Where is the joy?

Where is the joy?

I haven’t gone all contemplative on you for this post, nor am I channelling my inner Black-Eyed Pea, but instead this is a post about the recent phenomenon of the Joy Plot. And it’s fair to say it’s resulted in a bit of a division of opinion. Those who know the derivation of the Joy Plot will see what I did there … those who have no idea what a joy plot is but have good recollection post-punk gothic type music from the late seventies might have some kind of confused association going on …

As far as I know, the term “joy plot” has only recently been derived. It owes its name to the iconic cover of an album by Joy Division from 1979 (below)


Robert Kosara has an excellent EagerEyes blog – and in his recent post here, he mentions that the term was coined earlier this month by Henrik Lindberg, with examples here


It’s hard to give an accurate, exact definition, so instead I’ll give a vague one here:

  • A succession of horizontally close-packed area charts over time, usually showing defined peaks
  • The lines are sufficiently close together that the peaks overlap/obscure the lines behind (giving a sort of 3D effect)
  • Looks a bit like the cover of “Unknown Pleasures”

It turns out that the original artwork was based on radio / magnetic waves from a pulsar star. But I think the recent popularity of joy plots (edit: I deleted the words “explosion in” – let’s not overdo this) is down to a brand new addition of a module making similar chart types available in R.

To explain how I stumbled on to the idea of creating a joy plot, first I probably need to explain that my blog posts are slightly out of order. My last blog post was based around the choice of colour for a tennis visualisation I was planning, and after a long process of obtaining, poring over and cleaning the data, I was left with my finished visualisation. Cue my next post (currently about 25% written) about working with datasets over a longer time in larger projects, and showcasing my resulting visualisation. But as is my wont, I continued to be distracted by other ideas for new projects.

Since my aforementioned tennis dataset consisted of the individual game/set results for every Men’s Singles match at Wimbledon from 1968-2017), it seemed a shame not to use such a rich data source for new visualisations. As will become apparent, my visualisation considers the full dataset of male tennis players and compares the journeys through their individual careers by looking at the +/- scores of cumulative games won. But it doesn’t split the trails into individual lines for each player. What would be a good “small multiple” type way to show this? I began to try one or two things.


My first attempt (above, not annotated), might be described as a series of sparkline area charts. This shows the years from 1968 to 2017 on the x-axis, and the top 40 tennis players on the y-axis, in terms of total number of wins. Players are ranked in order of debut – Roger Federer is about the ninth player down, showing the thickest area marks over a particularly long career. At this point, I’ve already had the light bulb moment that this looks a bit like the album cover I’ve seen in discussion with the current trend of joy plots, so have deliberately made the chart very minimalist using white marks on black backgrounds.

But this isn’t a joy plot because there’s no overlap. Now it’s important for me to say at this point, that although it seems like stating the obvious, some people might assume I haven’t thought of what come next: I know that overlaps aren’t good. I know that 3D effects aren’t good. If I wanted to get analytical value from a chart, this might not be a fantastic visualisation but it serves a purpose, especially if annotated. But I am interested in discussion, in technically challenging myself, in new styles, in art within visualisation, and post-punk 1980s music, All good reasons to pursue a joy plot.

As might be expected, this needed a bit of thinking “outside the box” – with apologies for the cliche, it required less orthodox processes within Tableau. Once I realised how to overlap the individual lines, I reached a few possible joy plot alternatives: either fully opaque or partially transparent, and with or without grid/zero lines.

version 2bversion 2Dashboard 1a

The second and third options here are starting to look quite nice. Option 3 here shows some transparency, allowing all values to be seen (I’m well aware that I’m not showing player names/axis values at this point). Option 2 in particular, from a distance, looks like clouds drifting across the screen.

At this point – a quick (very high-level) technical note on how I did this. From a Tableau point of view, putting player detail on the row shelf will always lead to a non-overlapping visualisation, so the answer lies in plotting co-ordinates. I looked around for some examples and inspiration and found that Bora Beran has recreated the original already. See his blog here and you can download his Tableau dashboard which does that. Looking at Bora’s work, I could see that it was done creating a polygon for each row, with each of the points pre-determined rather than calculated within Tableau. So, I exported the data from my first chart (top forty men only), added some bottom “corner points” and some path indicators to distinguish the polygons, and re-imported to a separate project. Then I just need to offset each polygon by a multiple of the rank, and we’re left with overlapping horizontal polygons. Feel free to download the workbook from my visualisation below for any further insight.

Rightly or wrongly, I decided if I was going to do a joy plot, knowing its aesthetic advantages and its analytic disadvantages, I wanted to go the whole hog and go close to the album cover. I changed white shading to black, and here is the final result. Note that my interactive version adds a splash of colour to highlight individual players, in an admission of the downsides of overlapping trails (click on the image to access the interactive Tableau version).


Its worth noting that, as mentioned at the top of the post, this is absolutely not to everyone’s taste. Bora Beran in his blog faithfully imitating the original Joy Division album cover calls it dataviz art, and that’s exactly what it is. You certainly won’t find this in any instruction textbooks.

Here was reaction from Matt Francis – former Tableau Zen master and one of the people I respect most in the community. Now clearly he’s not overly impressed with joy plots which is both fair enough and understandable. But the point he makes is 100% right!

And here is Andy Kriebel’s response – Head Coach of the Information Lab’s Data School and organiser of Makeover Monday. If there’s someone in Tableau data visualisation you should be paying more attention to, then I don’t know who it is (hint: there’s nobody).

Certainly, I had other similar comments. There were those who appreciated the appeal of the chart but didn’t agree with it. Those who, while grudgingly accepting that I was going to attempt a joy chart, preferred the transparent white cloudlike earlier iterations to the final black version.

So should I just show a bar chart instead to visualise the top 40 men of the open area in Wimbledon? Maybe – the below took me two minutes flat and makes it abundantly clear the top forty winners of games and the dominance of Federer, Connors and Becker.

Sheet 11

But this won’t be remembered, discussed or explored. Investing time in exploring and understanding a visualisation can lead to a greater understanding and recollection of key information (that’s not an asserted fact, it’s just my own hypothesis, based on how I interact with my favourite visualisation types). Being eye-catching, unusual and fun can be plus points to any visualisation, if used in appropriate situations.

Back to Matt Francis’ question above. To paraphrase – instead of investing so much time devising and iterating on a joy plot, shouldn’t I just consider whether it should be used in the first place (the implication being that because it hides data, that it shouldn’t be)? Well, I’ve done that, and come up with the answer: “yes, it should”.

The key advice that will always be offered when considering a data visualisation is “consider your audience”. Therein lies the answer. If my audience were the general public; if I’d been commissioned to write an article for tennis fans or newspaper readers; if I were writing a report showing findings of my research of 40 years of results, there’s no way I’d create a joy plot. I’d probably spend time making the above bar chart look slightly prettier, include that, and move on.

But my audience isn’t the general non-visualisation consuming public. My audience consists of the following:

  • Data visualisation enthusiasts, whether sticklers for analytical best practice or fans of artistic less functional projects
  • Blog followers, many of whom enjoy a debate on pros and cons of various methods
  • People professionally interested in a variety of visualisation techniques and chart types
  • People who know what I’m like, and who enjoy my, still novice, foray into data visualisation

So, I make no apologies for my joy plot. It hasn’t brought joy to everyone, but despite some of the constructive criticism above, some of you loved it, and I thank you for it!

Of course, the sensible thing would be to leave it there. After all, I’ve got personal projects to consider, not least an out-of-sync tennis blog post to complete. But, being me, I stumbled upon Henrik Lindberg’s next idea and couldn’t resist  Depeche Plot, anyone?



Of course this also has its faults: it’s possible for a line to cross the circle twice, becoming a chord, for example. And although it’s explained with annotations, it’s not going to be a particularly intuitive chart. But, like my joy plot, it’s possible to explore more and find out everything necessary by hovering and examining tooltips. It’s imitating album art, contains genuine explorable data, and is fun. And yes, I’m wondering if there are any other album cover data art possibilities. Why not?!

I’ll continue to find joy in as many ways I can visualising data. Even if it means looking beyond best practice bar charts!


What is the best choice of background colour?

What is the best choice of background colour?

This week I began work on a Wimbledon-themed viz. I’m sure when I started this blog I had a stubborn determination not to use the word “viz”, rather to use the full term “visualisation” wherever possible. But I have a feeling it didn’t pan out that way, and given my propensity to veer off-topic, anything that saves ten characters of typing can’t be a bad thing.

And once I had the bare bones of my Wimbledon viz (which I will talk about in a separate blog post), I began to wonder what the best background colour would be. I’m not sure if there are many (if any) hard and fast rules or guidelines on the subject, so I asked the question of my peers on twitter. This was the question I posed:

And these were the three prototype dashboards I included with it (click any image to enlarge):

My visualisation was deliberately vague at this point, but my thoughts were that I wanted seven or eight colours for my circle marks. I’d chosen a full range of colours in my palette, and was happy that white showed the marks off well. But were the lighter marks even clearer if I chose black? And what about a third colour – surely the one colour associated with Wimbledon is green – would it be good form to go for a green background?

It’s not possible to do a poll in twitter if your post contains images, so i judged the responses either by replies, or “likes” to relevant replies. So I haven’t quantified the results into scores, results or favourites, but this was enough to convince me that there were several outright preferences for each of the three options.

It was not only great to get the feedback, but some of the thinking behind it too: Here is a collection of responses:

So, where to start?! There were more responses just expressing a preference (mainly for white or black). Green seems to be the most divisive, with many who liked it saying they would consider changing the green. Not to mention those who didn’t like it …

It’s true that all of the options I chose were very full-on in colour: the white is #FFFFFF, the black is #000000 and the green is, well, bright! What the responses did tell me though, as well as there being no right or wrong answer, is to carefully consider the importance of a number of factors:

  • is your choice too overbearing/saturated?
  • does it emphasise the marks colour palette well (moreover, is it important that it does so?)
  • does it detract from other elements such as lines, text or other markers?
  • is there an extra cultural/branding relevance to the background colour?
  • if so, does that matter, or does using it become too predictable at the expense of other factors?

I do like to choose very pale backgrounds – light shades of yellow or orange give a nice cream effect just removing the saturation of pure #FFFFFF white (and light blues/greys a similar effect with a cooler rather than warmer tone). But I’ve done a few sport-related visualisations where I’ve gravitated to a light green grass-themed background without thinking, whether it be golf, football or cricket. I’m not sure it adds anything or is necessary though. And recent Makeover Monday submissions are often themed around one particular country – in these cases we see many submissions where colour schemes go straight to national flag colours. Bright reds, golds and whites for Germany, or bright orange colours for Dutch-themed visualisations. To me it seems predictable or cliché – but it would be unfair of me to call out any particular examples. I’m guilty of it myself, having gone straight for a green-and-gold themed dashboard for an earlier Australia-related example earlier this year:

Dashboard 1-113

I have learned one snippet of advice the hard way: if you are planning graphical wizardry or anything that involves overlaying transparent layers, be they images or graphs, there are only two colours that work perfectly, particularly when using Tableau. Black, or white. This I found to my cost in my recent safari-themed rhino viz. Black on black is black, and white on white is white. But light brown on light brown is darker brown – trying to align different shades of a non-monochrome colour is very difficult! Want to know why the likes of the Data Duo or Jonni Walker  are among the best out there in their graphic style dashboards? Two words: black backgrounds. OK, two more words: incredible talent, but you get my point.

