Who are my data visualisation inspirations?

Who are my data visualisation inspirations?

I wanted to write a post about my inspirations in data visualisation. First of all, I feel a little bit uncomfortable citing inspirations, not because I feel uncomfortable expressing praise to others, but because I feel that as someone so inexperienced in the field, you might think that I am eliciting comparison. Please don’t think I’m like the guy who can sing and play the piano a bit and goes on TV wanting to be the next Elton John. In the same way, I can’t claim to be in the same league as most of my inspirations. If I’m honest, I didn’t know I was inspired by quite a few of the people on this list until I stopped and thought about it. But if you can get past that, then here’s my list.

1. Hans Rosling

Last week saw the sad passing of Hans Rosling. It feels a bit like jumping on a bandwagon to pay tribute to him at this point, since many words have been written in tribute. Hans Rosling is known to many in the data visualisation field for his work on gapminder.com, his TED talk videos, and his BBC FOUR appearances presenting The Joy of Stats. I’ll almost take it for granted that anyone who has found this page has done so because they have an interest in data visualisation, so will be aware of some or all of these things. If not, go and seek them out!

I can think of at least three things that point to the influence of Hans Rosling on my work. First of all – this animated viz, on the Global Peach Index.

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Above is the start of the visualisation, but you can click through to a hosted version which shows the animation of peach production in each year from 1062 to 2012. Bizarrely, this started as a bit of fun on Andy Kriebel and Andy Cotgreave’s Makeover Monday project which started as a result of a typo on the Global Peace Index. But that notwithstanding, I produced this animated visualisation. It’s crude and quite simple, but I got feedback that it was “very Hans Rosling”. I felt enormous pride at this comparison.

The second example was from last week’s project (below), the last viz I created before his death. Like Rosling, I love the scatterplot. It allows granular display, with x, y, colour and size available to compare four different dimensions. Five, if you include time as Rosling often did. I used this, I hope, to good effect in my Brexit visualisation here:

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The dataset available last week contained data on many millions of Chicago taxi trips. I can’t tell you how many more stunning and successful visualisations were made – see Exasol’s blog “Let them have ALL the data” for just some of the best. But I didn’t want to use Exasol’s big data capabilities, choosing to stick with what was regularly available to me. I also stuck with my favourite visualisation form. A scatterplot of destinations, using two measures, colour and size to cluster destinations. It didn’t attempt the sophistication of many of the big data dashboards, but it could only have been more Rosling if it was animated. And it was produced the day before his death was announced.

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My third example of his influence is perhaps the most powerful, but not a visualisation per se. Rosling had a real passion for his work, and wanted to get up and tell people about it. Not about anything technical in relation to his visualisations, but he wanted to tell the story the data was telling, to enhance the message. He told good news – focusing on social measures and the improvement in standards of developing countries, and used his charisma to enhance what his visualisations reported. This was perhaps the most inspirational thing that set him aside and placed him as such an influential master of his craft.

Just two weeks ago I was part of an exercise at the Tableau User Group in Nottingham. We split into groups of 4 or 5 of varying skill and experience levels to look at some data provided around road traffic accidents. Between us, we came up with a story and a couple of visualisations to present to the group. Nothing earth-shattering, I think we found things like the existence of more cycle accidents in London, more accidents during weekday rush hours, and each year’s quietest days being on Christmas day, that sort of thing. I felt I should get up and present – something that, like many introverted data people, is not something that comes naturally, but my role as a Tableau Ambassador would have made it a good chance to introduce myself and say a bit about what the team had done. But another attendee from our team wanted to get up and talk. I tried to be quite persuasive to say that I should be the person to present – I really didn’t mind – but my new friend was having nothing of it. Of course I was delighted that he was keen enough to take the presenting on, but in part I was actually quite disappointed. Having helped create the visualisation I wanted to tell people about it – explain the findings and tell the story. That to me was the biggest Rosling influence. Pride, ownership and conviction in our visualisation findings, with enthusiasm and charisma to pass them on.

2. David McCandless

There’s a spectrum in data visualisation that has Stephen Few or Edward Tufte on one side (strict adherence to best practice – “go to” experts for rules, standards and expertise), and David McCandless (fun, visually engaging, disregard for best practice at the expense of beauty). The first books on visualisation I bought were by David McCandless. As a novice stumbling on the concept for the first time, only about 18 months or so ago, his were the books I wanted to flick through, read and enjoy. Most exponents of data visualisation know the Few/Tufte principles and some decide never to stray from them. I feel all should know them and use them when appropriate. But I gain more pleasure from the visually striking and engaging, undoubtedly coming from the fact that I have created more projects for pleasure than I have professionally. McCandless’s brand, and website, is centred around the words “Information is Beautiful”. I bought the books because I agree!

Here’s a simple McCandless from his website – “Plane Truth” visualising commercial passenger plane crashes. The data is all there and accessible, but behind tooltips. Outwardly, all that can be seen is different sizes and colours of rounded squares. They tell a story and are instantly visually striking, but aren’t the best medium to exactly compare and contrast incidents without digging further; but to my mind you have to ask yourself if that matters. Are you using exact size of shape for insight (and, with that, the quickest, most accurate insight possible?) Or is an initial visual estimate OK if it’s visually engaging – if not an approximation, then an accurate depiction in not the most intuitive way. If the latter, then you probably, like me, lean towards the McCandless side of the spectrum. I firmly believe that it’s important to understand and adhere to all best practice principles where appropriate. But, once you understand them, you develop an appreciation of when it’s OK to bend, break or turn a blind eye to them!

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3. Chris Love / Rob Radburn

As I was starting out – one morning I was working from home with my twitter feed on in the background while listening to an England cricket match on the radio. At one point during the day, I suddenly realised that one person was visualising the game almost as quickly as I could listen to it and sharing his work with his followers:

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http://www.theinformationlab.co.uk/2016/03/30/7-tips-for-creating-a-topical-data-visualisation/

Instantly from this I realised several things

  • Though I was a complete tableau novice, the software can be used to make visualisations which are great, topical, quick (though I don’t think many people are as skilled and quick as Chris), and on almost anything
  • There are people doing this who are local to me. Within a few miles, accessible via the online community, and part of the local Tableau User groups.

I had no idea what a Zen master was but it turned out that this guy was one. As I’m sure you know, and I do now, that’s a pretty good thing. I’ve since seen many more great pieces by Chris – and though we often don’t agree (Chris is both technically adept at producing a great range of chart types while advocating the need to keep things simple), I can’t help be inspired by his work.

