How important is interaction in visualisations? (aka Where’s Big Sam?)

How important is interaction in visualisations? (aka Where’s Big Sam?)

Last week, I saw this visualisation tweeted by Gregor Aisch of a visualisation he produced for the New York Times.

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I really like the look of this – striking, simple but quite unique-looking. The visualisation aimed to show that Angela Merkel’s latest election victory cemented her status as the longest-serving EU leader and compare her against other European leaders since 1990, highlighting all those currently in post.

As with a lot of my visualisation ideas, some of my best inspirations come while walking the dog. A couple of days after I saw this viz, I thought: why not recreate this in Tableau? If I can recreate using football data (so often my go-to data source of choice) then I could look at manager tenure and I’m sure I could show a very similar range of data, with the managerial reigns of Wenger (current) and Ferguson (recent past) really standing out. Sure enough – I created a viz I was pretty pleased with. As is usual in this blog – do click through for the interactive version hosted on Tableau Public:

Lexis chart-3

The original visualisation, I believe, doesn’t feature any interactive elements, as it is intended just for NYT readership. So I didn’t worry too much about that in my version. I could have included the ability to filter or highlight based on choice of club, or manager. But I was well aware that most of the labels for the individual managers have been suppressed because of room, so at the very least I wanted to include more information on the tooltips. I actually really liked this – the obvious similarity with the NYT graphic, the fact that you could see Hodgson, just a couple of weeks into his job, but that others didn’t have their labels showing – a little air of mystery hidden behind the tooltips. If you want to find a manager that isn’t immediately visible then you can hover on any line to find out. And if you can’t remember who Lloyd is, or you don’t know who Redknapp was managing in his first long stint ending in 2001, you can find all this further information there too.

I tweeted this image, with due credit to Gregor and the NYT original. Now I’ve admitted on here before that I keep half an eye on likes and retweets, for no more scientific reason than it’s good to know what is being well-received and what isn’t being noticed. And I was delighted to see the attention that this was getting – dozens of likes and a handful of retweets meant that it was doing very well.

But several hours later I realised the views count on my Tableau Public viz was still in single figures. It wasn’t moving – the only views were my own. Unlike the image above, I hadn’t included the accompanying Tableau Public link. Sixty people had “liked” my viz, and not one of them had bemoaned the lack of an interactive version. They didn’t care that they couldn’t see all the names or find out individual details of each managerial reign. They liked the overall look and the standout message from the visualisation., and that was enough. Eventually I resent a tweet with the interactive link, but the majority of the exposure to the visualisation had come from the first, linkless tweet. As I write, it’s of only two of my tweets ever to have more than one hundred “likes”, and the overall number of impressions is the highest ever, at over 25000. I know it’s not Gangnam Style, but it’s all relative …

Now there are lots of reasons for this – whether it’s my second “best” ever, or second most eye-catching ever, is debatable. I can tell from the reaction that it was shared around a few football twitter accounts, hence it was particularly popular among football/analytics enthusiasts. Perhaps because it’s been popular amongst a less vis-savvy audience there hasn’t been such a call for the full functionality of an interactive viz.

My story is a classic example of the way many visualisations are consumed at the moment. If you can spot a visualisation that catches your eye in your twitter feed, among all the gossip, hype and Brexit/Trump hysteria you might also see, you will usually only have a couple of seconds to consume the visualisation before moving on. People don’t have time to click through and interact unless (a) they really like the visualisation, (b) they have time and (c) there is a call to action to do so. Key word in the last sentence is “and” – in my experience it tends to take all three requirements. If the potential viewers make the decision that they don’t have all three requirements, in two seconds the visualisation will be gone.

 

Back to the chart – a little googling suggests that the chart with parallel diagonal timelines is known as a Lexis chart. So in the interest of showing off a bit more about the data without having to dive in, here are static images of two further iterations:

First – one line per manager (instead of one line per job) – see how the lines have changed for managers such as Benitez, Pulis and Coppell who have managed several clubs over the course of the Premier League. (NB – there’s Big Sam, between Hughes and Pochettino, much of his career has been at Bolton, not included in this dataset because they are no longer a Premier League team).

Chart by manager

Second – a small multiple version showing one chart per club.

small multiples

Not many of my blog posts go past without referencing the community project that is Makeover Monday. In this case, as the project grows in popularity, the organisers Andy Kriebel and Eva Murray have to see 100+ visualisations every week (and not as part of their day jobs). The reality is that they won’t have time to click through to many visualisations, if any. More and more it becomes clear that interactivity only has its place in certain situations – but as a default we need to learn to swallow our pride and accept that a visualisation can either be a visualisation of differing layers which can be seen, liked, even “liked” without the viewer seeing everything that’s there. Or, keep it simple and static!