My dilemma started from the fact that I’d chosen a wide ranging “rainbow” palette for my circle marks. On recollection, I wanted the colours to have more of a sequential feel to them, since they were to reflect each round of the Wimbledon tournament. I moved away from my initial choice and on to the coloured scale below. Having made that choice, I opted for a darker background, since I prefer the contrast here to the contrast you would get with a pale background. But that’s no reflection on the advice I was given, since I moved the goalposts myself! I did agree that a charcoal looked better than a more intense black in this instance.

Screen Shot 2017-07-05 at 21.05.47

So I didn’t get my definitive answer in the end. I’d say the answer is “it depends” but if you know and follow the same famous Tableau evangelists that I do, one in particular, you’ll have heard that answer many times already. What I did get was a range of considered opinions and well thought out responses from my peers in the community. Much better!

So, what were the chances of that?!

So, what were the chances of that?!

This weekend (I’m writing this on a Tuesday evening) there’s the deadline for entering the second feeder contest for Iron Viz. Iron Viz is one of the highlights of every Tableau Conference, where three winners of the feeder contests perform on stage in front of 12000 or so people to produce an amazing viz in just 20 minutes. Possibly the most ridiculous display of nerve and talent you’ll ever see in the world of Tableau, a great spectacle that leaves those watching in awe of the three competitors.

To take part on stage would be absolutely out of my league, but there are still good reasons to enter. Firstly, the task of pushing myself to produce a competition-quality visualisation that wouldn’t seem too out of place in a field of 60 or so competitors will be a great experience. And secondly, if it was possible, just possible, to win the feeder, it would be a free ticket to this year’s Tableau Conference in Vegas, which otherwise I am highly unlikely to attend. And as I mentioned here, and here, it was rather good last year, to say the least.

And thirdly, the Safari theme suits me. If you have to find a dataset to work with and spend a long time on creating a viz, then I would always find something you enjoy or are passionate about. I have long been passionate about wildlife conservation, and so when I was browsing the Environmental Investigation Agency website and found some data about rhino horn seizures, I knew I had the start of what I wanted to to. I was about to enter my first Iron Viz. In fact here’s a photo of me from seven years ago … (and yes, the medal proudly displayed on my horn is a London Marathon completer’s medal)


I wanted to challenge myself in several ways. I wanted to emulate some of the best Tableau Public authors around, to challenge myself in the use of more artistic layouts. 100% floating tiles, transparent images to act as backgrounds, use of photo images, long form infographic style, circular insets to name a few things. I wanted to emulate the master of such visualisations, Jonni Walker:

Dashboard 1-107

Jonni’s viz above is one of many he has done on different animal and bird species. It’s so good, as well as looking like it has come straight out of the pages of a magazine, it actually made me want to go out into Derbyshire and look for kingfishers. We talk about a good visualisation having a call to action. There’s no more literal call to action than that!

Fast forward to last Sunday. My visualisation was making progress. I’d got most of what needed including working reasonably but still with plenty to do. And then, a stunning early entry from my good friend Ken Flerlage dropped on Monday. To emphasise, my version below is far from finished! But, see if you can spot some similarities and differences:


So here goes. Ken’s is amazing, mine is unfinished. Ken’s is black, mine is beige. Ken’s has a hard-hitting message, mine includes the same issue but doesn’t tell the full story, instead with more generic rhino info. Ken’s has great text annotations mine doesn’t (yet). Ken’s is polished, stunning and deserves to win … mine, well you get the point But we have both gone for exactly the same story and the same issue. We’ve even used exactly the same datasets from exactly the same sources. We’ve quoted and recommended almost exactly the same charities. We’ve both gone for circular insets and long form dashboards (of exactly the same length!). Given the only guide was “Safari”, it’s uncanny how similar our submissions are.

Ken’s blog is here – it includes some shocking imagery but I make no apologies for that given the subject matter:


The image on the right is exactly how it was the moment Ken got his early entry in – I mailed it to him to show just how close our thinking has been, and so it wouldn’t look overly suspicious if I entered at the weekend with such a similar idea. So what do I do now? Anything I do will either seem inferior or look like it’s copying. I know mine is far from finished – I need to decide what to do at the bottom, I need to sort out text and annotations, I need to finalise the story, I need to update tooltips and interactivity. And I’m not overly happy with the distribution map, since the specks for the  Javan, Sumatran and Indian (greater one-horned) versions are barely visible. A lot still to do, but now I know there’s such an obvious yardstick!

It doesn’t feel right to enter now. I will finish the viz – otherwise this is too much of an excuse not to. And I entered because I wanted to try my first ever viz of this type and highlight an important issue. Thankfully Ken is highlighting this better and more powerfully than I could have imagined! But if I do enter, it will seem like I am trying to outdo Ken’s viz, and I don’t think that’s even possible. I’ll think about this. If there are things I do that are slightly different (do I focus more on the map and the information on individual instances, do I include a video, for example?), is it because I’m trying to gain an edge? If I focus on things that Ken has included too, is this just imitation?

Ken (sorry Ken, you’re getting a lot of mentions here!) is one of the nicest men I know in the community and has often been very supportive and appreciative, so I know he will think there is nothing funny going on. In fact, he’s encouraged me to enter, despite us both having no idea we were working on identical data and issues!

