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.
Tableau sets allow you to isolate specific segments of a dimension, which can then be used in several different ways to find insights in your data. This post provides instructions on how to build sets as well as five different ways they can be used to enhance your analyses. Sets can be thought of as custom segments, but unlike dimension fields, they are always binary. In other words, you are either in the set or not. Other than that one restriction, sets can be created for just about anything. You can pick individual dimension members to place in a set, have sets be based on quantitative thresholds, created with the top or bottom performing dimension members, and more.
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.
In the last two tutorials about mapping, we have discussed path maps and custom symbol maps. There is a third type of map in Tableau called a polygon map that allows you to map custom shapes. These types of visualizations are what we’re making anytime we’re making a “filled map”. Imagine a map of sales by U.S. state where each state is colored by their respective sales volumes. With these filled maps, Tableau is essentially looking up the latitude and longitude coordinates all the way around the border of each state, and plotting a custom polygon for each territory. With custom polygons, we’re not limited to a prepared set of polygons like state borders—we can define shapes for anything we can imagine from custom geographic dimensions, to your favorite theme park, to your local dog park, to grocery store shelves, or anything else! This tutorial will use one of my most asked about visualizations, The Cost of Attending the 2015 World Series, to illustrate how you can create custom polygon maps with any shapes—including stadiums!