I’ll always enjoy producing the full range of visualisations, from a static table to a swirly data art piece (my words!). A propos of nothing – my latest visualisation for the aforementioned Makeover Monday project. Simple, to the point, and static, right back to first principles. No link included, no click through needed.

Dashboard 1-120

The title and implied causation is slightly tongue-in-cheek, but the design is such that no further information or interaction is needed. Maybe the way to go?

 

 

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How should you frame the title of a visualisation?

How should you frame the title of a visualisation?

It’s probably not ironic that I’ve changed and rewritten the overall title of this blog post probably a dozen or more times. It’s true that the theme for this blog is that each title poses a question, so that’s a very specific (and probably unnecessary) restriction. But I’ve had many other stabs at this title, some of which include:

  • Should a visualisation’s title always state its findings?
  • Should a visualisation’s title always be a statement?
  • Is it OK to pose a question as a visualisation’s title instead of answering one?
  • What should you include in a chart’s headline?

If it’s going to get in this blog, then the overall title always *asks* a question. But a standalone visualisation should always be titled. Even the most ingenious and impactful visualisations can be improved with the context and explanation that a title brings; many are useless without one.

In many ways this follows on from my last post and previous ones before it about stories and goals. The subject of this blog comes from a visualisation challenge set by Cole Knafic in her blog post where she states that she had a number of reasons for concern, but challenged the readers to submit their own makeovers before she published her own. Here’s the original visualisation from the Economist, with title included:

Hurricanes in America have become less frequent

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My last post about the point of visualisations has spurred me to take my own advice of setting out my objectives for any given visualisation. So here’s my take on the original visualisation:

What works well:

  • Concise, compact layout
  • Using decades smooths out yearly random distribution
  • Distinction between stronger and weaker hurricanes works well

What could be improved:

  • Axis labels are a bit of a jumbled mess
  • Vertical axes on the right is unconventional and axis lines are obtrusive
  • “All hurricanes” trend is in the blue text designating category 1-2, which is confusing
  • Is it valid for the two trends to overlap (would have more validity to compare categories 3-5 versus 1-2 rather than versus the whole)
  • Stacked bars make it particularly hard to compare the lighter blue categories year to year
  • There might be visual upward and downward trends, but can we state this with certainty or draw conclusions from it?
  • The final decade is only half-present. While that makes sense, it’s a bit out of place
  • There’s no explanatory text (although we don’t know what may have been included in an accompanying article, the viz is likely to be disseminated on its own, as indeed it is here!)

My goals for a makeover:

  • Find a story in the data
  • Remove gridlines
  • Make horizontal axis more legible
  • Discuss trends but don’t state them as fact
  • Use a different visualisation type that allows for easier comparison of all hurricane types
  • Include more explanatory text
  • Give more consideration to the title and whether we can state findings there
  • If I’m going to include the incomplete decade, remove it from findings or make it clear that it’s not incomplete.

Before I move onto my makeover, a word about the first point: “Find a story in the data” – wait, don’t I keep blogging about the fact that its not always necessary? In this case it’s  specific goal. It doesn’t have to be, but we are making over a viz from a news outlet: its job is to tell a story. What I want to do is find whether that “story” is there and whether it’s true, but then consider how we can frame this. Can we assert our findings as fact?

Here’s my makeover:

hurricanes-3

(edit: minor change to image since first publish, iterating on feedback about orientation of axis labels and left-aligning title)

Mission accomplished in terms of my goals. In separating the two grouped categories out of the stacked bar chart form we can easier compare numbers of hurricanes in each category from decade to decade and see trends. I state the noticeable change in difference between strong and weaker hurricanes, and call this out by separating my chart with colour (yes, they are hues of red and green, but they are colourblind-safe!)

Andy Kriebel advises that it’s good to phrase a chart’s title as a question, and that’s my biggest takeaway from this exercise, perhaps the biggest improvement. I don’t say that we are getting fewer hurricanes, or that the hurricanes we are getting are becoming stronger by comparison, because I’m not comfortable stating that. I’m a statistician by training and there’s no rigour or analysis to back the assertions up. But what I can do is present the data in such a way that I make it easy for the reader to explore and come up with their own conclusion, and that conclusion will probably be similar to the conclusion as stated by the Economist, but if a reader wants to conclude that as fact, they’ll need to explore, experiment and research further.

In framing a title as a question we remove any liability in stating something that can’t be proven. My explanatory text mentions the words “it seems” or “appears to”, and perhaps these ploys are more the tactics of an exploratory, rather than an explanatory visualisation; the types of visualisation that rely less on a story, but still require their own points and goals in order to be viable.

Finally, this is only a remake and if I were to look at my version I would agree that my own could be improved. Is a dumbbell chart the most intuitive for users? Is my explanatory text too wordy/repetitive? Should my vertical axis text not be vertically aligned? Iteration and practice are still key, and expertise some distance away!