Incidentally, a couple of weeks ago, a fascinating blog was published on the subject of tile maps and maps of Africa, using the recently published data of world countries’ Internet usage.

It was Ken again! Ken has done some amazing work considering many more regular and irregular ways of representing Africa using tile maps (the link to his blog is further up this article). Only during the last phase of his work did he realise that someone else was doing similar work and blogging about this; er, me – here and here! But he didn’t mind at all and nor did I.

The coincidences continue, perhaps they’re not *quite* so coincidental if you consider that there are two of us out in the blogosphere who are both fascinated by Africa, its maps and its magnificent endangered inhabitants. There is certainly room for the works of two keen data amateurs to explore different ways of visualising the continent of Africa, so perhaps there is room for two takes on the same important issue, if only because it gives twice as many opportunities to highlight the organisations that are doing great work trying to combat crime and poaching in this area. Ken’s been kind enough to call me out in his blog and I’m delighted to do the same for him.

I know who I want to win iron viz! That said, as always I’m excited to see the amazing standard of entries and looking forward to many more. I just hope there are no more on rhino horn trafficking, that might just be one too many …


Why do we visualise using circles?

Why do we visualise using circles?

I should clarify – if the books of Tufte and Few are your visualisation bibles then you may not enjoy this post. Rightly so, the two authors I’ve just mentioned are seen as the foremost proponents of the field of creating professional, analytical dashboard visualisations.

But for those who enjoy data visualisation as data art, who appreciate the aesthetics of data visualisation over and above the rigorous application of best analytical practices, then, like me, you’ll know that the data visualisation field spreads far wider. So I was excited to see the publication of a new book from Manuel Lima, the Book of Circles.

I have done a lot of visualisation using circles – more than I’d realised until I came to contemplating this book and the review, with all of the below featuring in my portfolio, visualising subjects as diverse as football results, 1980s music, Donald Trump’s tweets and animal counts in a zoo inventory.

To summarise this book I was able to read it from cover to cover on a long train journey, which means that (a) it wasn’t too text-heavy that I couldn’t complete in a couple of hours and (b) I was captivated, and once started didn’t want to put it down.  If you don’t want to read further than my overall rating, then my key takeaway is that as a result of reading the book, I wanted to do three things:

  1. Read it all over again
  2. Go away and do some (more) circular visualisations, inspired by those in the book
  3. Recommend this book and tell everyone how good it is.

And I’m doing these three things right away, starting with number 3.

The first thing to note is that this leans more towards a “coffee table” book than a technical reference book. The majority of the book reads like a gallery, with full or half-page high quality visualisations and gallery-style descriptions. But, despite this, strange to say that my favourite page is the page below:


The contents page “taxonomises” circle visualisations into seven different types, with different sub-types within them. Instantly from this I can reference those that i’m familiar with and those less familiar with, and can see potential ideas for adapting work I’ve done to fit a new circular visualisation type. It means that if you want to derive inspiration from concentric ring visualisations (for example), you know just where to turn. Though I challenge you not to be drawn in to all the other visualisation types in the book in the meantime. I do wonder though, which one would best represent my concentric circular bump chart? Maybe an idea for volume two?!

The first fifty or so pages are just the right amount of theory to discuss the aesthetics and psychological use of circles over the years before launching fully into the visualisations for the remainder of the book. But these introductory pages still contain many more examples of circles through the ages. Particularly relevant is the explanation of the metaphor of the circle in four ways:

  1. Perfection (simplicity, balance, harmony, symmetry etc)
  2. Unity (wholeness, completion, containment etc)
  3. Movement (continual force, rotation, cyclicality, periodicity, etc)
  4. Infinity (eternity, immensity, etc)

Lima’s examples throughout the rest of the book span art, astronomy, biology, cartography and of course the more recent notion of data visualisation. Allowing for coverage of such a wide range of genres, he intersperses ancient, classic and modern examples throughout to show how our concept of circular visualisations has lasted over the centuries. I’ve toyed with the idea of picking some favourite visualisations from within the book to show you but I’m not sure that’s fair on the original artists. Beauty is obviously in the eye of the beholder and if you read this while leaving preconceived ideas of data visualisation rules to one side, you’ll see several hundred eye-catching visualisations.

Instead, I’ll leave this circular image – the one chosen for the book’s cover, and suggest this would make a  fine addition to the shelves of any data art enthusiast


Do we publish visualisations just for the attention?

Do we publish visualisations just for the attention?

A week or so ago I wrote three-quarters of a blog post about this, inspired by the attention that my Africa tile map viz was getting. I ran out of time and “parked it”, heading off to the Tableau Conference in London

To summarise my first (unpublished) post: I compared a data visualiser publishing her personal work online to a (traditional) artist displaying her work at a gallery. An data visualiser’s collection of work on his blog, website or Tableau Public profile is like an artist’s portfolio. I was pretty sure I had a good analogy going, but that’s what it was, an analogy. And it was an analogy that meant i was prepared to admit that yes, I do publish visualisations for attention, and this contextualised it nicely for me.

The portfolio analogy of the online data visualiser was a simple and obvious one, but a key one that bears repeating. I’ve often felt that a varied and eye-catching portfolio of visualisation work is a huge help when seen as just that – I know of several people whose portfolio, in Tableau Public or otherwise, has actively helped in the process of getting a job (myself included).

