Viz-in-Label Treemaps in Tableau

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I’ve been experimenting with Viz-in-Labels lately like in my 9M Profit Bridge on Deutsche Bank’s financial report data or my custom treemap on Tableau’s superstore data.

Deutsche Bank 9M Profit Bridge https://public.tableau.com/profile/klaus.schulte#!/vizhome/DeutscheBank9M2019ProfitBridge/DeutscheBank9M2019ProfitBridge
Custom Treemaps in Tableau
https://public.tableau.com/profile/klaus.schulte#!/vizhome/CustomTreemapSuperstoreSales/CustomTreemap

Viz-in-Label (or Viz-in-Marks) is a technique to visualize more than one data relationship at once. In both visualizations above, the chosen primary chart types (waterfall and treemap) are visualizing part-to-whole. In the first example, little sparkbars show change over time, in the second example horizontal sparkbars show ranking/magnitude of sub-categories. In both examples viz-in-label give additional context to the primary chart. Unfortunately however, unlike viz-in-tooltip, there is no such functionality in the Tableau standard.

This post will guide you through the steps to create one of the examples, the custom treemap with viz-in-label.

1 Data

This example is using Tableau’s superstore data. I aggregated sales on sub-category level and included category for additional context and saved this data to an excel file.

Superstore Data

While it isn’t possible in Tableau to create custom treemaps out of the box, I had to get the treemap coordinates from outside Tableau. Therefore, I uploaded the data to R and used this little script to get my coordinates:

library(ggplot2)
library(treemapify)
library(readxl)
library(writexl)

# Getting Treemap Coordinates ----

superstore <- read_excel("Superstore_data.xlsx")
superstore

treemap_coords <- treemapify(superstore, area="Sales", start = "topleft", subgroup = "Category", subgroup2 = "SubCategory")
treemap_coords

write_xlsx(treemap_coords, "treemap_coords.xlsx")

Next step was to bring my data back to Tableau and it eventually needed this data model to execute my idea. No worries, I will guide you through this model step-by-step.

2 Self Join

First step was to join the superstore data with itself.

This allowed me to create the little sparkcharts at a later step. For demonstrating purposes, this is what it looks for example like, when you bring in both Sub Categories and Sales from the second table: 17 identical charts for each Sub Category.

3 Creating the Sparkbars

To place my sparkbars, I’m using the Get-Rid-of-the-Grid technique. To do so, I had to calculate some xy-coordinates based on my treemap coordinates:

I wanted to place my sparkbars to be placed in the top left corner of each area. Therefore, I calculated the minimum x and maximum y depending on the level (Parameter “Level” 1 (Category) or 2 (Sub Category)) I wanted to display.

For the y-values (I had to do the same with min for the x values)

3.1 Y-Coordinates

Starting from Ymax I subtracted for each member of my selected level (category/sub category) a fixed height for each bar based on a parameter Height, the maximum number of possible bars which is the distinct number of sub-categories, and index of the selected level. The parameter ‘PositioningY’ helped me to place the charts at a later stage.

3.2 X-Coordinates

For the x-coordinates I started from my calculated Xmin and added Sales as a percentage of total (Sales_rel). I implemented a parameter Length bars to play with the length of the bars and again a parameter ‘PositioningX’ to place the charts at a later stage.

3.3 Building the View

I wanted to have the view switchable between Category and Sub Category level, thus I first created two dimensions Level 1 and Level 2:

After bringing these two fields on detail, x on columns, and y on rows, I just had to set my index-table calculation accordingly to get a first result:

Then sorting the Level 2 dimension based on Sales in descending order and we’re almost there.

For a first interim result (we have to get rid of this step again at a later stage) I’m creating a combined axis with a second x (spark) to define the 0-positions for the sparkbars.

Adding a boolean calculation to color the bars, …

…changing the mark to lines. and putting measure names on detail gives us this for sub categories…

…and this for categories.

Nice!

4 Creating the Treemap

4.1 Data and Calc

First step to create the treemap is to densify the data. I therefore created this simple model data set and built the Cartesian product with the data already in.

In addition to Xmin and Ymax which were already calculated in step 3 I also needed to calculate Xmax and Ymin the same way.

Then x (tree) and y (tree) could be calculated with simple CASE statements.

4.2 Creating the View

With x on columns, y on rows, Level 1 on details, PointOrder on path, and selecting the polygon mark, putting together the view is pretty straight forward.

For category level
For sub category level

5 Bringing everything together

5.1 Data and Calcs

For bringing everything together, I brought in a second model called layer…

…and again joined this data with the already existing data.

(To sum it up: at this point we have 17 (from the self-join) x 4 (from the treemap model) x 3 (from the layer model) = 204 copies of the data in.)

Based on these three layers I could define my joint X:

I also had to define my sparkchart Y for Layer 1 & 3, but not for the treemap Y, because sparkbars and treemap are using separate axes.

5.2 Creating the View

The views can now be created like shown before.

For the treemap:

And for the sparkbars:

Creating a dual axis and we are there:

6 Summary

From this point, everything else is just doing the formatting and placing everything with the parameters. I used the 0-position of my first “bars” for the labelling, therefore I had to calculate some additional fields. And I also hid the sparkbars for the smallest areas based on a parameter. You can find in in the workbook on Tableau Public.

Thanks for reading, please reach out to me for further questions here in the comments or on Twitter (@ProfDrKSchulte).

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