I also noticed plenty of correspondence at the time between Chris and Rob Radburn – another Zen master who worked in the same city as me. Rob was producing some lovely stuff too, and worked in local government, so often his concerns were to do with social data, the same field I was about to move into. This viz by Rob really resonated with me, and gave me ideas of more striking things I could do professionally. I didn’t yet realise that it was actually technically pretty easy to do, but it was a great example of “thinking outside the box” and telling individual stories through visualisation

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It was a great encouragement to have two experts local to me, living as I do a long way from big city London, and they continue to be leaders in their field. And I’ve yet to see a bad piece of work from either of them!

4. Adam McCann / Chris DeMartini / Rody Zakovich

So many talented data visualisation creators, but these are just three more of those high on my personal list, who do things that I like to create, and do them in my software package of choice: Tableau. There are people who create jaw-dropping graphic creations but I don’t include them because they are not the kind of visualisations I create. I like curves, mathematical visualisations, and unorthodox chart types. I know that some of what I create will make the purists cringe. I like to create exploratory vizzes, not necessarily insightful ones. So why these guys in particular? Here’s one example from each of a technique I’ve leaned heavily on …

Adam McCann loves to demonstrate new ideas and techniques, the very latest of which was just two days ago – the spark bar chart:

spark-bar-chart

It’s come in for equal measures of appreciation and criticism (probably the Few/McCandless spectrum again!). Why aren’t the sparklines aligned? How can one measure on the x-axis represent time but the bar chart doesn’t? I see past all that – I don’t need to directly compare the sparks, just see a visual snapshot of each against the bar it represents. And I borrowed heavily from this to create my own, using Valentine’s Day data.

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It’s not as good as Adam’s, not least because the bars are more uneven, leading to sparklines off the chart to the left. But Adam remains a huge influence (as I may have alluded to in my signature radial bump chart viz here)

Chris DeMartini has also created and documented some great chart types – including the jump plot which remains one of my favourites. Mine is adapted for Premier League goalscorers. Unorthodox, and there are many simpler ways of showing goalscoring trends. But this is crisp, different, and fun! I included the jump plot in my previous post right here

Rody Zakovich loves to create vizzes with curves – check out his public profile to see great work on Queen songs, golf shots, US football quarterbacks to name just a few. When he created a sunburst chart based on the Boston RedSox, I knew it wouldn’t be to everyone’s taste. But I loved it and used it twice, with Rody’s backing and help!

Rody’s Redsox viz (top right) became anglicised to a Birmingham City football viz (bottom right), and eventually resurfaced as a viz for songs of my favourite prolific band. Great examples of exploratory vizzes, versus explanatory ones

One thing each of the three have in common is that their work is unorthodox but accessible, and all use Tableau (to Zen Master status) so I know that they might all have greater skills and experience, but start with the same toolkit, and the same blank canvas, as I do. This gives me the inspiration to know what is possible.

5. Nicholas Rougeux / Valentina D’Efilippo

My other inspirations here signify what to me has been one of the most exciting ways of using data visualisation; that is to create pieces of art. I could have included other examples of great data artists here, such as Stefanie Posavec or Giorgia Lupi, the two responsible for Dear Data. The insight comes from the way you want to explore the visualisations and the time you want to take enjoying it. For me, I’m so completely uncreative and unartistic, that creating data visualisations has been the first opportunity I’ve ever really had to do something a bit more “arty” (or so it seems to me, anyway). And, unlike some of the inspirations I’ve quoted above, these artists don’t use Tableau. So in this case, inspiration is less about stealing (like an artist, of course) and more about being inspired to create something similar from scratch.

Nicholas has created some great work on www.c82.net. Often his visualisations are completely word-free, with simple lines and swirls depicting multiple examples of a certain genre. Below are visualisations of sonnets and literary first sentences respectively

Nicholas’ work is good enough to sell as a poster, which is something I would love to aspire to. When I recently wanted to do something a little “different” in visualising classic chess games, I came up with the following, based on average piece position and number of pieces present throughout the game. As soon as they took shape, they reminded me of Rougeux’ work on sonnets, road intersections, or classic novel first paragraphs. I saw that only as a good thing!

I haven’t finalised/published these yet as I’d like to include some more games, but here are my classic chess game visualisations covering three centuries:

(Inspired by Anderssen vs Dufresne, Carlsen vs Karjakin and Spassky vs Fischer). These are snapshots of a work in progress, but I think they could become similar art style visualisations. Would anyone buy them, even a fellow chess nerd? I don’t know about that.

Valentina D’Efllippo is another data artist I admire, and I have included her here specifically in relation to the Oddityviz project. Oddityviz is a serious of visualisations inspired by David Bowie’s classic Space Oddity album. How good must it be to use data to create physical works of art in an art gallery? The full gallery consists of seven record-themed visualisations: here is one of them, based on rhythm.

oddityviz_4_rhythm

I had this very much in mind when I tried all sorts of ways to visualise my 1980s pop data which I first blogged about here. The influence of Rody & co (see above) meant that I was always planning to do something circular or spiral, but I was never particularly happy until I muted the colours, my viz became more record-like, and I realised I had an OddityViz inspired creation here:

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It won’t sell out any galleries, and it has been pointed out to me that it looks very much like an eye – I think I’ve perhaps missed out on a whole different metaphor opportunity there. But I’m still learning and experimenting, and this is one visualisation which I wanted to frame (no pun intended) as an attempt at data art, with Valentina’s work as an inspiration

6. Donald J. Tr*mp

So who saw that coming?! The first five are current and past influences. This one, tangentially at least, is one for the future.

People are human, and although my social media bubble, in particular twitter, is almost entirely data visualisation related, a lot of people have been very affected by this guy (I refuse to write his name in full). They write, rant, protest and share about this man. What can we do, other than vote differently in 4 years time, and encourage others to do the same? (In my case, as a Brit, only the latter!). We’re not politicians or major influencers.

The fact is, dissatisfaction is a great source for visualisation material, and that is something that we can do. Tr*mp has appalling attendance figures at his inauguration? Visualise it! Tr*mp has worst approval ratings of a new president in recent memory? Visualise it! So far I’ve only created my tweet spiral viz but the very fact that the “topic” of the viz is something that polarises opinion has created more interest than usual.

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Ben Jones and Flippo Mistriani have created great visualisations too about his recent golfing following his many tweets about Obama doing the same. Here’s Filippo’s:

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People such as the fabulous Brit Cava and Ken Fierlage have visualised the strength of feeling about Tr*mp’s Muslim travel ban and reaction following Women’s marches in the USA and round the world here, they are just two of many doing great work.

Here’s Brit’s visualisation

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And here’s Ken’s

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As a final example, author Alberto Cairo (who I mention a lot, a highly respected data visualisation practitioner and lecturer) has had a lot to say about the election of Tr*mp. For a while, his (very frequent) twitter output changed almost exclusively from data visualisation and journalism related posts, to political comments and opinions. But, crucially, he has now decided to do something about it.