But, as I say, I didn’t quite finish that or draw it to a reasonable conclusion. I went to the conference last week, where in the “Box and Whisker Bistro” (one of the many themed rooms in the venue) I saw this:


It’s worth saying, that at my age (and those with a keen eye and an accurate abacus can work out exactly what that is from above), things can come as a surprise even when you’ve had strong hints dropped about them in advance – I remembered there may be such a display, but had subsequently totally forgotten. It’s great – I can watch my favourite films over and again and still not know what happens! Anyway, back to the blog …

So what’s this? It’s a real actual art gallery (for the duration of the conference). With actual art works. And they picked mine, not once but twice. Now, I’ve always loved the idea that for more creative visualisations on the David McCandless side of the spectrum (look away, Stephen Few and advocates) would be great if framed, blown up in size to large installations, and shown as artworks, but I’ve never seen it done. And it’s fair to say, I bloody loved it.

And they’ve even put biographies for each picture (shame about the date of birth) to make it look like I’m an actual proper artist. A nice touch!

Here’s a photo I took of Tableau’s Andy Cotgreave (there were whispers that he’s written a book recently that might be very good – the Big Book of Dashboards and Charlie Hutcheson – a great friend in the community who has quite possibly the best blog for those learning Tableau. Not only does Andy have a great current book, but I believe he was instrumental in the gallery idea. How many chances will there be to meet them both at the same time and get great photographic proof? But instead I shoved them off to the side so I could sneak in a photo of my pictures in situ in the gallery with people looking at them …


So now before I meander onto the rest of the post I should give some context and a big thank you to Tableau. The context: I’m a non-artist. A non-drawer. My artistic talent is off the scale in a minus direction. I came from a school where we had to submit sketches to our art teacher which would be graded 5 points for an A, 3 for a B and 1 for a C. Lowest scores got shamed and put in a league of shame on the wall, with potential detention for those not meeting a certain threshold. (Different times!). My sketches got ticks. An acknowledgement that they were done and submitted, but saved from the embarrassment of a mark lower than C. Not saved from the embarrassment of a place in the hall of shame on the wall though. So that’s some context having been through school last century as “boy least likely ever to get work shown in an art gallery in his life”. So it’s in that context that I sincerely thank Tableau for doing this – it made this middle-aged artistic klutz taking up a new career in data visualisation / data art very happy and proud of his achievements – an unexpected career highlight for which I thank you sincerely!

My pieces were just two out of eleven and I’m honoured to be in the company of the remaining works, see them here:

The remaining artists are Rody Zakovich, Chris Love, Matt Francis, Pooja Gandhi, Rob Radburn, Adam McCann, Mike Cisneros, Ben Davis, Many of whom I have previously cited as my influences, and the rest of whom I look up just as much. Thanks to the biographies, I know there’s a certain irony in the fact that I always look to Rody for inspiration (code for I quite often steal his work, like an artist of course), yet he is a depressing eighteen years younger than me!

So, apart from this being a wonderful ego-boosting surprise example of an analogy taking form in reality, have we answered the original question? My thoughts about publishing data visualisations for attention were obviously more leaning towards publishing (and publicising) online visualisations for all to see – whether on social media, blog posts or areas such as Tableau Public.

There’s a feeling that people are using things like Makeover Monday to make a name for themselves. Or that people are courting attention from new or highly technical visualisation types. Those of us who are not professional data journalists or visualisers (perhaps, like me, they will use tools such as Tableau professionally, but not to produce the kind of work that they will publish publicly) will produce a visualisation, but what then? Do we just keep it to ourselves, or do we publish it and publicise it with a tweet or a posting?

I agree, that’s what people do. I know this, because I do it. For me, Makeover Monday is a great yardstick. Last week, I published my Africa tile map visualisation. And, with no great subtlety, because it’s one I’m really pleased with, I’m going to show it again here.

Dashboard 2-16

Because work published for MakeoverMonday gets published at roughly the same time each week, to the same group of Twitter followers, I know that certain works are getting more attention than others. And whether we court it or not, (positive) attention is the sign of a visualisation that has worked well. I make no apologies for excitedly watching and counting the number of likes and retweets it garnered through the week, because I could draw an obvious conclusion from it: that was a good one, people liked it.

Week 22: Internet Usage in Africa: 125 likes, 28 retweets

Week 21: Drinking Habits of Britons: 12 likes, 0 retweets

Week 20: Youth Employment in Latin America: 27 likes, 0 retweets

Week 19: Car Colours in the Netherlands (seen above, which I’m not sure if I’ve even included in this blog before, and therefore shown below): 44 likes, 3 retweets

Week 18: Sydney Ferries: 23 likes, 1 retweet

Dashboard 1-105

And so on … whereas it’s not a matter of pimping likes and retweets, the reaction of a visualisation in the public arena is as good an indication as any that we have of how well received our work is. I can look at the above, and see that my week 21 submission was largely overlooked. OK – I have to squint through the numbers and it would be much clearer if I visualised it, but you get the point. Week 20 was also somewhat down on my usual figures. I don’t see it as being competitive, but rather see the quantity of interactions as a critique on my work. Weeks 20/21 , could do better. Week 22, you nailed it – you did something good, or at the very least did something that got liked and noticed.

I don’t see it as desperately looking out for likes/retweets and attention from a numbers point of view. I don’t expect anything to “go viral” – I’ve never put anything on reddit and wouldn’t want to. I follow my reactions enough to know that 125 likes for Africa tile maps is my best ever, but I’m fully aware that in the grand scheme of things that’s not exactly a sensation, nor is it likely to get me included in the next Olympics’ opening ceremony. My cousin’s dog, Sprocket, got national media coverage and 3000 likes in a day, and rightly so. He’s the standout picture in this blog post!


The “work/out of work” dilemma is a difficult one – many people use visualisations and hone their skills in a certain direction while using software at work, but often to a corporate standard or a very closed skillset. Publishing “non work” visualisations can take any form – do you hone these skills further to create corporate style dashboards on a variety of open datasets, or utilise your hidden graphic skills to create a work of beauty? But for anyone who does so, who is their audience?

