This is the second in a five-part series about my go-to elements of Tableau dashboards. For future updates, subscribe to my mailing list. In the last post, I showed you how to build a current performance versus comparison performance index callout; one of my favorite descriptive tactics for communicating performance. One of my favorite prescriptive tactics is to provide a scatter plot that the user can build themselves - even if they don’t know how to use Tableau! The “parameterized scatter plot” is considered prescriptive because it helps us understand why something happened in the business, and ideally, prescribes something to do about it. Scatter plots have several advantages including (1) they’re able to show many data points at once, (2) they help illustrate correlations, and (3) they create a natural four-quadrant segmentation. This post will show you how to make scatter plots even better by allowing your end user to choose the measure displayed on the y-axis, measure displayed on the x-axis, and dimensional breakdown of the marks on the view.
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.
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.
Maps are one of the most effective chart types in Tableau and are also among the easiest chart types to create. They are effective because they help us decode latitude and longitude combinations almost instantly, allowing us to see patterns between geographic locations that may otherwise be challenging to discover. They are easy to create because Tableau comes prepackaged with thousands of geographic coordinates all over the world. This makes it so that simply double-clicking on a dimension that Tableau recognizes as geographic will create a map on the view. What’s more, Tableau maps are technically scatter plots with points at the combination of each latitude-longitude pair and an image of a map in the background. This unlocks even more applications including the ability to map anything – even if it’s not related to geography. This post will use a map of my top 10 favorite barbecue restaurants to share three ways to take your Tableau maps to the next level. Tips include a formatting trick, instructions for how to unlock additional map styles, and how to create a dual-axis map using a combination of generated and custom coordinates.