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Alberto is channeling the political anger and disappointment in the wake of the ubiquitous “fake news” articles into his area of expertise – data visualisation. He will be touring with “Visual Trumpery” lectures later this year. Personally I will do all I can to persuade him to visit the UK (more specifically somewhere Midlands or North!). More of this to come later in the year if and when it happens. But this is a fine example of using social or political issues to inspire data visualisation. Something I need to do much more of. And I’m sure that Tr*mp will inspire me and people round the world to get visualising, telling stories that need to be told.

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What can be achieved by collaboration?

What can be achieved by collaboration?

At the start of the year were a lot of tweets, messages and blog posts covering predictions in data visualisation for the year ahead and people’s own resolutions for what they might do in a new and different way this year to develop their skills, experience and exposure. Personally, I ducked out of that approach, preferring shamelessly to focus on what a good 2016 I’d had instead. But many spoke of collaboration being a key topic for the year ahead. Projects where people would work and visualise together, or pool together thoughts, designs and technical tricks in order to get the perfect combination. Some predicted this would happen more, and others resolved to be part of this new way of working.

As projects grow in size and the community of data visualisers grows at pace, I’m starting to notice this already this year. Makeover Monday has been a huge success through 2016, with on average approximately 60 people submitting visualisations on the same dataset each week by the end of the year. But in 2017, the first three weeks already have seen well in excess of 400+ submissions alone. Opportunities for sharing and collaboration are growing as quickly as the project.

This week, our dataset looked at New Zealand tourism spend figures, in index form, from 2008-2016 domestically and internationally. Our data covered the 60+ Territorial Authorities, and after a quick perusal of the data I came up with one conclusion: regional breakdown, comparable figures in every region, never seen it done before … I’d create a New Zealand tile map. I’ve blogged here about tile maps and had plenty of fun creating them, so I set to work on Sunday and created my visualisation below.

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I’m an early starter (it suits me to complete on Sunday as I will be much busier with work during the week) so mine was one of the first submissions. I was pleased with my effort but I think it was far from perfect. The tile maps worked well (or, they  worked the way I intended anyway). The colour scheme showed the regions as well as the Territorial Authorities, and my showing of monthly rather than yearly figures had two effects: (a) it showed all areas were somewhat seasonal but some much more than others, and (b) It meant it didn’t matter that 2016 figures were incomplete.

However there are some things I wasn’t pleased with. The overall colour scheme was eye-catching but somewhat quirky at best, with the lines looking like childish pen lines. The blocks to filter region in North or South Island worked and were functional, but the chart looked very busy. And generally I thought the whole thing was very unstylish (I know this is a particular weakness of mine).

I didn’t attempt to label each Territorial Authority on the map. Well, that’s not true, I did attempt, but with the long names for some areas and fiddliness of the task, I chose to remove the area labels and include them in tooltips and the rollover summary graph instead. And the colour scheme, though it did receive some compliments, will not have been to everyone’s taste – the colours were pinched from a London Underground colour palette (I can see UK readers having lightbulb moments here!). I kept in the regional regional breakdown as it was an important visual reference for me when creating the original tile map (see below – the aim to match left and right as close as possible whilst keeping the individual colours as true to their correct geographies as I can ). But was it necessary to keep the regional breakdown in or did it make the viz more complicated? Perhaps the latter, let’s just say I haven’t seen anyone else use the regional breakdowns yet.

The next day I saw the following submission (below) from Sumeet Bedekar. He posted his submission giving me full credit for the “design inspiration” (and crediting sirvizalot.com for the content/layout of the tiles). I don’t know Sumeet but he also participates in the Makeover Monday project. At first, I was torn between feeling a smidgen of unease that my tile map had been used, and delight that someone had chosen it. After all, in well over forty years on the planet I have never been anyone’s design inspiration in any way, shape or form! But within a matter of seconds, as soon as I looked in detail, any unease had dissipated to be replaced by satisfaction, and the delight remained.

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So what did I like about it? Yes the tile map design is exactly mine and uses the “work” from me the previous day to set up multiple graphs in tile formation. But the colours from my original have gone to be replaced by a much more pleasing red/blue scheme. The blocks for filters at the top and bottom merge in seamlessly across the screen instead of sticking out in ugly fashion.  Showing each line in a box gives a lot more definition and outlines the country more clearly. There is far less clutter and it’s a far more aesthetically pleasing visualisation – great work, Sumeet!

There are still elements of my own that I preferred. i’m not sure of the validity of combining domestic and international tourism in one graph as a percent, it doesn’t feel to me like that’s a valid measure and it doesn’t show increase over time. And perhaps he’s gone a little too far in keeping it simple – there needs to be some way of understanding what the figures represent (clarify, don’t simplify!).

Then, a little later, but also on Monday I received a message from Sarah Bartlett – it seemed she wanted to use the tile map as design inspiration too! Her final visualisation is here:

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Wow! This I really like. First of all – by now I’ve had a couple of hours to get used to being an influencer in the world of visualisation design, so this is old hat to me! If someone wants to use a snippet of my idea, I’m fine with that, so this time there was a bit of instant pride in seeing my own personal NZ map centre-stage for the third time. But everything else in the overall layout has taken my idea, and, like Sumeet, come up with something better and different! I tend to shy away from graphics and stylisation too much as it’s something I tend to struggle with, but it making the chart black with the subtle addition of just the Kiwi silver fern, we have a really elegant and NZ-branded visualisation. This confirms to me that it would have been easier than I thought to come up with something better than rainbow scribbles and grey text on a lime green background!

And in showing yearly, rather than monthly figures, we don’t see seasonal variation but we see increase over time more easily, and, whether by accident or design, each individual line chart resembles an ocean wave – perfect for the island geography and wild seas of New Zealand. To be critical – I think the upturn at the end of each line is due to incomplete 2016 figures skewing the averages up, which might need redressing (a point shared by Chris Love, in a further example of collaboration), but the visualisation would still look great if revised. Blue and orange are used to show decrease/increase since 2008, which I think is a better use of colour than dividing North and South island into its regions – used for insight rather than geography.

This might not be a true collaboration – after all, it’s three visualisations that were designed by three people. But the overall process feels like a collaboration of sorts that happened organically – I’m still really pleased with my original but feel that both Sumeet and Sarah took it up a notch and there are definitely elements of all three that I like. To frame it another way, I can claim credit for Sarah’s stylish visualisation which was beyond my means/creativity, because it couldn’t have been done without my tile map! There might well be a visualisation that combines the best bits of all three, or surpasses them all, but the nature of visualising with freely-available open data in a growing, proactive, online community which communicates, offers feedback and collaborates is sure to reap rewards and push standards higher. I’m looking forward to it.

How did I create the spiral chart?

How did I create the spiral chart?