Chris Love wrote here (and he’s not the only one) about the difficulties of finding an audience for personal projects. A new, ground-breaking or artistic piece of work will tend to be published only to your professional or social media bubble. We create visualisations considering the number one rule “know your audience” but for non-client dashboards you can’t determine your audience. I could create a piece all about golf, or pop music, or the Oscars (all of which I have done) but my audience, by definition is not specific to any of those areas. Rather it is those who have chosen to follow me on social media outlets, for whatever reason that might be. So it’s easy to see the frustration, or the futility even, of just releasing work to an undefined audience, with the only measure of reaction or appreciation being in the likes/retweets or equivalent reaction. So sometimes, unless we have a different “way in”, that’s our only option for “non work” pieces. But I don’t think that’s something to shy away from, or deny that it’s what we do.

At the conference, I was asked to give an interview for the Tableau Wannabe podcast. I was delighted to say yes (after all, this is a post loosely related to the humblebrag and pimping for self-promotion, so in for a penny …!) just to give my perspective, and I found to my surprise, that as someone who doesn’t usually say much, that give me a data visualisation topic and I can waffle for England!

I do believe though that if data visualisation is both your hobby and your profession, then the public forum is the ideal outlet for what you create. One of the things that I mentioned, which I have had the most feedback on since doing the podcast last week, was about working on personal projects and working with data you love. It’s already inspired some work such as this by Sarah Bartlett (or, if it hasn’t, and they were going to do it anyway, they’ve been kind enough to credit me). And I think it’s important to work on personal projects, enjoy yourself, learn your skills as you’re doing them, and get them out there! If that counts as visualising for attention, by soliciting reaction, feedback and “likes” then count me in.

Perhaps because I still think of myself as being very much on the steep, early, upward phase of my learning curve, I see no shame in this. We’re lucky to have a thriving online data visualisation community who help fellow participants to develop. And if any of those and my fellow friends in the community are reading this blog post and enjoying it, then I ask ashamedly, feel free to like and retweet the link!



Which shapes work well with tile maps?

Which shapes work well with tile maps?

Last year I posted about tile maps and the fun I had devising one for Africa in particular. My approach up to that point was simple – just devising a square grid, basically based on graph paper and an atlas. Or at least the slightly more up to date version of rows/columns in Excel and an online map.

On that occasion I tried Africa and France, and have since published square tile versions for Scotland and New Zealand using a similar low-tech technique. Last month I tried something a little more complicated, but only a little more, by trying a hexagonal tile map. My choice was Europe (well, Europe and Australia, but then that’s the Eurovision Song Contest for you) with the resulting tile map below:

Dashboard 1-95

Logistics about setting up a tile map notwithstanding, Eurovision is an ideal subject for a tile map. The reason for this is that each country is equally as important as any other, with exactly the same weight given to a vote from (or a performance by) a small country as for a large country. But the first issue with tile maps will always be the disparity in size between countries. There’s no getting away from the fact that in the European example above, Russia and San Marino are the same size, despite the fact that you can fit over a quarter of a million San Marinos in the area of Russia.

The second issue is often the fact that you might not have data for every country/region, whether due to incomplete data, disputes/changes in territory, or small city/state areas which are often not included. My Europe example has many of those issues. I’ve included Andorra and San Marino because they have been included in the contest, but not Liechtenstein, since they have never taken part. I have included Luxembourg, since they have participated in the past, but they have ended up as a blank tile. But not included is Kosovo, only recently recognised as an independent state. This is more common than you might think – does your America data include D.C? Does your UK data consider Bristol as a separate county or part of Gloucestershire? Is Kosovo recognised as a separate state? Is Greenland a separate entity in your data? … etc

The third issue is just one of aesthetics. Does it work? Tile maps aren’t for everyone, and none can be done without a certain amount of displacement and distortion. But is the overall shape/format recognisable as the country or continent it represents? Plainly and simply, does it look nice?

To compile a hexagonal tile map (in my case, in Tableau) the method is very similar. You can even download hexagonal grids if you want to print off and colour your map while planning manually. The only difference is that as a “grid”, some of the columns will be offset by 0.5. This is really well explained in this post by Matt Chambers, aka Sir Vizalot, and so I used the techniques he explains to generate my hex tile map in Tableau.

Last month in Manchester I was offered the chance to speak at a Data Science conference (as alluded to in my last post). I decided to attend the full two days of the conference, partly to acclimatise for my first major conference talk, and partly because it seemed like a great conference, which the organisers had made me feel very welcome to be part of. Scanning through the list of talks, I noticed that Dr. Graham McNeill from the Oxford Internet Institute was talking about how to automatically generate tile maps. He and his colleague Scott Hale are to present a paper on their findings at Eurovis in Barcelona later this month (June 2017). In a conference where a lot of high level mathematics and science was included (my talk, on the second day, was the first not to include equations, algorithms or Greek letters!), this stood out to me as one that really reached my interest.

Graham’s talk explained that he was devising a program/application that would convert input locations to a given tile map, using algorithms where users could determine the most important features. What is most important – overall shape? Positioning? Geographical accuracy? Are there certain arrangements that can’t be broken? For example, he showed several different interpretations of US tile maps, probably the most widely recognised tile maps, explaining that different publications use slightly different “house” versions. Some have the most optimised versions with North and South Carolina actually adjoining east and west, whereas some have North above South, and need to make concessions in other areas. Problems I’m well aware of from my low-tech map generating!

