Chapter 3. Moving from Foundational to Advanced Visualizations

You are now ready to set out on a journey of building advanced visualizations! "Advanced" does not necessarily mean difficult. Tableau makes many of these visualizations easy to create. Advanced also does not necessarily mean complex. The goal is to communicate the data, not 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 introduce some advanced techniques, such as calculations, to extend the usefulness of foundational visualizations. Many of these techniques will be developed more fully in future chapters, so don't worry about trying to absorb every detail.

Most of the examples in this chapter are designed so that you can follow along. However, don't simply memorize a set of instructions. Instead, take time to...

Comparing values across different dimensions

Often, you will want to compare the differences of measured values across different dimensions. You might find yourself asking questions like these:

• How much profit did I generate in each department?

• How many views did each of my websites get?

• How many cases did each doctor in the hospital treat last year?

In each case, you are looking to make a comparison (among departments, websites, or doctors) in terms of some quantitative measurement (profit, number of views, and the count of cases).

Bar charts

Here is a simple bar chart, created using the Superstore Sales data source, similar to the one we built in Chapter 1, Creating Your First Visualizations and Dashboard:

The sum of sales is easily compared for each category of item sold in the chain of stores. Category is used as a discrete dimension in the view, which defines row headers (because it is discrete) and slices the sum of sales for each category (because it is a dimension). Sales defines an axis...

Visualizing dates and times

Often in your analysis, you will want to understand when something happened. You'll ask questions like these:

• When did we gain the most new customers?

• What time of the day has the highest call volume?

• What kinds of seasonal trends do we see in sales and profit?

Fortunately, Tableau makes this kind of visual discovery and analysis easy.

The built-in date hierarchy

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.

Tip

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 visualization workbook and create a view similar to the one shown in the following screenshot by dragging and dropping Sales to Rows and Order Date to Columns:

Note that even though the Order Date field is a date, Tableau defaulted to showing sales by year. Additionally...

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 like these:

• How many patients with different admission statuses (in-patient, out-patient, observation, or ER) make up the entire population of patients in the hospital?

• What is the percentage of total national sales made in each state?

• How much space does each file, subdirectory, and directory take on my hard disk?

These types of questions ask about the relationship between the part (patient type, state, or file/directory) and the whole (the entire patient population, national sales, and hard disk). There are several types of visualizations and variations that can aid you in your analysis.

Stacked bars

We took a look at stacked bars in Chapter 1, Creating Your First Visualizations and Dashboard, where we noted one significant drawback: it is difficult to compare values across categories for any but the bottom-most bar (for...

Visualizing distributions

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

You might find yourself asking questions like these:

• 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 components fall above or below that average? Are there any components with extremely long or extremely short lives?

• How far above or below "passing" were most students' test scores?

These questions all have similarities. In each case, you are asking for an understanding of how individuals (patients, components, and students) compared with each other. In each case, you most likely have a relatively high number of individuals. In data terms, you have a dimension (Patient, Component, and Student) with high cardinality...

Visualizing multiple axes to compare different measures

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

Scatterplots

A scatterplot is an essential visualization type to understand the relationship between two measures. Consider a scatterplot when you find yourself asking questions like these:

• Does how much I spend on marketing really make a difference to sales?

• How much does power consumption go up with each degree of heating/cooling?

• Is there any correlation between rental price and the length of contract?

Each of these questions seeks to understand the correlation (if any) between two measures. Scatterplots are great to see these relationships and also to locate outliers.

Consider the following scatterplot, which looks at the relationship between the measures of the sum of Sales (on the x axis) and the sum of Profit ...

Summary

We 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 of the data will often lead you to a certain type of view. You 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 also covered some details on how dates work in Tableau using the special Measure Names / Measure Values fields.

Hopefully, the examples using calculations have made you eager to learn more about creating calculated fields. The ability to create calculations in Tableau opens up endless possibilities for extending 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.