Jump, Darn Dolt!

My latest visualisation is the spiralling chart below this paragraph, showing all the tweets of a certain dolt in anagram form who I don’t want to give any more publicity than he already craves, part of the Makeover Monday project. I’ve been toying with a spiral chart for a while (for example, I created one for my 80s pop data featured in this post, but it didn’t really show anything other than to achieve the goal of producing a spiral, so I never published it). But this time I decided to take the plunge. Uncharacteristically, this time I’ve had a few requests asking how this was done in Tableau. I hadn’t really planned technical posts on this blog – I tend to think that if I’ve come up with something new it must be simple, and if I haven’t it’s because I’ve usually nicked the idea and code from elsewhere (like an artist, of course!) But in this case I’ll make an exception – I’ll try and explain the thought processes behind the visualisation rather than just the instructions to replicate it.

As usual, you can click through to the online version – there’s some interactivity inasmuch as the instructions for reading the chart are behind the question mark if you hover.

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So, before going into the nuts and bolts, perhaps a quick thought process as to why on earth you might produce a spiral chart. What kind of things would I put in this post if I’d phrased it “When might you use a spiral chart?” or “Is there ever a place for spiral charts in data visualisation?” There’s not really a good answer, other than it is a legitimate way of saving space showing data on a very long timeline. If each turn means something (such as a year) then you have a potentially space-saving way of showing, say, nine years of information rather than a very long line, and you can see information from the same time of each year in the same area of the chart. You can’t read information off it particularly easily, but as a simple, fun, visualisation, it has its merits. In my case, I was able to marry the chart to the analysis (using the term loosely) that the tweets are “spiralling out of control” and if you add up each of these factors I think I have at least a few scraps of justification.

Moving on to the chart itself, the first thing to note is that it’s just a scatter plot. There is no connecting line being drawn or path being followed. It just looks that way because all of the circles are so close together (a consequence of this particular dolt tweeting almost every day). So, this is just a plot of Y versus X for every tweet, where X and Y are determined by the date. With 30000+ tweets in the dataset, and 2800 or so days elapsed since tweet one, it’s just 30000+ circles plotted on one of 2800+ possible locations.

The key to this visualisation is obviously the spiral. We’ll design the spiral so that each turn of the spiral is one year (this aligns days/months together and nothing else would really make sense). Spiral plots are like circle/radial plots, except the distance away from the centre of the circle increases the further round the circle you go, and this distance continues to increase with each revolution.

In order to plot the spiral we need to know two things for each date:

  • (1) How far through the year is the given day? This will determine the angle of the plot and how far round the circle we want to plot the data.
  • (2) How far away from the start (first tweet) is the given day? This determines the distance of the plot from the centre (without this, all the plots from each year would overlap).

So we use the datepart function to get the day of year from 0 to 365

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Then we can calculate the number of years since the start of the year 2009 and use this to work out which day of the whole dataset each date represents. First tweet was actually in May 09, but that doesn’t matter, it just means that the lowest value isn’t as low as zero. It’ll still give a sequential number of all the possible dates.

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Next, the angle. We know that each day is 1/365th of the way round the circle. Tableau uses radians, but all you need to know is that there are (2 x π) radians in a circle. So the angle is however many 365ths of the year have elapsed, times (2 x π)

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I should mention, I should strictly be a bit cuter here for 2012 and 2016 which were leap years, so in theory there two days (1/1/13 and 1/1/17) where we might have two days’ data on top of each other, where they coincide with 366/365ths of the way round the circle from the previous year. But for the purposes of this makeover visualisation, I’m OK with that. Just putting out there that I know this should be tweaked for 100% pedantic accuracy. But I don’t intend this visualisation to be used for analytical purposes. To be fair, I didn’t expect it to be open to scrutiny on this blog!

Now, we have enough to calculate X and Y using a bit of schoolboy trigonometry, see below for X:

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Ignore the left hand sides for a minute, but first we want to start by plotting X and Y at the correct place on a unit circle based on the angle value we’ve worked out, with day zero (angle=0) at the 12 o’clock position (X=0, Y=1 (maximum)). Best simple explanation I can see of how to use cos/sin is here:

https://en.wikipedia.org/wiki/Unit_circle

… though note in the Wikipedia example that the angle starts at the horizontal (x-axis). We want to start at the vertical (y-axis) for our zero point. But cock your head and it makes sense – the sin or cos function will give you the correct point on the circle!

Calculation for Y is below …

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For the left hand side of either the X or Y calculation we are controlling the multiplying factor which relates to distance away from the middle. This is based on the overall number of the day in the full timeline, which is what we want. The larger the “day of whole” figure, the more the sin/cos value is multiplied, therefore the further away from the centre it gets. It’s additionally controlled by two parameters: “Inner” and the imaginatively titled “parameter”. Without “Inner” the spiral would start at the origin, but tweaking “Inner” until you are happy with it will affect the size of the central area. “Parameter” controls the spacing of the spiral layers – again, try increasing or decreasing this number to get the lines closer together or further apart.

All you need to do now is plot Y vs X with the tweet ID on the detail shelf, and your spiral is in place. The key thing is that the values of X and Y don’t actually mean anything as such per se, they have just been calculated to put everything in roughly the right place on the screen, and to show your spiral you’ll remove all axes, headers and grid lines. Tableau will essentially plot all points as a ratio, in other words if X is, say, 200 it’ll be twice as far away from the x-axis as if X were 100. And the chart will automatically centre at X=0, Y=0. I settled on values of 50 for Inner and 4 for Parameter, which gives the spiral below. Note how it starts on the inside at about the 5 o’clock position (for the first tweet, May 2009) and spirals out to just after the 12 o’clock position on the outermost ring (since there are 12 days’ worth of Jan 2017 data).

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The rest is all formatting – I decided to go with the number of retweets determining the size, and the tweet method determining the colour. I’ve given the circles a small amount of transparency and a light grey border to allow for the overlapping effect, but obviously the nuances of the visualisation are up to the individual.

I think this was quite an easy one, though I say that as a closet mathematician! Perhaps it’s more accurate to describe as “easy when you know how”. No tricky Tableau functions to be aware of (apart from the favourite trig functions we all remember from school: SIN/COS), main visualisation on one sheet, no dual axes, only a few calculations and no level of details or data densification required. As mentioned above, I might struggle to think of a particular use case where a spiral chart is a perfect choice over and above anything else, but for me it’s just something a bit different which does its job visually but not in an over-analytical way, and given the subject matter of the visualisation, that’s fine by me.

Though if anyone can recreate it using wild spiralling strands of his hair, then I would be seriously impressed!

What did I learn from Tableau Conference 2016? (part 2)

What did I learn from Tableau Conference 2016? (part 2)

Part 1 was posted a few days ago here, featuring numbers 1-8. We’ll jump in at number 9.

