“How do you calculate a headcount at a moment in time when you only have a start and end date?” I’ve gotten this question several times. My answer used to be “Ideally, you’d want a row of data for an individual for every possible date unit you’d want to count them at.”
If you haven’t had a chance to check it out for yourself yet, I want to introduce you to Tableau’s latest breakthrough, the “viz in tooltip”. Let me show you how this works and why it’s valuable.
There will likely be times when you want to calculate performance year to date versus the same time period prior year to date.
Have you ever had an asterisk (*) returned in place of a value in Tableau? This unexpected behavior is the result of what’s called the Attribute function (ATTR). We’ll look into it more here.
Let’s imagine that we work for a restaurant chain and are helping perform an analysis to figure out which items are under-performing. We’ll start with a visual like this...
Last spring I climbed Mount Rainier. Being an adventurous, outdoorsy, Pacific-Northwest born kid, it’s something I’d always dreamed about. While I enjoy physically grueling events, I’m a naturally risk averse person. I needed some friends to finally convince me to do it.
I want to teach you a method I’ve used with various clients when they needed a flexible date field as part of their Tableau dashboard. In one example, I was working with a company that was using Tableau to create client-facing reports. Problem is, they had different granularities of data for different clients. For some clients they collected data daily, others monthly, and some yearly. What they needed was the ability to create a flexible dropdown that allowed them to change the level of date granularity in the view.
If you’ve ever received the error “Cannot mix aggregate and non-aggregate comparisons or results in ‘IF’ expressions in Tableau I feel your pain. I spent my first several months in Tableau not understanding what that error meant and running into impassable roadblocks aggregating data in Tableau as a result.
Imagine you are living your best life and run a company that owns an ice cream parlor and a chocolate store. You have data for both companies that looks like this:
You may have noticed that under “Compute Using” in the Table Calculation dialogue box there is a section called “Specific Dimensions” where you would normally select a scope and direction. You generally only need to use Specific Dimensions when you have 3 or more dimensions in the view.
Most dentists recommend visiting every six months for a routine cleaning. Have you ever wondered if you really need to go in that often? Why not every two months? Eight months? Two years? There are several indicators that determine how often you should visit the dentist
A few years ago I was working with a Fortune 500 restaurant chain that many of us frequent. What you probably don’t know is how often the rats frequent their restaurants too. The chain was trying to reduce the number of pest incidents at the worst offending stores, but was having trouble determining which stores were worst because their data was so messy.
I’ve pulled a set of data for the top pitchers in Major League Baseball in 2017. Let’s say we want to do an analysis to see which of the best pitchers got extra help from their teams and which didn’t.
I’ll start by creating a scatter plot displaying wins and losses by each pitcher. Pitchers with dark blue circles that are farther toward the top did not get much help from their teams. Pitchers with lighter circles that are closer to the bottom did.
Data blending is a great tool to have in your Tableau arsenal, but it has its quirks. It's one of the oldest forms of data preparation in the product. There are a lot of unique properties to the data blending feature which are important to understand if you're going to use this method to prepare your data for Tableau.
I was working with a client recently and they wanted to create a calculation in Tableau allowing them to compare performance this month to date versus the same time period last month.
Let’s say we are comparing sales from this month to the previous month. We’ll start by creating a calculation to return sales for just this month: