Chapter 3. Moving from Foundational to More Advanced Visualizations
You are now ready to set out on a journey of building advanced visualizations. Advanced does not necessarily mean difficult. Tableau makes it easy to create them. Advanced also does not necessarily mean complex. The goal is to communicate the data, not obscure it with 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 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 understand how the combinations...
Comparing values across different dimensions
More often than not, you will want to compare the differences of measured values across different categories. You might find yourself asking questions like this:
How much profit did we generate in each department?
How many views did each web page get?
How many patients did each doctor see?
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 count of patients).
The following figure is a simple bar chart, similar to the one we built in Chapter 1, Creating Your First Visualizations and Dashboards:
The sum of sales is 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 (because it is continuous) and is summed (because it...
Visualizing dates and times
Often in your analysis you will want to understand when something happened. You'll ask questions like:
When did we gain the newest customers?
What times of day have 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 for Tableau hierarchies. You will want to ensure that all the date hierarchies and date values that you need are defined in the cube.
To see this in action, continue with the Chapter 03 workbook, navigate to the Built-in Date Hierarchy sheet, and create a view similar to the one shown here by dragging and dropping SUM(Sales) to Rows and YEAR(Order Date) to Columns:
Note that even though the Order Date field is a date, Tableau defaulted...
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:
How many patients with different admission statuses (in-patient, out-patient, observation, ER) make up the entire population of patients in the hospital?
What percentage of total national sales is made in each state?
How much space does each file, sub-directory, and directory take up on my hard disk?
These types of questions are asked about the relationship between the part (patient type, state, and file/directory) and the whole (entire patient population, national sales, and hard disk). There are several types of visualizations and variations that can aid you in your analysis.
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 bar other than the bottom-most...
Visualizing distributions
Often, simply understanding totals, sums, and even the breakdown of part to whole only gives 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 such as:
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 the average score were most students' test scores?
These questions all have similarities. In each case, you seek an understanding of where individuals (patients, components, students) were in relation to the group. In each case, you most likely have a relatively high number of individuals. In data terms, you have a dimension (patient, components, 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 the visualizations with more than one axis.
A scatterplot is an essential visualization type for understanding the relationship between two measures. Consider a scatterplot when you find yourself asking questions such as:
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 hours of study and test performance?
Each of these questions seeks to understand the correlation (if any) between two measures. Scatterplots are great for seeing these relationships and also for locating outliers.
Consider the following scatterplot that looks at the relationship between the measures: the sum of Sales (on the X axis) and the sum of Profit...
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 of the 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 and using the special Measure Names / Measure Values fields.
Hopefully, the examples using calculations in this chapter have whet your appetite for learning more about calculated fields. The ability to create calculations in Tableau opens up endless possibilities for extending analysis of the data, calculating results, customizing visualizations, and creating a rich user interactivity. We'll dive deep into row level, aggregate, level of detail, and table calculations in the next two chapters...
