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.
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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.
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:
A couple weeks ago I was teaching a course and received a question from a student. Her question was “How can I use Tableau to only show the most recent 3 transactions per customer?” I thought I’d have an answer for her quickly, but I was wrong.
My first thought was, let’s just use the “RANK” function to accomplish this. We’ll use it as a Table Calculation to determine the most recent transactions by customer. I was feeling confident until I saw this:
A student recently asked me how she could create dynamic, color-changing labels based on whether a field passed a threshold. My first response was “Tableau can’t do that.”. My second thought was “How can I make Tableau do that?”
Tree maps are a data visualization used to communicate hierarchical values in a systematic way with nested rectangles. A lot of the tree maps I see look something like this:
I don’t know about you, but I don’t find this to be particularly informative or compelling. I prefer to use tree maps as a way to highlight a few relevant data points. Notice in the dashboard below how I use a tree map to highlight the top 10 items sold.
I was given a challenge by a student recently. They wanted to color territories on their map by a region, but they wanted the intensity of the color to represent the amount of sales that occurred in an individual territory.
Take a graph like the following:
Let’s say you want to visually distinguish the 5 days with the lowest profit ratio. I’ll show you how I prefer to approach situations like this.
Designing dashboards in Tableau can be tricky. One problem my students run into is how to space worksheets evenly. It is time consuming to drag the edges of worksheets, making minor tweaks, until they are all the exact same pixel width.
It’s easy to want to cram too much information on a single Tableau dashboard. Whenever I have a scenario where I want to provide additional information to my end user without overcrowding my dashboards, I include an info button.
There will be times when building dashboards when you’ll have multiple worksheets that use the same measure on color. It’s best practice to use color consistently throughout your dashboard, but it can also lead to unforeseen complications. Take the below dashboard as an example.
I shared an example with a class recently about how to use Table Calculations in Tableau and got a request to share this via a write-up. One of the most powerful things about Table Calculations in Tableau is the ability to set a scope and direction.
One of my favorite additions to a dashboard are summary tiles. They’re a great way to quickly communicate a few quick, important data points. In this write-up, I’ll share some of my favorite techniques for building them and making them look crisp. See below for an example of summary tiles I created recently.
I recently got an email from a former student explaining that they were trying to recreate something similar to the image below in Tableau.
The trouble she was running into was recreating the total header called “Planned Cost by Perf”. If you try to use a measure as a discrete header, you end up with the value summed for each partition. Here is an example using Superstore data:
When calculating growth rates from one date period to another, it’s important to compare apples to apples. For instance, when building a graph in Tableau to compare quarterly sunscreen sales in Seattle, I probably wouldn’t want to compare Q3 Sales (July - September) to Q2 Sales (April - June) because there will be more sales in Q3. The product has a cyclical sales cycle. Instead, I would rather compare Q3 Sales of this year to Q3 Sales of last year to more accurately understand growth rates.
Comparing progress against a goal in Tableau is a common use case and I wanted to share a few tips about how I like to do this.