Vizible Difference

Last Thursday on 3/19/2015, I went to attend San Francisco Bay Area Tableau User Group's meetup, which was hosted by Twitter.

It was a great event enlisting 4 excellent speakers, including Tableau luminaries like Allan Walker and Anya A'Hearn, a.k.a, Zen Masters, MCed impeccably by Ashley Ohmann.

Dan Seisun of Twitter made a great presentation on the infrastructure of data visualization using Tableau at the company. He shared invaluable best practices of running tableau servers, educating users, instituting Tableau champions and making corporate BI as self services.

I was able to capture his presentation on my phone, because the fonts were big enough seen from where I sat. Though succinct, not every one of his slides contains less than 160 characters.

If you think you understand what Top N means, think again. Here is a little story.

Boss: This year we have done really well. We are going to send our top 10 sales guys to the Moon. Give me the list.

Analyst RM: After running my Tableau, I can only find 5 guys in the top 10.

Boss: What? You are fired.

Analyst R: I found 11 guys in the top 10.

Boss: Are you serious? We do not have budget for one more person. You are fired.

Analyst RD: There are 20 people in the top 10.

After finishing Grouping Lower % Slices in Pie Chart, I feel it is necessary to write a companion post about the bar chart counterpart. Bar chart is still the most popular chart type of all time. Tableau Zen Master Joe Mako wrote a post on grouping low ranked bars into one, which is great inspiration to me.

I will use the same data in pie chart example which makes things easier. The statement of the problem is how to group the lower % bars into a single one after a sorted measure.

[This post is long over due. The Super Pi Day reminds me of it and thus this Pie Chart post is dedicated to the Day!]

[A sequel has been written on Grouping Lower % Bars in Bar Chart It explains the calculation from a slightly different perspective.]

A post of couple of months old in Tableau Forum has re-surfaced thanks to Patrick Van Der Hyde's effort, asking how to group lower % slices into a single one in a pie chart.

A recent post by James Eichinger inquired about the best practices of manipulating color values because his viz is swamped by high intensity colors. A solution is mentioned by David Walp and Joshua Miligan as logarithmic transformation:

Use natural logarithm LN([Measure]) for coloring, instead of [Measure]. Joshua even had a great article about its use in mapping.

Here is an example I produced using the Superstore data to visualize sales by city.

This task can be implemented using Index() as documented by many. Here I am going to present an alternative solution based on Rank_Unique(). I found it a little easier than using Index().

Let's use the classic Superstore example. We want to get Top N Categories per Department in terms of Sales.

Nested Sorting

Step 1.

Drag Department and Category to the Row shelf. 

Step2.

Create a calculated field Rank_Unique: Rank_Unique(Sum([Sales])) . 

Step 3.