9. Tableau is moving into data preparation.

With the announcement of a three-year roadmap followed by the developers on stage, Tableau announced a raft of new features large and small. These are all well documented now in a number of blogs (and Tableau’s own website here). As an aside – on the way is the ability to import from PDF. Wow! But the one I want to focus on is Project Maestro. This is Tableau’s name for a suite of data preparation tools within Tableau itself. As a one-man team at work, I spend a lot of time cleaning, manipulating, reshaping and pivoting, but high-end data shaping tools such as Alteryx are prohibitively expensive. This seems to be a direct rival, and on speaking to people who use Alteryx and the like, they have good reason to be worried! I’m excited to be able to have much more data-wrangling available within the visualisation software. It feels like a game-changer, and it’s likely to raise the game of Alteryx (other data preparation software is available!) too. I’m sure they’ll do things to stay ahead of Tableau’s data preparation capabilities.

10. An artistic solution is often better than a complex solution

Michele Tessari of Tableau gave a talk about Artful Data. I was expecting a session about nice artistic visualisations but I really liked the way he balanced the artful approach with the analytical. Best example was in calculating and displaying the median of a dataset. The string of complex calculations left most of us open-mouthed in complexity. And yet, set up a curve showing cumulative percent, and hey presto the 50% mark is the median. Done in seconds, more easily, just as accurate, and a clear example of the advantage of an artful visualisation over a complex set of calculations.

11. Make sure your visualisation stands up in greyscale

There’s so much talk in data visualisation about correct use of colour. For that reason, this one piece of advice from Andy Cotgreave (which I haven’t heard before) seems counter-intuitive at first, but makes perfect sense. If you have a well-chosen colour palette then it should be perfectly readable in greyscale (just black, shades of grey and white).

Rather than critique some visualisations out there, I’ve decided to put a couple of my own more colourful works under the proverbial microscope. Time to admit that the first one (a France tile map using a large range of pastel colours) has not worked well in greyscale. The second (a heptathlon-themed visualisation) has worked nicely in my opinion, with the full range from green through yellow to red distinguishable in the Overall Position column.

One out of two looks OK – perhaps I should go back to the drawing board with the France visualisation!

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France tile map – some colours are hard to make out in greyscale
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Heptathlon data visualisation – originally used palette of red, yellow and green

 

12. #VizForSocialGood

There were many separate sessions on a common theme – the idea of using visualisation for a good cause. Schedule clashes were such that I missed sessions about the work Tableau Foundation are doing to support non-profit organisations, as well as a great talk about the work of Tableau geniuses Datablick and how they are using tableau mapping to help eradicate malaria in Zambia. I didn’t attend that talk because I don’t really have experience mapping in Tableau, but have heard nothing but good from those who attended and those who have taken part. But in addition to this, the final gathering of the conference introduced the idea (and hashtag) of #VizForSocialGood (from Chloe Tseng/@datachloe again). I like this. As we continue to learn and improve, it’s important to work on personal projects you want to work on and enjoy (as I alluded to in the Steal Like an Artist section here). But I think that should include not just cricket, or music, or books, but important causes, especially as presence in social media networks grows. I refuse to use such a cringeworthy term as a New Year’s “Vizolution”, but it’s definitely on my list to do more of over the next year. And soon.

13. The Double Pivot

Another technical takeout here, which I won’t write too much about. I have to include innovations in survey data reporting though. Zen Master and leader in the field of Tableau survey data reporting gave a talk on new ways to visualise survey data given recent improvements in version 10. Most prominent was the “double pivot” –  a way to pivot demographic data and merge back with rest of the pivoted survey data. The upshot is a great way of showing traditional banners of demographic/dimension data. Survey data is my bread and butter professionally, so it’s raised some interesting possibilities for professional work. So it’s included in my list.

14. Let the Data Decide the Visualisation

Another tip from an Andy Cotgreave talk – is “Let the data decide the Viz.” I like to think it’s obvious but it’s such an important thing to bear in mind that it bears repeating. Particularly within the confines of an environment where you might be keen to try something new. You can’t just learn how to do a sunburst chart and decide you’re going to use it for your next visualisation. Data first, then visualisation type, not the other way round. Below is a very famous visualisation which Andy referenced – creatively and brilliantly set up to represent descending blood representing casualty numbers. But it only works so well because the data fits. Unnamed_CCI_EPS

15. A word on Tableau’s community

Leaving the best to last, but I have to be careful here. A few months ago, Ben Jones of Tableau Public wrote this excellent blog pos about building a thriving community: http://dataremixed.com/2016/07/building-a-thriving-community/ – in particular including this graphic

community-e1469116614224

I said I need to be careful here – with the welcome I got from so many of the community at the conference I’m in danger of rolling down the back of the hill straight into the love fest! I can’t talk highly enough about the welcome I received. It seems that if you (a) do a lot of visualisations, (b) tweet a lot, (c) have a blog that people read (this one, believe it or not) and (d) are English, for added novelty value, then there are so many people to meet, and so many people who recognise you. People, some of whom I didn’t know but many of whom I admire, introduced themselves, shook my hand and just wanted to share conversations and experiences (and sometimes beers!). I thought of mentioning everyone here who I met who I spent time with and made my experience better in their own way. But it’s no exaggeration to say there are too many. In fact, much too many. And to single out just a few would be unfair. But you all know who you are, and thank you all.

Added to this was a genuine big surprise. Tableau Wannabe Podcast Hosts Emily Kund and Matt Francis host the “Vizzies” awards, for various categories  voted for within the online Tableau community. The (joint) award of “Notable Newbie” for me, presented on the final morning, was the icing on the cake for me. And it was a hell of a good cake even before then.

Here’s a link to Matt and Emily’s podcasts including one with more details on the Vizzies (I’m not sure the awards are published yet).

And here it is.

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Back to Ben Jones’ blog post and diagram, it’s true the “love fest” isn’t the most constructive way for the community to grow. Perhaps we can make an exception for 13000 or so like minded people meeting face to face (this sounds bad doesn’t it?!). But I love the idea of “iron sharpens iron”. The result of the face to face contacts and friendships I made is that the number of people I can call on for constructive criticism as well as help and advice has greatly increased. I’ve already been pleased, since the conference, to be constructively talking to those I previously only knew tenuously. And I genuinely think that one week in the year spent talking about visualisations, meeting people and socialising (talking about anything but visualisations!) leads to the other fifty-one weeks with a stronger support network. Of course there’s no need to wait for national congresses – we are all encouraged to join local user groups and meet-ups, and I’m delighted to have taken part in the inaugural Birmingham Tableau User group already since returning from the conference.

