It’s the holiday season, so why not amp you your vizzes’ holiday spirit by adding some bell curves to your histograms? Also, I just recently came across this request in a customer meeting and thereby discovered how easy that is to do. The most difficult part is wrapping your head around what a normal distribution is (please resort to Wikipedia for that), how it’s calculated (I literally stole the equation from Wikipedia) and how to translate that into a Calculated Field in Tableau. The rest is a simple dual-axis chart, a parameter and some rather basic Tableau techniques that need your attention.
“R you nuts?” is what my colleague asked me when I once proposed this little hack. He’s not completely wrong, we’ll get to that later…
The task I was presented with was to embed the graphical output from an R package in a Tableau dashboard. Of course it’s possible to run R code from within Tableau Calculated fields, you can read more about it in official Tableau resources here, here, and here and also here on my blog. But part of the game is that there is only one vector of data being returned from the R session via Rserve into a Table Calculation in Tableau. So what about some of the complex graphics R can produce? Sure, you can try to rebuild those natively in Tableau based on the data returned from the code. But what if a) you’re too lazy to do that (and also it’s all just about rapid prototyping something anyways), or b) the visualization is just too complex (think 3D brain models)?
This is the continuation of a blog post I published a few weeks ago on how to draw directed arrows in Tableau. The approach introduced there – I dubbed it the “linear” approach, as instead of drawing one line we created two additional lines for the arrowheads – works fine on scatterplots, but things turn out to be a bit more difficult when working with maps. This article shows how these difficulties can be overcome using some on-the-fly reprojection of our data. While I claim the arrows to be my original idea (at least I didn’t find anything similar on the web – please correct me if I’m wrong!), I can’t and won’t take credit for this one. All original work was done by Alan Eldridge and the @mapsOverlord herself, Tableau’s Sarah Battersby in an article on hexbinning on Alan’s blog back in 2015. Sarah is an absolute expert on all things map projection, as she has shown time and time again in articles on the topic on the official Tableau and Tableau Public blogs and elsewhere (as in: real scientific publications).
In a recent blog post I showed how easy it is to create maps in Tableau showing paths, basically lines connecting two points each: the start and end locations. Those can be departure and arrival airports of certain flight routes, origin and destination of refugee flows, source and sink of money transfers, … the possibilities are endless!
But now imagine a map with a line connecting two locations A and B. Or rather many such lines. What information does this hold for you? What insights can you get out of such a viz? There is one very important element still missing! That is: which direction is this connection? Sure, there are cases where direction doesn’t matter, but thinking of the three aforementioned example use cases, many times it does! So let’s give our connecting paths some directionality. Let’s take simple lines and make them arrows!
I had been planning to write this post for a long time. Not only have I been asked many times how to do this in my daily consulting work, but especially during and after my hands-on training “Stretching the Boundaries with Advanced Mapping” at our Tableau Conference On Tour 2017 in Berlin earlier this year. The question is pretty simple: How can I draw paths in Tableau? Oftentimes these are some kind of movement data, e.g. refugees or flight connections. The way to do this in Tableau is actually very easy – and some of the recently introduced features made it even easier – but it’s imperative to understand how Tableau draws lines and how the data therefore needs to be structured.