Moving Beyond Basic Visualizations

You are now ready to set out on your adventure of creating more advanced visualizations! Advanced does not necessarily mean difficult since Tableau makes many visualizations easy to create. Advanced also does not necessarily mean complex. The goal is to communicate the data, not to obscure it in needless complexity.

Instead, these visualizations are advanced in the sense that you will need to understand when they should be used, why they are useful, and how to leverage the capabilities of Tableau to create them. Additionally, many of the examples we will look at will introduce some advanced techniques, such as calculations, to extend the usefulness of foundational visualizations. Many of these techniques will be developed fully in future chapters, so don’t worry about trying to absorb every detail right now.

Most of the examples in this chapter are designed so that you can follow along. However, don’t simply memorize a set of...

Comparing values

Often, you will want to compare the differences between measured values across different categories. You might find yourself asking the following questions:

• How many customers did each store serve?
• How much energy did each wind farm produce?
• How many patients did each doctor see?

In each case, you are looking to make a comparison (among stores, wind farms, or doctors) in terms of some quantitative measurement (the number of customers, units of electricity, and patients).

Let’s take a look at some examples of visualizations that help answer these types of questions.

Bar charts

Here is a simple bar chart, similar to the one we built in Chapter 1, Taking Off with Tableau:

Figure 3.1: A bar chart showing the number of patient visits by department

This bar chart makes it easy to compare the number of patient visits between various departments in the hospital. As a dimension, Department slices the data according...

Visualizing dates and times

In your analysis, you will often want to understand when something happened. You’ll ask questions like the following:

• When did we gain the most new customers?
• Is profit trending up or down?
• What times of day have the highest call volume?
• What kinds of seasonal trends do we see in sales?

Fortunately, Tableau makes this kind of visual discovery and analysis easy. In this section, we’ll look at how Tableau works with dates and some various ways you might visualize time.

Date parts, date values, and exact dates

When you are connected to a flat file, relational, or extracted data source, Tableau provides a robust built-in date hierarchy for any date field.

Cubes/OLAP connections do not allow Tableau hierarchies. You will want to ensure that all date hierarchies and date values you need are defined in the cube.

To see this in action, continue with the Chapter 3 workbook, navigate...

Relating parts of the data to the whole

As you explore and analyze data, you’ll often want to understand how various parts add up to a whole. For example, you’ll ask questions such as the following:

• How much does each electric generation method (wind, solar, coal, and nuclear) contribute to the total amount of energy produced?
• What percentage of total profit is made in each state?
• How much space does each file, subdirectory, and directory occupy on my hard disk?

These types of questions are asking about the relationship between the part (production method, state, and file/directory) and the whole (total energy, national sales, and hard disk). There are several types of visualizations and variations that can aid you in your analysis.

Let’s now look at some visualization examples that will aid us as we consider how to show part-to-whole relationships.

Stacked bars

We looked at stacked bars in Chapter 1, Taking Off with...

Visualizing distributions

Often, simply understanding totals, sums, and even the breakdown of part-to-whole only gives a piece of the overall picture. Most of the time, you’ll want to understand where individual items fall within a distribution of all similar items.

You might find yourself asking questions such as the following:

• How much does each customer spend at our stores and how does that compare to all other customers?
• How long do most of our patients stay in the hospital? Which patients fall outside the normal range?
• What’s the average life expectancy for components in a machine and which last more than average? Are there any components with extremely long or extremely short lives?
• How far above or below passing were students’ test scores?

These questions all have similarities. In each case, you seek an understanding of how individuals (customers, patients, components, and students) relate to the group. In each...

Visualizing multiple axes to compare different measures

Often, you’ll need to use more than one axis to compare different measures, understand the correlation, or analyze the same measure at different levels of detail. In these cases, you’ll use visualizations with more than one axis.

Scatterplot

A scatterplot is an essential visualization type for understanding the relationship between two measures. Consider a scatterplot when you find yourself asking questions like the following:

• Does how much I spend on marketing really make a difference in sales?
• How much does power consumption go up with each degree of heating/cooling?
• Is there any correlation between hours of study and test performance?

Each of these questions seeks to understand the correlation (if any) between two measures. Scatterplots help you understand these relationships and see any outliers.

Consider the following scatterplot, which looks for a relationship between...

Summary

We’ve covered quite a bit of ground in this chapter! You should now have a good grasp of when to use certain types of visualizations. The types of questions you ask about data will often lead you to a certain type of view. You’ve explored how to create these various types and how to extend basic visualizations using a variety of advanced techniques, such as calculated fields, jittering, multiple mark types, and dual axis. Along the way, we’ve also covered some details on how dates work in Tableau.

Hopefully, the examples of using calculations in this chapter have whet your appetite for learning more about creating calculated fields. The ability to create calculations in Tableau opens endless possibilities for extending the analysis of data, calculating results, customizing visualizations, and creating rich user interactivity. We’ll dive deep into calculations in the next two chapters to see how they work and what amazing things they can do.

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