16. And finally …

That’s it really – I could have a miscellaneous section on non dataviz-related weird and random stuff, such as (a) people take pigs onto planes from Austin airport, (b) a previous governor of Texas could have been my ancestor, (c) the giveaway squishy Tableau brains make great cat toys (and I admit that’s a touristy “Keep Austin weird” mug accidentally in the background, or (d) the local Voodoo do(ugh)nuts were amazing, even if shaping them into a “T” for Tableau was perhaps more suggestive than intended, but it’s not that kind of blog. Oh, go on then. Hoping to continue this blog series in a little under twelve months time from Vegas in 2017!

 

What did I learn from Tableau Conference 2016? (part 1)

What did I learn from Tableau Conference 2016? (part 1)

Last week, I had the great fortune to attend Tableau’s yearly conference, in Austin, Texas. In the interests of disclosure, I should say that my entry was paid for by Tableau, as winner of an entry competition. With so many sessions to attend, things to learn, speeches to hear and people to meet, I’ve tried to narrow down to a few key points. I failed, there are lots. Here are the first few of a two-parter, in no particular order:

1. Write the book you want to read / Write what you want like

This learning is the only one that is in a particular order, because it came from the plane journey. A couple of weeks ago, I recommended the concept of “Steal like an artist” – in doing so I obliquely referenced Austin Kleon’s book of the same name. It felt right that I should go one step further and actually read the book, so I purchase the book to read on the plane. I can thoroughly recommend the book for anyone who wants to think creatively in making data visualisations. The author is a creative person, an artist, who, by coincidence, lives in Austin, Texas, so reading the short book from cover to cover really gave me the mindset for the conference and upped my excitement level from day zero.

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The quote I’ve chosen is simple – in looking to make an interesting and creative visualisation, think of yourself as the audience. The advice “Write what you like” is in direct contradiction to the usual mantra of “Write what you know” which is crossed out in the book. As an example, in traditional writing, you will write something much more creative and interesting if you write what you *like* (even if it’s science fiction, wizards and dragons) rather than what you *know* (your job, family and what you had for lunch …). That’s the mentality for creative visualisations.

2. Complexity is seductive

This comes from Chris Love’s excellent talk on the subject of simplicity. Keeping the mantra “Keep it simple, stupid”, i.e. KISS, he skilfully led us through the arguments for simplicity in visualisations. Complex is OK – if you’re trying to be noticed, win a competition or make your portfolio more visually attractive. But it’s rarely the best option analytically. In deconstructing a beautiful full multiple Sankey chart (below, original devised by Pablo Saenz de Tejada)

dashboard

he showed us how insight could be better shown with simple histograms. I felt this was done with good taste and balance, since I’m a huge fan of the visual appeal and complexity of the original. A well-told story, not just for exalting the advantages of simplicity (which is an easy sell) but in acknowledging the balance.

3. Complexity is continuous, not discrete

Of the eleven breakout sessions I went to at the conference, when people asked me my favourite, I usually answered that it was Matt Francis’ talk: Bunking Data visualisation Myths. I would follow it up with the admission that it was probably the only talk where I didn’t learn anything new as such, but confirmed many of my thoughts on standard data visualisation arguments (are pie charts bad? should you never use red and green? etc etc). It was my favourite for pure entertainment – I’ve met Matt before and know he’s a nice charismatic guy (as comes across in his Tableau Wannabe podcasts). But I didn’t know quite how entertaining the talk would be – an hour of bad jokes and laughs at just the right point in the conference, great fun. I would thoroughly recommend a chance to watch Matt talk in the future, or at the very least listen to or catch up on any podcasts.

But on re-reading my notes from the talk I notice this gem that I wrote down – that complexity is continuous, not discrete. It follows on nicely from the point above and I like that a lot – there is no more an obligation to go for the flashiest possible an option than there is to keep the simplest possible option. The answer can lie anywhere on the scale from simple to continuous, depending on data, circumstances and so much more. In Tableau terms, it’s a green pill, not a blue one!

4. Positioning is important in how the user reads the dashboard

One of the most captivating talks was from Bridget Cogley – her expertise and philosophy is further demonstrated on her tableaufit.com website. In fact the full presentation she gave can be seen here: Bridget explained her background in ASL (American Sign Language) and linguistics as a background to her experience in designing dashboards. The results are always beautiful, but the importance of every consideration in a dashboard is every bit as important as grammar in the linguistics of a language. From a sound basis in correct grammar, the most beautiful and creative prose and poetry can be generated (not to mention the most technical and prosaic tomes too!). And from this background, Bridget made no apologies for showing us tweaks on the same dashboard dozens of time. From such an engaging and enthusiastic presenter (with the best trousers in dataviz) there was no apology needed.

Just one specific take-out was the importance of positioning and the consideration of Z-reading (shown below)

writz-pattern

Our “default” way to read a dashboard follows the way we read text, and therefore we read in a “Z” fashion, unless of course we are reading in non-Western languages such as Arabic. It’s possible to position things in such a way as to draw our vision in a different fashion (perhaps the self-explanatory “N-reading”) but always consider Z-reading as the natural default.

I could have put lots of elements of Bridget’s talk as a headline, but possibly the key takeout message for me (let’s call it 4a) was

4.a. In interpreting, if your message isn’t understood, you’ve failed. Exactly the same goes for dashboards.

5. The “Crying” emoji was the most popular Twitter emoji in April 2015

loudly-crying-face

Why is the “Crying” emoji the most popular in April? It turns out because a lot of people are very unhappy about doing their tax returns. How do I know that? Because Twitter use Tableau in many ways, both internally to monitor the progress of their analysts and the tickets they work on, and to record (sometimes fun) analytics about how people are using twitter. How do I remember that? Because I attended a session on how twitter use Tableau. In fact, this was the only customer session I attended, with all others being specific to features in Tableau or principles of data visualisation. But the real reason that answers the question “how do I remember that?” is the enthusiasm and passion of the speaker (Chloe Tseng). Quiet and polite away from the microphone, her enthusiasm, passion and humour shone through, whether in her daily work or the causes she believes in. It was a key example to me of the many ways in which data visualisation people (Tableau people in particular) are passionate about what they do and in sharing their experiences. It made me want to be as enthusiastic and charismatic as Chloe (and Chris, Matt, Bridget mentioned above) and talk at more gatherings and conferences, from the fun to the serious and technical.

Here we see Chloe talking about her work at twitter, possibly to do with emojis, but equally likely to do with the Lean Startup philosophy of designing dashboards internally where they try and minimise the number of administrative/iterative progress points in the design and creation process. The emojis and enthusiasm draw you in to the drier business message which in turn denotes a promising ethos for the future.

