Marimekko in Tableau


Marimekko in Tableau

by Rob Austin

This Marimekko chart was fun to get right. There are some great examples of complex table calculations built in to it. The use case is similar to that of a series of stacked bars. It has graphical similarities to a treemap but it lacks the visual superiority when dealing with large numbers of members; however it does have the advantage of defining the positions on the x-axis, hence it is more structured and can be more useful than a treemap or stacked bars in this respect.

To read the chart think of it as being a stacked bar chart where the colours (y dimensions) show the split within each of the vertical bars. The width of the bars represents the overall share of the x dimension. Try Changing the x and y dimensions to see what happens.

One would typically use this type of chart when dealing with data which has 3-12 different members in a few different dimensions. It is great for seeing where the overlaps are. When trying to to this type of analysis with other chart types you will either get an impression of the results without easy detail (e.g. stacked bar / heat map) or you will get the detail without a real overview (e.g. scatter plot). The Marimekko chart gives a really nice compromise in the middle.

I have added a 5 part blog on the building of this useful chart type in Tableau:

Part 1: Polygons, Paths and Custom SQL contains information on data preparation and the methods used to build.

Part 2: Parameters and Table Calculations introduces some good ideas for flexible charting and the use of table calculated fields. Here we use a variable width stacked bar chart as a simple kind of Mekko.

Part 3: Table calculations delves deeper in to the table calcs needed to create a Mekko and shows what the data looks like.

Part 4: Basic Marimekko is our first go at creating the Marimekko.

Part 5: Totals is where we add in the optional extras such as total bars which help the viewer understand the visualisation. We also look at a method to avoid the need for data densification.

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Rob Austin

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