 

So, what’s the point?

So, what’s the point?

Another blog post – another question. But this one might suggest that I’m losing my love of data visualisation. Losing the momentum that drives me to produce lots of work. An introspective question it might be, but don’t worry, it’s not an exasperated cry for help. Let me explain.

Earlier this year, not for the first time, there was a lot of talk about storytelling in data visualisation. I read about it extensively, I blogged about it here, I was invited to give a conference talk about it (and I did just that). I spoke about storytelling as a narrative, as an anecdote, as a news story, and spoke a little bit about the pros and cons of each definition. And then mostly, my talk became: “Who cares? Here are some great visualisations.”

But recently the debate has been raised again. Alberto Cairo tweeted this …

Is he right? He usually is. I tend to think that if it’s open to so much discussion, interpretation and misuse then there must at the very least be caveats in the use of the term. Are so many others wrong, in different measures?

Then recent MakeoverMonday correspondence got me thinking. I’m always full of admiration for Andy Kriebel and Eva Murray running the project, because of their weekly commitment to (a) sourcing and preparing data (b) delivering it to the community (c) creating their own visualisations (d) blogging about the thought processes behind their own visualisations, (e) offering constant advice and feedback and (f) weekly recap with advice and favourites. There’s probably at least a (g) to (z) that I’ve either forgotten or didn’t even know about, their commitment is amazing. And I don’t always agree 100% with their advice in (f) but I know even if I don’t they are usually right.

But it occurred to me that the oft overlooked part of the above is (d). The community have very little to do: download, create, tweet, refine if they wish. And learn every week. But in amongst all the work needed, Andy and Eva still always do (d). Their vizzes may be among the simplest each week, but they are always instructive, and they will always say what they wanted to show, what question they wanted answering, and how they decided to viz as a result.

For example (excuse the paraphrases here – full versions and visualisations on the makeovermonday website

Andy (week 37 – UK bike theft), gave his goals as a list of questions he wanted to answer:

  • What are the worst areas in the UK?
  • Is bike theft increasing or decreasing overall and in specific areas?
  • Are there as few positive outcomes as it seems?
  • Where should I avoid locking up my bike?
  • Is there any seasonality in the data? My hypothesis is that the number of bikes stolen would reduce in the winter months.)

And then he created a visualisation that answered them. You can’t argue against that being perfect preparation for a good, successful visualisation.

Eva (week 30 – how thirsty is our food?), gave a list of goals, showing her deliberate intention to focus on a specific issue and keep it simple:

  • Mobile design
  • Focus on two food items: Starchy roots (potatoes!!!) and Bovine meat (dead cows and calves)
  • incorporate the story of Chris Voigt, who ate nothing but potatoes for 60 days straight, to contrast the differences between what you can consume on a plant-based diet and what you surely wouldn’t attempt to do with meat; include just numbers, bars and text

These aren’t stories, but they perfectly set out the point, or the goals, of the visualisations. Sometimes you can over-think to the extent that it seems they are pulling a small element of information from a rich dataset. So you may or may not agree they are telling a story, or at least the full story. But they tell you the point of their visualisation.

Another person whose work, and way of working, I admire is Colin Wojtowycz. A recent starter, he takes time to curate his visualisations but always explains what he is trying to do, and in doing so offers great learning and expands on the visualisation. Results are always good, but richer for this. you can read about his process here, and the point behind his visualisation here.

https://datawoj.co.uk/2017/08/10/how-to-maximise-the-impact-of-your-data-visualisation/

Colin’s points include maximising impact (the main point), improving on weaknesses in the original visualisation and finding a story. The latter being just one of the objectives in a series of points.

So you can have the best intentions of telling a masterful story but the minimum (and often all you need) is a point to your viz. Otherwise it is a waste of time and effort. The converse to this is a viz where data and/or charts are just “thrown on a page” because it was possible to do so. Viz without a point can be confusing, non-memorable, and, well, pointless.  And pointless is no good unless you’re on a gameshow.

p01l4g01

I want to start with an example of mine which is a bit like storytelling in reverse – a viz inspired by a story. Here’s my visualisation on the Ski Jumping long hill competition from Calgary 1988 (click image for interactive version).

Screen Shot 2017-09-18 at 09.19.39

There’s a real circular argument to this – the viz itself was inspired by watching the “Eddie the Eagle” film, a recent film inspired by the exploits of Eddie Edwards, the plucky British competitor who finished a distant last. So I decided to visualise the data. Is this storytelling? There are plenty of arguments to say that it is, but then even I’d be the first to admit that a one-page visualisation doesn’t have the same storytelling appeal as a two hour Hollywood movie (even if a certain amount of “poetic licence” is used in the storytelling of the latter). But there is a point. The points behind my visualisation were

  • Show the disparity in distances including the impressive winning distances and the large gap to Edwards at the back of the field
  • Demonstrate the trajectory of each jump in relation to each competitor
  • A personal, technical point: experiment and demonstrate that I can trace out an arc that is a little different to what I’ve done before.

