1. Peter Karpov created quite a few interesting math graphs using Mathematica. One of them caught my attention because I think it can be done in Tableau: Polygon circumscribing from triangle to square to many equal-sided polygons.

    Here I created it in tableau.
    The data set is the same 2-row seed table. With parameters, we can create as many nesting layers of n-gons (n=3,4,5,...) as we wish, because we can create data using data densification on the fly. The graph above is created using polygon as data marks.

    Below are a few renditions of the same graph. Click here to access to viz.




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  2. Tableau won't allow the aggregation count distinct or CountD() on a secondary data source. For example when we want to count the number of orders that are in a secondary data set, where each order may have multiple lines of records, we can't do it in a simple way.

    There are solutions to it before LOD is available. Given LOD, this can be done a bit more easily.

    Here is an example. We have a tiled map where we need to show the number of orders from each of the US states plus DC. The order data is the superstore data set included in Tableau.

    The primary data set is the tiled map which has the coordinates of each states. The superstore data is the secondary data set.

    In the secondary data set, let's create a calculated field One Row per Order:

    This is equivalent to de-duplication of order records: AVG() will be 1 no matter how many records per order are there. That's all we need. We got the equivalent of CountD(Order) in Sum(One Row Per Order).
    The example workbook is here to be downloaded for further details. In the viz, a reference is provided using CountD() from the superstore data set. We can see the numbers are the same.

    There is a solution that is similar where Max(1) is used as the aggregation which is equivalent to AVG(Number of records). I don't understand why the author claimed {Fixed} won't work. {Fixed} works in the example above.

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  3. The purpose here is purely for the pleasure of visualizing this bipartite graph in Tableau, because of its mesmerizing optic effect, as well as for the little exercises of building the logic behind it.

    As in what I did in creating math vizzies, I try to use the minimum data set, because the rest of data points can be all derived from it based on the math relationships between points. This bipartite viz is again based on the minimum data set of two rows. Click here to access the interactive version. Below are a couple more variants. Click images to access the viz.
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  4. I have been curious about the US Supreme Court #SCOTUS for a while. Especially, recently I got a chance to watch the movie RBG on Justice Ruth Bader Ginsburg. This made me become more interested in the subject.

    Supreme Court Justices are of primary importance to US, as a country that is governed by law. Recent announcement by Justice Kennedy to retire will give President Trump an opportunity to nominate a new justice that will certainly bear his political ideology. This will change the political balance of the supreme court for years to come.

    To better understand it, there is no better way than creating a Tableau viz on it! Here is the result. Still, it only provides a subset of information on #SCOTUS. Click the image to view the interactive version. Feedback and comments are welcome.
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