6. The US Election is a tricky time to hold a conference

“Avoid politics” was the general mantra – so many great visualisations had been done on the US election in the run-up to the week, but they were avoided so close to the event. I suspect that the US election date wasn’t considered when the conference was organised, with the result that election day fell right in the middle of the conference on Tuesday. Rightly, all US citizens had been encouraged to vote in advance before attending the event, but still the event passed with barely a word. But as votes were counted in the evening (and I watched in fascination in the evening on my phone in the bar in company of bemused and incredulous US delegates I now consider my friends) it became obvious that something very big and unexpected was happening. When you’re in the business of data, research and visualisation, it’s impossible to ignore the implications of the vote (I’ll devote a full post to it and will link to it very soon here once it’s written). But aside from that, speakers and delegates had to deal with a world-changing situation in mid-conference. Generally this was done with great dignity. The keynote speech the next morning was given by popular US radio scientific personality Shankar Vedantam  and although the psychology of decisions based on fear seemed particularly apposite, he kept respectfully on-meaage. The election and results were largely unmentioned except in good-humoured asides.

Outside, the streets of Austin were disrupted with protests which were small and good-natured. Political discussions were kept out of the limelight and life went on. Only the final keynote (from Bill Nye, Science Guy) went political. There was a little discomfort in his anti-Trump/conservative/evangelical views, but Bill’s pleading for future generations to use data to save the world were purely science and truth-based, and though not to everyone’s taste I don’t have a problem with that.

7. Tableau is moving forward with diversity, slowly!

I’m a white, English-speaking, male data nerd and was expecting to be in the company of an overwhelming majority of people like me. Now I don’t know what the figures are, but I was encouraged throughout to see the prominence of women. The pre-conference began with a Data+ Women meetup and when the keynote speech featuring five of the brightest young developers took place, four of the five were young women including Ethiopian-born Makari who seemed a brilliant star for the future. The line-up of Zen masters (the most talented Tableau users who do most for the community and users) remains largely male although the most recent intake has addressed this slightly, with three of the most recent ten Zens being female. Overall though, the line-up is largely white and male

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Now this can’t change overnight and it would be unfair to “un-recognise” the great work and talent of the men involved, but the brilliant women I met suggest that the talent is out there. But the one place we really need to do something about it is in the UK. Our five UK-based Zens are all white, male, and similar in age. Let’s see some talented British women or non-white men there next year, Tableau!

8. Diagonal reference lines, starburst charts, mobile formatting charts …

This blog is getting long now – let’s end on a high, and a “more to follow”. I learnt so many new technical tips that there’s not enough room to include them all. I missed Andy Kriebel and Jeff Shaffer’s “50 tips in 50 seconds” talk which is now top of my list to watch on-demand. But despite that, Robert Kosara taught me how to use diagonal reference lines. Adam McCann taught me how to do a starburst chart. Dash Davidson gave a great “Jedi” tip on how to create mobile dashboards, Michele Tesseri showed us how to visualise median measures … the list goes on. These kind of tips are the nuggets that we all went to pick up to do our job better. The list is long, and the conference just kept on giving.

Part 2 will follow

How should you prepare a visualisation project?

How should you prepare a visualisation project?
Here’s a post about my latest significant visualisation – it’s Olympic-themed, centred around all the decathlon greats from 1984 to the present day. I’m genuinely quite happy, but not delighted, with it. Click on the image below to see the interactive version on Tableau Public. And please, if you like to explore this kind of thing, and love your decathletes, facts and figures, interact to your heart’s content!
decathlon
Prior to working on this, I listened to a webinar from Andy Kirk (data visualisation expert and author, owner of  www.visualisingdata.com) which has coincided with the release of his excellent new book. I don’t need to tell you about Andy or his website, because if you’ve found my blog in the online data visualisation community, it’s an absolute certainty that you know Andy’s far more important and instructive blog already. Having also now read a significant portion of his book I can certainly recommend that too for any visualisation practitioner. Using his own visualisation project as a reference point throughout, it’s vital expert advice on all stages of preparing, formulating and executing a visualisation project. Read this post first, then go to his website and, if you’re feeling flush/generous/inspired, buy his book!
The gist of Andy’s webinar as well as his book is that there are four stages of designing a project.
          Formulating a brief
          Working with data
          Editorial thinking
          Design solution.
All these stages should be considered in order to give the best possible grounding for a successful project.
I’ve also taken a lot of stock of what Tableau’s Andy Cotgreave says, on his gravyanecdote.com website. Notwithstanding the fact that the fantastic domain name makes me wonder if I should register rancidrelish.com, Andy offers great advice on iteration. His advice features the “squiggle” of data visualisation project design (below)  (which is based on Damien Newman’s Design Squiggle.)
 squiggle-narrow
It then features a relatively simple visualisation which had over 300 views of the data before finalising the project. The point being that the process starts with rapid exploration, and in the middle “exploring” phase (the continuing messy bit of the squiggle), we find new ideas to try, to improve on or discard as we continue the process. Only towards the end do we invest time in finessing the final product. You can read Andy’s post in full here:
So how, if at all, do these two approaches marry up – can you involve yourself in the full planning stages as suggested by Andy K, fully laying out the brief and having the solid foundations of your project ready before committing to working on it, and is this consistent with diving in to a rapid exploration “squiggle” phase of mass iteration as suggested by Andy C?
I think, to an extent, yes. The two need not be mutually exclusive, and the key difference is that in Andy C’s example (are you keeping up?) our example was a “self-commissioned” project. We have a dataset we have chosen to explore and visualise, and we are essentially our own client (although in my case, work started earlier in sourcing and shaping the dataset). However. I do believe adopting the four-stages approach of Andy K can really help. In an ideal world, the up-front preparation could really reduce the iteration at the project stage if we have a firm plan and well-formed brief. Working with the data beforehand not only ensures that we can ensure that it is clean, accurate, and in the correct format for our software package to work with, but it also allows us a sneak preview of some of the insight we might find.
So, back to my decathlon visualisation. The first questions to consider were what inspired me to create it, who were my intended audience, and what insight was I trying to show (what story was I trying to tell? – though I’m careful about using the “story word, I think this is potentially more of a “story” type visualisation than many).
I was inspired to create it because I wanted to join in with the many Olympic-themed visualisations. My favourite visualisation up to now has been my Premier League visualisation (you can find that elsewhere in my blog) which was a lot of fun and has been well-received. However the radial nature of my Premier League visualisation was not key to the insight, or the story, rather it was just an eye-catching way to display the twists and turns of a season. Described as a “polar bump chart”, I haven’t seen anyone else produce one (perhaps for good reason), so I wanted to continue along that vein. Extend this radial format (literally) to an oval shape and you have a running track, where the radial nature has more significance. Extend nine months to 32 years, substitute 38 matches for ten events, and you have the basis for a new visualisation. What I then wanted to see is how the greats of my childhood measure with the greats of recent years. My intended audience are people like me – sports fans, data fans and visualisation fans. I know that I don’t work on a worldwide newspaper or hold nearly enough fame and status to be seen by more than a handful of people (even a “viz of the day” will only take the audience from two figures into four) so the intended audience is people like me. If a hundred people like me see it and enjoy it, I’ll be happy.
We then move on to what insight do I want to display – crucially the leading message should be to see what the podium would be if all the competitors since 1984 competed simultaneously. And we do that successfully: the podium of champions features Sebrle, Eaton, Thompson. Personal delight for me that my hero from 1984 (Daley Thompson) would still be a medallist if he competed at that level today. But I found so many other stories while preparing the data and running early iterations of the visualisation. For example:
          Dmitry Karpov of Kazakhstan was the 2004 bronze medallist, but he would have been leading this fictitious all-star event well into day two. What happened to him? With no photo of him at the landing page you’d need to do a bit of exploring and moving the filter sliders to unravel that particular story.
          Similarly, Bryan Clay (2008) led after nine events but didn’t make our final podium! Was he a very poor 1500 metre runner or was his lead so strong in 2008 he could afford to relax?
          How close were all the battles in each year? In merging all together, we’ve taken that information away (visually, at least).
          Who did the best in individual events? OK you can just about make this out with judicious use of the slider, but it’s not very clear.
          Ongoing “league table” – I had a running league table available to show interim standings, which made it into so many iterations (and was thrown out of so many others). Ultimately there just wasn’t room so I went for the podium and photos instead, but I can’t help thinking there might have been a way somehow …
          What did some of the greats look like? Unless you can manoeuvre them onto the podium, you won’t see a photo. Thompson’s great 1984 rival: Jurgen Hingsen, remains consigned to the code, as I downloaded and included images for all 24 competitors, though you may never see some of them!
          Animation: I tried so hard to animate this but it just didn’t work properly. Animation meant losing all other functionality such as filtering and/or using the slider, and was just too clunky.
 decathlon-2
(the above capture from the decathlon visualisation proves that it is possible with judicious slider use to see the top three finishers in the discus, as well as to admire the photo of 1984’s Jurgen Hingsen – but you need to know where to look!)
So, I formulated a brief (display the decathlon competitions since 1984 as one simulated combined competition between medallists), worked with the data (copied, data entered and formatted in such a way that Tableau could plot all points on the curve not only for the ten events, but for points in between, so as to allow filtering at 100 points on the “track”, adopted my editorial thinking by focussing on the story, and came up with my design solution (the aforementioned unique oval racetrack bump-chart!)
My self-criticism might be me over-thinking, it might be me not making the best use of the iteration/scribble stage, or it might be that I have produced an unfinished or imperfect project. Each time I went back into the “scribble” to consider one of the points above, I either came back out with it unresolved, or made a compromise that led to a new unanswered question. I think I need to be less critical and accept there is only so much a visualisation can show. If I were to see this produced dispassionately by someone else, I like to think I would dig in, explore, find out or confirm many facts, figures and stories that interested me, and be inspired to read further (on- or offline) about the relevant competitors and events. And that’s all we can really hope for. But I think it’s best achieved by taking the time to devote to the four stages of the design project, as well as intelligent and confident use of the iteration stage.