The other thing that attracted my interest was the fact that he was easily able to toggle between four types of polygon: square, circle (essentially the same thing – the arrangement is identical, it just doesn’t tile snugly), hexagon and triangle.

(Wondering how exactly a circle tile map could work? Take a look at this from https://flowingdata.com/2017/05/18/chernoff-emoji/ with further explanation within)


The first three (square, circle, hexagon) I’d seen, and tried, but not a triangle tile map. The talk was therefore exciting on two levels: firstly, is there a method in the pipeline that will do the mapping part for me, better than I can do myself (though secretly that’s a bit of a shame, as it’s the best bit!), and secondly, regardless of the algorithm or not, it completely opened my eyes to the fact that a triangle tile map was an option. We were shown a few examples, some of which looked better than others, but it was quite exciting to think that a triangle map could look good in certain cases.

Graham mentioned that he’d tried two in particular, because he’d seen some good manual versions of France and Africa out online. Cue moment of surprise and the slide below (for those who haven’t followed my blog or clicked on earlier links, these are, of course, mine!). As a newcomer in the field of data visualisation, I was quietly chuffed to see my work (via this very blog) had been noticed and publicised.


So this got me thinking – could we work together to see whether there were some interesting tile maps that would work for data visualisations that I could try using in Tableau? Shortly after the conference I asked Graham if he had some interesting triangular maps to send me that his application had generated.

There were three versions of Africa:


The first thing to notice about triangle maps is that there are two different types: essentially those where the triangles are all on their point/base (so the bases align horizontally), and those where the bases align vertically. The one thing that a human has over an algorithm is their own aesthetic choice. I don’t know which of the three is most accurate, but instantly I saw one of them (the largest of the three images above) and loved it. I prefer the former kind to the latter kind, but that might be because of the longitudinal nature of Africa – perhaps a different-shaped country or continent would suit the alternative alignment better?

The algorithm had done a great job of aligning Africa into a really pleasing shape, but there were three things that didn’t quite sit right. Morocco pointing up out of the horizontal plane in the North, Somaliland poking out to the East, and Lesotho occupying the furthest tile south. Along with the image, Graham supplied me with a row and column number to use to import into Tableau, and one extra parameter, essentially whether the triangle is to point up or down. This is the only difference to a square or hexagonal map, since two separate alignments are needed within the same map in order to tesselate.

Fortunately this is where overriding some of the choices manually becomes an option, and some of the quirks of African nations worked nicely in my favour. First, I swapped Lesotho and South Africa, so that Lesotho sat above South Africa instead of at the very bottom. Perhaps this is the equivalent to the North/South Carolina quandary I mentioned above, but it didn’t sit right without having South Africa at the bottom, especially since Lesotho, though entirely surrounded by South Africa, is not a coastal nation. Secondly, Graham’s map included Western Sahara, which is still considered a disputed territory. The dataset I planned to use was the latest Makeover Monday (you may have heard me mention that before) which didn’t have Western Sahara, so I was able to manually adjust the surrounding countries and smooth out the Northern coast. Thirdly, Somaliland was also not included in my data, and a quick google suggested it’s also not a recognised country.

Finally, the island nations (Sao Tome & Principe, Cape Verde Islands and Madagascar)  weren’t included in the original algorithm. Those just need to be added manually.

So I had my version of a nice smooth tile map and used small multiples to show each of nine separate years. My design choice was to use ochre shades of brown/orange, but much of the rest of my design choices were down to a bit of luck. I struggled to get the triangles to line up, and decided it didn’t matter too much. The added zig-zag lines, as well as leaving a bit of visibility between nations, alluded to African patterns, and really helped the design theme. The resulting image, below, was one I was very happy with:

Dashboard 2-16

The feedback has been quite overwhelming – it seems many of you are appreciative of a new type of tile map not seen before. In particular, Tim Ngenwa in his feedback pointed out the following page https://uk.pinterest.com/sharonlwhittake/african-patterns/ which included African patterns that it resembled, such as the pattern below:


From a Tableau point of view, if anyone was to download my workbook, you’ll see how simple it is. The only slight difference to the work I’ve done previously is that the row/column values are not 1,2,3 (or 1.5, 2.5 etc), but numbers relating to the latitude/longitude of countries (and to several decimal places). But once brought into Tableau, because the points will be shown to scale, and the axes suppressed, it doesn’t make any difference.

Not all tile maps work, and there will be tile maps that work well with certain tiles, but not so well with triangles. With the UK election on the horizon, I’ve been badgering Graham for a UK constituency triangle tile map, but the upshot is, with the algorithm as it stands at the moment, it just doesn’t look very good! The non-smooth geography of the UK makes it a difficult proposition. And 650 constituencies probably make it a leap too far for a manual approach (though I won’t rule it out!) In addition, I’m not a big fan of the US tile map above, whereas the established hex and square tile maps do a good job. But Graham’s France map (below) looks promising! So I’m looking forward to investigating more tile map options.


So expect more tile maps from me in due course – all feedback welcome, I may even consider requests!

Should data visualisations always tell a story?

Should data visualisations always tell a story?

I feel a bit of a fraud asking this question. It’s been asked, debated, blogged and further debated many times before. You can get opinions on the subject from people with much more experience in data visualisation than me. For example: Cole Nussbaumer Knaflic, Alberto Cairo, Jon Schwabish, Georgia LupiChad Skelton, Sophie Sparkes, Robert Kosara to name but seven of many. So do please check out what they have to say, but allow me to put my thoughts here too, even if that just involves collating and arranging the thoughts of the aforementioned others.

