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Tableau実践問題集 #TableauChallenge を作りました。

Tableau Dashboard Training 1-4: Customer Dashboard (& Shipping Dashboard)

The picture above is today's goal. I will talk about

1. Measure Name and Measure Value.

2. Highlighting.

The reference for this training is here. If you haven't downloaded yet, please see this post.(Edit it later)


Before I start, I'd like to revise my Sales per Customer variable. Since I used R to do this calculation, all records that has a certain customer name have the same amount of sales. This is the example.

So, I calculate Sales_Per_Customer and Profit_Per_Customer again in Tableau. For how to calculate them in Tableau, see Series 1-1. The formula is like this below. Here is a link for other functions in Tableau.

If the data size is "n times m", if the result of the calculation doesn't have n rows, we shouldn't do the calculation in R, because we produce duplicated data.


So let's start making worksheets with the bar chart. The first idea we will do can be like this for a bar chart that has many measures.

However, I recommend you to use "Measure Value" and "Measure Name" if you have more than 2 or 3, because it makes it easy and makes the window less messy. I believe in many times it is better to use Measure Value and Measure Name to organize them in an easier way. This is the example.

As for the other charts, I believe that you can make them by yourself if you finish Tableau Training Video so let me skip them.

Finally, I would like to show "Action" function for highlighting.

By the way, when I see the scatter plot, I feel that we can do a clustering. It seems that there are more than 3 groups based on Profit Margin. I may talk about this later.


I also have finished Shipping Dashboard, but it was so easy to talk about something new. I just mention how to add the grid lines.

I hope you enjoy today's topic. Next time, I will talk about how to use R in Tableau. As I mentioned before this series, my goal is to integrate R, Tableau and Google Analytics into an entire system. We will start with clustering and predictive modeling with this superstore data.

See you.

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