What makes a “viz of the day”?

What makes a “viz of the day”?

First – an explanation. The majority of my visualisations are done using Tableau and published on Tableau Public (here). Available for all to see, it’s a great resource for experimenting and publishing work that you’re happy for all to see. Click on it today and you’ll see there are 190000+ authors, 25M+ views/month and over 20,000 visualisations posted per month. It’s a fantastic, free, public resource. Presumably “Viz of the Day” needs less explanation. Somehow, one of them gets picked each day to be promoted on the website and via social media.

To my amazement and delight, despite the odds above, I have been picked as “Viz of the Day” twice now in two months. The second of these was today, with my recent Brexit data visualisation. I’ll post it again here:

Dashboard 1-10

Now, that was the awkward bit, because this isn’t a trumpet-blowing post. Obviously to be “viz of the day” you need to have produced something reasonably competent and polished, but that’s not the be-all and end-all. Right now, there are some amazing pieces of work on there, for example there are many entries to the Tableau “Iron Viz” politics competition. Far more involved, detailed and visually stunning than mine, the key criterion is to make the reader go “wow”. They all meet this aim. But of course, none of these could be viz of the day, since this would be demonstrating favouritism for a publicly-voted competition. So, let’s make this point 1.

  1. The visualisation needs to be polished and finished, and in terms of technical skill, at least vaguely “ok”. I’m not good enough to enter visualising competitions yet (certainly not good enough to win them). But I think I have improved, through practice, to the level of “OK”. And I genuinely believe that Tableau Public like to promote new, keen users. They know who the Zen Masters are and probably feel that they don’t always need/want the same level of recognition!
  2. It helps if the topic is interesting / topical. I mentioned in my last blog post how great elections are for visualisation – being up to the minute, they are likely to contain and show information that people are genuinely interested to see. In the last week, our news and social media feeds in the UK have been full of Brexit and Euro football and very little else. I’m drawn to anything on these topics, especially anything which will give a different angle on our surprise exit from Europe (in both cases). So it’s no co-incidence that most visualisations I’ve experimented with in the last few weeks have been to do with the UK referendum, or about football. More great advice from Chris Love (Tableau Zen master) in his blog post here for the Information Lab.
  3. Social media following – I’m not suggesting it’s a requirement to be Stephen Fry or Taylor Swift, but there’s quite a community growing around data visualisation, and Tableau visualisation in particular. For example, search the #MakeoverMonday hashtag and you’ll find an increasingly large group of people who not only contribute regularly to Tableau Public but who feedback and interact with each other. Reputations, profile and “brand” of everyone involved is increasing week on week, and many participants will blog about their creations too: thought processes, technical tips, different iterations of the final project … or in my case a rambling question tangentially related to what I’m doing. If you interact from time to time and maintain a blog, regardless of increasing profile you will learn so much more all the time – be constructive, friendly and open, and you’ll find people are the same back.
  4. If in doubt about anything you’re doing, so long as it’s not awful, do something and post it. Most people will give nice comments if they like it, and ignore it if they are less impressed. I mentioned online today that my VOTD is down not so much to high quality but to a scattergun approach, but I was only half-joking. The more you put up, the more of your stuff will get noticed. And if something is recognised, often it won’t be the visualisation you think which will “take off” and get recognition.
  5. Luck. Large portions of it. Of course. (2) and (3) above help sway the odds to an extent, but it’s just “right place, right time”.

Do all these things and you might beat the odds to get Viz of the Day when you’re least expecting it. Or, if you don’t get the same luck, you might not. But you’ll definitely increase and improve your output.