So why am I even broaching the subject? Two reasons really. One – I daftly decided to start a blog (you’re reading it) about questions in data visualisation, and what better question? And two – in less than a fortnight I am giving a presentation on data storytelling at a conference in Manchester. I’ve volunteered for this, stepping into the breach to help out a presenter who couldn’t attend, and have started off with obvious fear at what would be my biggest speaking engagement so far. The more I’ve researched, read and listened to many talks on the subject, the more excited I’ve got at the prospect at having a great subject to talk about with so much potential material – I should have no problem talking for 45 minutes about a subject that fascinates me within data visualisation. And yet the more I’ve continued the process, the more daunted I’ve got again. There isn’t overall agreement, there’s no real answer. How can I tell a hall full of delegates what data storytelling involves when there isn’t agreement within the industry?

So here’s a frivolous but pertinent example from none other than Kurt Vonnegut. Known for his modern science fiction classics such as Slaughterhouse Five, Vonnegut also showed how stories can be plotted out on a simple pair of axes:

Ill Fortune to Good Fortune, Beginning to End. In the story of “Man in a Hole” (which requires neither a man nor a hole) the protagonist starts off fine, then finds some trouble, and gets out of it in the end.

In Vonnegut’s diagram of the “Boy Gets Girl” story, the boy finds something wonderful, then something bad happens (e.g., the girl and boy fight), but then he gets it back again.


Two examples of how the simplest and most classic of storylines can be visualised graphically – as (almost) a sine curve. I could flip this upside-down and change the words (if I had the inclination and Illustrator skills), then you’ve got a very true to life story:

  • Middle aged Man prepares conference talk: by Neil Richards
  • Man agrees to talk – is worried and daunted (ill fortune)
  • Gets lots of information, gets really interested (good fortune)
  • Keeps researching, realises there’s an overload of contrasting material and gets daunted again (ill fortune)
  • End

So where to start? Last week, Cole Knaflic, author of Storytelling with Data visited London to talk to the Tableau User Group. I was privileged enough to attend. As you’d expect, Cole explained how visualisations could be improved by treating them as stories with all the elements you’d expect in a work of fiction. Your work of fiction doesn’t have to be Pride and Prejudice, it can be an adventure book written for a small child. But all good fiction stories have characters, a theme, a plot, a point of view, setting, conflict and tone. All these were great pointers to improve our presentations and business dashboards. The result of this approach was to highlight the importance of many data visualisation principles.

In the two hour train journey to London I finalised this visualisation of the French Election first round results.

Sheet 2

Cole mentioned that colour is great – for children’s parties. If you don’t need it, don’t include it. An hour into my journey home, I had the following. It still needed some colour, after all there are four main candidate, but this was far less busy and made every outcome clearer. Much better – an instant improvement, and a story more clearly told.

Dashboard 1-90

I asked Cole the same question as the title of this entry – should every visualisation tell a story? Her answer was “no”. There’s certainly a distinction to me made between explanatory and exploratory visualisations (a distinction I’ve attempted in the past myself). But the key thing is that a visualisation should always convey the message that it’s intended to convey.  Her blog then makes a distinction between a “Story” (with upper case S) and a story (sic). I’ll leave you to explore the differences, but perhaps the conclusion is that every visualisation should tell a “story” (lower case s!)

Jon Schwabish is keen to point out how the term “story” is overused, that many visualisations in fact do not tell stories. I’m not sure whether or not he concludes they *should* but don’t, or just don’t! But I do like Chad Skelton’s interpretation a lot. He takes us away from the idea of a story in terms of a work of fiction.

Fundamentally, the purpose of literary stories is to entertain and the teller of a fictional story has the luxury of making things up to ensure their story is as entertaining as possible.

In contrast, the purpose of data analysis is usually to inform an audience and those visualizing data are limited to plain old facts.

What if those facts don’t stir emotions, or fit into a satisfying story? Trying to make facts “tell a story” could distract from communicating correct analysis. Chad suggests that a “news story” is a far better alternative. Inform rather than entertain – you can’t make something more entertaining than it is!

However, I do think it’s possible to tell an intriguing story (OK, a news story) by careful selection of data. The next examples are league tables – they are not sophisticated or artistic, but they are every bit as valid as an example of data visualisation as a bar, pie, bubble or any other chart you might want to mention. If I asked who was the best football team in England, would you remember the most recent complete season and give the answer as the runaway league winners, Leicester City?

Screen Shot 2017-05-04 at 23.19.10

Or would you say this year’s league leaders and favourites for the title, Chelsea, who have as many points as Leicester did last year, with four games still to go?

Screen Shot 2017-05-04 at 23.19.47

Both are credible suggestions, but if you were to consider the overall results over the last two seasons, a new team emerges – despite not topping an individual year’s league table since the 1960s, Tottenham are a clear 15 points ahead (graphic courtesy of the Sun).

Screen Shot 2017-05-04 at 23.22.25

This isn’t so much a data visualisation technique, but it is a data storytelling technique. And hard to argue, when facing the less conventional third option above, that Tottenham are not the best and most consistent team in England at the moment.

Whatever your definition of a story, it’s possible to make your data tell the wrong story. If you’ve seen this chart before, featured and debunked in Business Insider, you’ll know exactly what’s wrong with it. If you haven’t – I’ll make it easier and put two at first seemingly different charts next to each other …

There are lots of powerful (and, unlike the above, ethical) ways to tell a good story with your data. Whether that’s with a small or large S, these ways give extra impact to your data. And that’s what I’l focus on when I talk about storytelling, not so much about the semantics of the word, which will continue to fascinate. I’ll probably include the above charts and will definitely include very many more, and my aim is twofold: to promote interest in great data visualisation, and to stimulate debate rather than answer questions.

Still terrified though!