In all the hype about big data, we have to acknowledge that some of us have “wee” data. Not every organization has a fully-built out Information Systems department or Business Intelligence team with access to petabytes of data and the latest tools like Hadoop and Alteryx. Some of us are still running on legacy hardware and software, have tiny budgets, part-time staff, and thousands or tens of thousands of records that we want to analyze vs. billions.
My day job is in the latter camp. For all that US healthcare includes the latest treatments and technology, healthcare IT has historically been behind the times. My desktop is running Windows XP SP3, Office 2007 is our productivity tool, Microsoft Access is our most commonly used database, and Tableau is our go-to choice for data visualization (so there’s at least one area where we’re we’ve got current technology).
Every couple-few months I get a question about Microsoft Access and Tableau, I thought I’d take a few minutes to combine my answers into one post, so read on for what I know about integrating Access and Tableau.
Here’s a problem that has been bouncing around in my brain since I first used Tableau. How do I compare the results of every permutation of one item vs. another? Here’s an example using Superstore Sales – I put Region on Rows and Columns, and SUM(Sales) on the Text Shelf, and only see four values:
What if I want to compare Sales in Central to those in East, South, and West, and Sales in East to South and West, and Sales in West to Sales in South simultaneously? We can compare two at a time using parameters or a self-blend, or one vs. the rest in different ways via sets or table calcs or calculated fields, but how about each against each other? What if we want a correlation matrix? Read on to find out how to do this without any SQL, and learn a little bit about domain completion.
This is a post about getting the output you want, despite what Tableau thinks.
This is a post about making Tableau do what Excel can do (whether it’s a good idea or not).
This is a post about gaining better understanding of dimensions, measures, continuous, and discrete.
This is a post about putting bars and lines on the same chart. Continue reading →
Tableau is a Swiss army knife for data visualization, with a bunch of component tools – the view types, calculated fields, table calculations, custom SQL, mapping, performance optimization, etc. As I’ve been learning Tableau I’ve been mastering new bits. Lately I’ve been exploring Tableau’s Show Missing Values feature, otherwise known as “date padding” or sometimes “domain padding”, and made an interesting discovery. Continue reading →
In Part 1 and Part 2 of this series, I described how Tableau computes Grand Totals and several options for generating your own Grand Totals. In this post, you’ll learn the most flexible method for customizing Grand Totals, via custom SQL to duplicate the data.
In November 2013 Tableau 8.1 also added a new two pass totals feature that may remove the need for customizing grand totals for your use cases, more details are in Tableau 8.1 Two Pass Totals. In December 2015 Tableau 9.3 added total control for placing totals to the left and/or on top of the view.
For my entry for the Tableau Interactive Political Viz Contest, I chose to create an infographic that reframes the healthcare debate in terms of a moral question: Who are we choosing to deny health coverage to? Or, more specifically, who would you deny health coverage to?