An interesting tweet came across my Twitterstream the other day, showing a hexbin scatterplot chart type for Power BI:
Having just presented a session at TC17 on working with dense data where Sarah Battersby and I covered (among other things) hexbinning in Tableau, I was intrigued by this viz type and wondered if it could be created in Tableau. I was a little wary as mixing polygons and points together can be complicated, but I hoped it could be done.
Let’s just say that I’m glad I was bald when I started this exercise because it involved quite a bit of hairpulling. But after a few hours of trial and error and a welltimed break to go sit in the sun and ruminate, I managed to produce this little beauty:
I started with Alberto Cairo’s Datasaurus dataset â€“ a group of datasets that behave similarly to Anscombe’s quartet. Really I was just being lazy as I had it lying around and therefore didn’t need to mock up my own sample scatterplots. The source data looks like this:
dataset 
record id

x

y

dino 
1

55.3846

97.1795

dino 
2

51.5385

96.0256

dino 
3

46.1538

94.4872

dino 
4

42.8205

91.4103

dino 
5

40.7692

88.3333

dino 
6

38.7179

84.8718

dino 
7

35.641

79.8718

â€¦ 
â€¦

â€¦

â€¦

With the data in this format there are two approaches for generating the hexbins â€“ one uses densification to generate the polygon vertex records, and the other generates them through a join to a scaffolding table. I opted to use the scaffolding approach as a) I have a manageable amount of data and b) it makes life easier when you have hexbins that contain just a single point. The scaffold table looks like this:
And the join of these tables in Tableau looks like this (the join simulates a Cartesian product of the two tables):
The result of this is 7 rows of data for each point on the scatterplot:
I’ll use one of these (PointID=0) to plot the actual point location, and the other 6 to plot the hexagon shape. I’ve blogged on several occasions on how to generate a dynamic hexbin polygon and we’re going to use the same techniques here:
Generate the hexbin center point:
[HexbinX]: HEXBINX([X]/[Hexbin Size], [Y]/[Hexbin Size]) * [Hexbin Size]
[HexbinY]: HEXBINY([X]/[Hexbin Size], [Y]/[Hexbin Size]) * [Hexbin Size]
Generate a unique identifier for each hexbin. As you may know, I’m an advocate for efficiency so I use a numeric function for this (based on Cantor’s pairing function) instead of a string function:
[HexbinID]: ([HexbinX]^3 + 3*[HexbinX] + 2*[HexbinX]*[HexbinY] + [HexbinY] + [HexbinY]^2)/2
Generate the actual plot points keeping the original location when PointID=0 and using trigonometry to generate the hexagon vertices when PointID=(1..6):
[PointType]: IF [Point ID] = 0 THEN 0 ELSE 1 END
[Angle]: (1.047198 * INDEX())
[PlotX]: IF MIN([PointType]) = 0 THEN MIN([X]) ELSE WINDOW_AVG(MIN([HexbinX])) + [Hexbin Size]*COS([Angle]) END
[PlotY]: IF MIN([PointType]) = 0 THEN MIN([Y]) ELSE WINDOW_AVG(MIN([HexbinY])) + [Hexbin Size]*SIN([Angle]) END
We can now start plotting our viz â€“ first let’s just get the points up:
You can see that the blue marks are the original data points and the orange points are the vertices for the hexagons. Because we want two marks types (a polygon and a point) we need a dual axis chart:
We need to isolate the orange marks on one side and the blue marks on the other. We can’t filter them, so we have to make some clever use of the “hide” function. I duplicated the [PointType] calculation from before so I can use one to colour one axis and the other to colour the other:
We then hide the marks we don’t need on each axis (rightclick on the colour swatch in each legend and select “Hide”):
We can now make the hexagon marks on one axis, and circle marks on the other. Tidy up the colours and other formatting:
Finally, we set the axis to be “dual axis”, synchronise and hide the unwanted top axis, and voila:
The last couple of steps I put in were to a) colour the hexbins by the number of points they contain, b) tidy up the tooltips for each mark type, and c) set up a hover action to highlight the elements in a hexbin:
This ended up being quite a challenging viz and required quite a few techniques to get it done. But being able to do it at all reinforces for me that an expressive presentation model that allows you to natively create complex chart types (i.e. the Tableau approach) is faster and more reliable than a model where you are reliant on a developer to write a custom chart widget (i.e. the Power BI model). Even accounting for the trial and error needed to nut out the final successful method, Tableau allowed me to achieve the result much faster than a solution based on coding.
And of course, now that I know how, I can reproduce this solution in minutes.
You can download the workbook from here. Enjoy.
PS. I couldn’t help myself. The workbook now includes solution examples using both the scaffolding and the densification approaches.
It was a mental itch that needed scratching.