Comparisons and viewing trends across dates are two effective ways to turn raw date into insights, but dates can be tricky to work with in Tableau. I illustrated in my last post how to find hidden patterns in line graphs by adding a slope graph toggle, but what if the dates are not lined up on the same axis? For example, if you were to make a sales by continuous Order Date line graph with Tableau’s Sample – Superstore dataset colored by Year, you would get four colored lines that do no not line up on top of each other. Tableau does not have a Month + Day date part, which can make it challenging to compare year over year performance. Due to this, I often go through the relatively elaborate process of setting up two sets of date comparison filters and equalize the dates so the lines are right on top of each other. This post will show you a simpler way to normalize months and days so they share the same axis when colored by year. When the marks line up, it is much easier to evaluate year over year performance.
I just returned from a week-long visit to London for Tableau Conference Europe and, as usual following a TC, have come away re-energized by the Tableau community and the company’s dedication to making the best data visualization software possible. I attended several valuable sessions including Andy Cotgreave’s, New Ways to Visualize Time. I was looking forward to hearing his perspective on why line graphs can hide insights, and he did not disappoint. One of the alternatives discussed was slope graphs, and Andy shared a formula to dynamically keep only the earliest and latest dates on the chart. The talk inspired me to add a twist made possible by Tableau’s flexibility. This post will show you how to make slope graphs in Tableau, how to allow the slope graphs to update when a new date range is selected, and how to add a toggle that allows your end users to choose between a line graph or a dynamic slope graph.
I’ve shared before that I bet my career on Tableau because of its flexibility, but a close second reason is Tableau’s user experience. One of my favorite aspects of Tableau’s user experience is the ability to update my analyses “in the flow”, without having to interrupt my line of thinking or take redundant steps to answer new questions. This provides the benefits of rapid iteration and reducing the risk of distracting stakeholders that I may be presenting to. This post shares three of my favorite applications of using Tableau in the flow. I’ll show you (1) a clever way to reverse-engineer table calculations, (2) how to update calculations on the fly, and (3) how to save advanced calculations for future use.
As I often discuss, one of my main objectives as a data visualization practitioner is to avoid the question, “So what?”. I never want to have an analytics partner put their trust in me and spend my time building out a dashboard, only to have them not know why my findings are important or what to do about them. Three of the best ways to make your data visualization deliverables useful are to (1) build in comparisons, (2) add context, and (3) visualize the same fields in different ways. In the case of scatter plots, adding marginal histograms accomplishes all three of these techniques. Marginal histograms are histograms added to the margin of each axis of a scatter plot for analyzing the distribution of each measure. And as my friend, Steve Wexler, points out, they’re not just for scatter plots. This post shows you how to make marginal histograms for scatter plots, marginal bar charts for highlight tables, and explains the difference between the two.
When it comes to my favorite chart types, scatter plots are a close third behind bar charts and line graphs. In several industries, and especially scientific journals, scatter plots are the favorite choice because of their ability to reveal and communicate correlations. Another benefit of this chart type is it is one of the few visualizations that allow you to view many marks in a small space. No, you cannot analyze every individual mark because they will likely overlap, but scatter plots make it easy to identify outliers and the aforementioned correlations. But wait – there’s more! Due to the way scatter plots are set up with a measure on each axis, adding reference lines for the average of each axis creates a natural four-quadrant segmentation. This is a great technique for isolating different groups so you can act on them individually. This post will show you how to make scatter plots and take them to the next level in three ways. We’ll cover (1) a formatting trick to make your scatter plots stand out, (2) ideas for maximizing the data-ink ratio in the context of scatter plots, and (3) a calculated field that will automatically break your dimension members into four usable segments.