enough to 50% to make any differences mere noise.

The original
The original chart for this week’s Makeover.

As with many other makeovers, this week’s original chart doesn’t have too much wrong with it. I liked the following:

  • They binned the states into just 4 groups
  • The tooltips for each state add good information
  • The title and font keep things simple and clear
  • A map helps for quick lookup if the viewer is looking for a specific state

Things I would have changed:

  • Was this data best shown as a map? If the story is about underrepresentation, then show that, rather than just geographic information. Bar charts, or distributions could show that story more powerfully.
  • The colour scheme is, to me, a little muddy. High/low doesn’t pop out. The grey is especially unclear. Without looking at the key, would you be able to tell if grey was good, bad or middling? I wouldn’t know.


MakeoverMonday: From Millions to Billions

Click here for an interactive, downloadable version
Click here for an interactive, downloadable version

I’m late to the game this week! Normally I get the Makeover done on Sunday or first thing on Monday. This week I had to wait until the end of the day.

The original was great:

  • Appealing colour scheme
  • Mobile friendly
  • Great tooltips
  • Lovely use of Gantt bar
  • Customisable sorting

This week I decided to see what everyone else did before I did my own, to force me to come up with a new perspective.

Most people seem to have focussed on visualising the ages that these entrepreneurs made their millions/billions, reusing the same main fields as the original.

Since that’s what most others do, how could I show the same story in a different way?

I decided to focus on the other measures: the gap between millionaire and billionaire and their net worth. It’s not quite the same story, but I needed something original!

Did I succeed? I’m not sure.

As I write and reflect on this one, I don’t think my story is as compelling as the original. Not only that, I don’t think the scatterplot makes it clear! I do think the difference between Alan Sugar and Bill Gates is pretty clear, but as a scatterplot it’s just too hard to decipher for a story that’s better told in the manner of the original.

Trends in Adult BMI (a Wednesday makeover looking at colour)

My makeover - new colours and ordering.
My makeover – new colours and ordering. Download it from here.

Ramon Martinez produces excellent work. His latest is a small multiple masterpiece showing the growth of BMI over 40 yrs. It’s beautiful, impactful, full of insight and has rightly been celebrated by people on Twitter.

I do propose four changes to help focus on what I think the key story is: the growing obesity crisis.

  1. Change the colour scheme. I found it a little hard to decode the colour scheme. Which colour is good and which is bad? I’ve changed to a red/grey scheme: now red is bad and pops out more clearly. My palette choice isn’t perfect as it’s not a truly continuous scale.
  2. Change the order of the BMI categories. I’ve put the highest levels of BMI at the bottom, so it’s easier for us to see the growth of obesity.
  3. Change the default view to be sorted showing the worst countries first.
  4. Go tall! [I didn’t implement this one] I would show all the countries on the dashboard without needing the scrollbar. One thing MakeoverMonday is revealing to me is just how effective very tall charts are. I’m loving seeing these and they work very well on mobile devices.

What do you think? Would you have kept the original? What would you have changed? One thing I wonder about is whether the original colour scheme, harder as it was to decode, was more appealing because of its novelty and brightness? If you spent time looking at Ramon’s, did the colour scheme draw you in? Would my red/grey one have piqued your interest?

Ramon's original on the left, and mine on the right. I changed colour and order of categories in order to focus the eye on the BMI problems.
Ramon’s original on the left, and mine on the right. I changed colour and order of categories in order to focus the eye on the BMI problems.