The Joy Is Wherever You Find It
This blog is effectively a response to that of Neil Richards yesterday, who examined the recent phenomenon that is the joy plot. I can’t do a reply the required justice in a series of 140-character tweets, so here’s a slightly more long-form version.
First of all, if you’ve not already read his post on the subject, I implore you to do so. It covers off a good explanation of joy plots as well as a walk through of his own take on one. The key section for me, however, is this excerpt:
…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…
Neil also refers to the aesthetic advantages and analytical disadvantages of this chart type – a sentiment that I happen to agree with in part but would argue is incomplete. I think it could be better phrased as analytical limitations, since whilst these joy plots aren’t brimming with insights, they might well be sufficiently provocative to the analytical mind as to prompt a new, analytical perspective. That doesn’t seem very clear when written down, so let me try an example. Here’s a static version of Neil’s joy plot (interactive link):
There are no axes shown here. There are no labels on the plots either, so I don’t know which player was playing when. However, there is enough information to reveal some interesting characteristics. The footnote to the chart helps us to navigate a little and we can observe periods of dominance by individual players followed by periods without success. We can see periods where one player appears to have dominance over the rest of the field and other periods where many players are battling with each other for supremacy. One particular feature that provoked my interest was the occasional dip below the zero line – I interpret this as a year when the player lost more games than they won. Knowing that it is possible to lose more games than one wins in a single match, but to still win that match has led me to consider the players who are occasionally able to succeed through matches without having a favourable win/loss ratio, and whether there is something interesting in looking at the most ‘successfully bad’ players.
So, whilst I take little direct insight from Neil’s plot itself, if this data is a subject of my interest there is still plenty there for me to get stuck into. The same could be said of many design-led data visualisations, and I touched on this a little in last week’s post about David McCandless’ talk on his own work. I don’t produce visualisations myself with aesthetics foremost in my mind, but I can appreciate them and they definitely have a role to play in provoking further questions. One of my often recycled claims about Tableau to non-users is in its ability to generate subsequent questions, and alternative or creative visualisations can definitely act in this capacity.
As a more analytical vizzer myself, I look at the relationship Pablo and I have in coming at problems from different perspectives and helping to provide each other with alternative angles that we might never have otherwise encountered. I’ve not yet produced a joy plot and might never do in the future, but that isn’t to say that I cannot appreciate them, or that they’re of little or no value. Not every joy plot will carry insights, but nor does every bar or line chart. And so long as the data is accurate and I know how to interpret what is shown in the chart, I should never rule out the value of something that is, by its own admission, primarily aesthetic.
If sufficient numbers of these new joy plots are generated, sooner or later there’s also the possibility that someone will stumble upon a really practical use case. So long as we are honest and true about what their analytical limitations are, that shouldn’t prevent us from utilising them, right up to those limits.
Cover image for this post taken from http://feelgrafix.com/938285-joy-division.html