Although it does each of the three things above, it deliberately backs away on focusing solely on Edwards. That would be more like the “Hollywood” story. But I was delighted by online feedback from Matt Francis (and backed up by visualisingdata.com’s Andy  Kirk)

I was particularly delighted, not just because it’s great feedback, but because that’s was exactly what I was trying to do. In this case, arguably the data tells the story without me doing so – I’m crediting my audience with being intelligent enough to find it (it’s not difficult) without the need to make it my story. That’s the point (pun intended!)

To reiterate, it’s not always necessary to state your point in the viz or the post/blog promoting it – it might or might not be obvious but so long as there was a point driving the viz it should help it improve impact or insight to the desired audience. Here’s another one of mine (again, links to interactive version):

Dashboard 1-117

I had several points here

  • Try an area bump chart but on a dataset where it’s relevant and able to show interesting trends.
  • Show that although Western industrial economies have been more stable on CO2 emissions in recent years, the overall total is continuing to grow.
  • Highlight an important environmental issue

I think I succeeded – (a) proved to myself and a few interested parties that I could do this clever new chart type well (albeit by copying/adapting the great work of those before me who had devised the concept in Tableau), (b) the visualisation achieves this because the chart type does this nicely and (c) is done not just here but in a wider context by VizForSocialGood

VizForSocialGood is an amazing project which deserves more than the time I can give it. Go there. If you research the brief and immerse yourself in the project, your viz will have a point. A very good one. Really recommended for involvement with great datasets that make a difference. If you are looking for a genuine point behind your visualisations as well as an opportunity to make a difference for some high-profile organisations (and some equally important low-profile organisations that need help) then it’s the best place to start.

Professionally – my work involves the production (and reproduction) of dashboards which are produced as proof-of-concepts from teams of Higher Education professionals, with a wealth of data sources at their disposal. Every project and dashboard (or set of dashboards) produced requires a user story (for those of you who know Agile methodology you may recognise the term). Despite the use of the word story, this is not what I’d consider true “storytelling”, but  it’s an excellent way of predetermining exactly the point of the visualisation. Like the examples from MakeoverMonday above, it encourages the participants to set out the goals of their visualisations, the questions they are hoping to answer as a result, and the context and reasoning behind these goals.

A composite / generic and fictitious example of such a user story might be :

As a chief planner, when preparing courses, I want to see the most common destination industries for my students and those of my competitors, so that I can tailor my courses appropriately.”

This approach (though I can’t back it up visually with examples in this arena) sums my argument up personally. We’re never looking to tell a story, but we have goals of data we want to show and explore, and questions we want to ask. A point to the whole visualisation, story or no story.

And finally … what about “data art” type visualisations? Those kind of visualisations that may not have an obvious story behind the data, but attempt to catch the eye and focus on beauty rather than function? (I have a whole different, abandoned blog post on that – it’s such a well-discussed argument that I felt I had very little to add, and my influence is far too small for Stephen Few to notice, or care, that I think he’s wrong!).

Well, what about them? In their case, the art itself can be the point. The discussion point. The aesthetic point. Here’s a recent Hitchcock-themed visualisation I created.

Dashboard 2-26

The principal point of this wasn’t an analytic point or a story to tell, but to demonstrate that iconic film art can influence visualisation in an engaging way just the same way as record cover art which is a subject I’ve had fun with in the past. OK, as it happens, in this case, there is an additional point to this – to highlight gender disparity in earning power between Hollywood males and females by the relative size of circles. But you could argue (easily and successfully) that there are better ways to do the latter, though it at least formed the analytical half of my “point”.

So my conclusion is not to take a particular side on the storytelling debate, but to state that every visualisation should have a point: to answer a question, to provoke further questions, to tell a story, to demonstrate a skill, to create impact, to promote an issue,  to achieve a goal, or whatever that point might be.

Do that well and you should feel that your visualisation is worthwhile. If your release of a visualisation leaves you a little “flat”, revisit and determine whether it really had a point (and whether that point was obvious). If not then it will probably come across as just data thrown on a page. Pointless.

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)

d47f34a612983fa65f98b5da356997ff

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

https://eagereyes.org/blog/2017/joy-plots

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.

joyplot

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).

joyplot

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?

Depeche

 

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:

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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.

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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)

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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:

http://www.kenflerlage.com/2017/06/the-killing-fields-rhino-poaching.html

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:

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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

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