Radial Stacked Bar Charts in Tableau | Ryan K Rowland

Once completed, you’ll have the option of displaying your bar chart as absolute values (bottom-right), or as a percent of each segment’s total (left).

The size of the bars, inner circle and gaps between each bar will be adjustable as you see fit per parameters we’ll create.

Click here to view the interactive version on Tableau Public.

Introduction…

Welcome all, to my first ever blog post!

The past couple of weeks I’ve been taking part in a great Tableau community project called #MakeoverMonday. This week’s challenge was to create a visualisation based on the daily opinion polls for the US 2016 election, for which I submitted this (Click image to open interactive version).

6 Powerful Reasons Why Your Business Should Visualize Data | Maptive

You know the phrase. It’s the belief that an idea, story, or concept can be explained or conveyed with one single image. You’ve probably heard or used it in conversation yourself more times than you could count, but have you ever thought about how it could apply to your business? Consider the following statistics shared in a blog postpublished last year on the power of visuals:

The brain can see images that last for just 13 milliseconds.
Our eyes can register 36,000 visual messages per hour.
We can get the sense of a visual scene in less than 1/10 of a second.
90% of information transmitted to the brain is visual.
Visuals are processed 60,000X faster in the brain than text.
As a society, we turn to and rely on visuals when we want to be entertained, when we want to socialize with others, and when we want to learn.

If you’re looking for ways to better communicate, educate, or connect with your current and prospective customers, your employees, or your investors, one great way to do it is by taking the time to visualize the raw data you collect and share.

Here are 6 powerful reasons why your business should visualize data

Earth Temperature Timeline | xkcd

This is the most interesting  example of a Marimekko chart I have seen.  Previously I have struggled to find a use for them, but this changes my perspective.

Tableau Software

COUNTING CUSTOMERS WHO BOUGHT THE SAME PRODUCTS – VIZIBLE DIFFERENCE

I wrote previously a post on how to count customers who bought both A and B. Today someone asked how to count those who all bought the same N items. N>=2. He specifically requires a multiple choice filter so that people can select some items and only those who bought them all are showing. (All right, the problem is paraphrased.)

In the previous post, the purpose is to map out the tallies for all possible pairs of products and to study the correlation between products. Here we only need to show the customers who bought one set of products.

The Design of Everyday Visualizations | DataRemixed

I’ve been educated and inspired recently by the best selling design classic “The Design of Everyday Things” by UX guru Don Norman. You really have to read the entire book, which applies to all types of objects that people design – from chairs to doors to software to organizational structures. It provides thoughtful and practical principles that guide designers to design all of those things well. By “well” he means “products that fit the needs and capabilities of people.” (p.218)

As I read it, it occurred to me that data visualizations are “everyday things” now, too. Even richly interactive ones viewed on tablets and phones. That has only become the case in the past half-decade or so. Yes, examples can be traced back to the early days of the internet, but the recent explosion of data, software tools and programming libraries has caused their proliferation.

And I found that point after point, principle after principle in Norman’s book applied directly to data visualization. I’d like to call out five points that struck me as particularly relevant to recent discussions in the field of data visualization.

Sparkline Twitter - © Edward Tufte

Tableau Sparklines

Tableau Sparklines This is part of my series on Advanced Tableau Visualizations. What they are for: Display a simple, succinct time series of multiple dimensions Save space by omitting axes …