3

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 instructions. Instead, take the time to understand how the combinations of different field types you place on different shelves change the way headers, axes, and marks are rendered. Experiment and even deviate from the instructions from time to time, just to see what else is possible. You can always use Tableau’s back button to follow the example again!

In this chapter, visualizations will fall under the following major categories:

• Comparison
• Dates and times
• Relating parts of the data to the whole
• Distributions
• Multiple axes

You may have noticed the lack of a spatial location or geographic category in the preceding list. Mapping was introduced in Chapter 1, Taking Off with Tableau, and we’ll get to some advanced geographic capabilities in Chapter 12, Exploring Mapping and Advanced Geospatial Features.

You can recreate the examples that are found in this chapter by using the Chapter 03 Starter.twbx workbook, or you can even start from scratch by using a blank workbook and connecting to the Hospital Visits.csv file that’s located in the Learning Tableau/Chapter 03 folder. The completed examples can be found in the Chapter 03 Complete.twbx workbook.

We will begin by assessing which types of visualizations are effective for quantitative comparisons.

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 to each distinct value, such as ER, ICU, or Cardiology. It creates a header for these values because it is discrete (blue). As a measure, Number of Patient Visits gives the sum of patient visits for each department. Because it is a continuous (green) field, it defines an axis, and bars are rendered to visualize the value.

Notice that the bar chart is sorted by the department having the highest sum of patient visits at the top and the lowest at the bottom. Sorting a bar chart often adds a lot of value to the analysis because it makes it easier to make comparisons and see the rank order. For example, it is easy to see that the Microbiology department has had more patient visits than the Nutrition department. If the chart wasn’t sorted, this may not have been as obvious.

You can sort a view in multiple ways, as follows:

• Click one of the sort icons on the toolbar: This results in an automatic sort of the dimension based on the measure that defined the axis. Changes in data or filtering that result in a new order will be reflected in the view:

Figure 3.2: Toolbar sort icons

• Click the sort icon on the axis: The option icon will become visible when you hover over the axis and will then remain in place when you enable the sort. This will also result in automatic sorting:

Figure 3.3: Axis sort icon

• Use the drop-down menu on the active dimension field and select Sort... to view and edit the sorting options. You can also select Clear Sort to remove any sorting:

Figure 3.4: Sorting using the drop-down menu

• Use the drop-down menu on the field label for rows and select the desired sorting option:

Figure 3.5: Sorting by the field label

• Drag and drop row headers to manually rearrange them. This results in a manual sort that does not get updated with data refreshes.

All of these sorting methods are specific to the view and will override any default sort you defined in the metadata.

Bar chart variations

A basic bar chart can be extended in many ways to accomplish various objectives. Consider the following variations:

• Bullet chart to show progress toward a goal, target, or threshold
• Bar-in-bar chart to show progress toward a target or compare two specific values within a category
• Highlighting categories of interest

Bullet chart

A bullet graph (sometimes also called a bullet chart) is a great way to visually compare a measure with a goal, target, or threshold. The bar indicates the measure value, while the line indicates the target. Tableau also defaults to shading to indicate 60% and 80% of the distance to the goal or threshold. The line and the shading are reference lines. We’ll consider detailed examples of working with reference lines throughout the book, especially in Chapter 9, Visual Analytics: Trends, Clustering, Distributions, and Forecasting. Here is an example of a bullet chart:

Figure 3.6: Parts of a bullet graph

In this scenario, we’ll consider how the hospital operated with respect to revenue goals. The hospital administration set the following revenue goals for 2019 and now wants to understand how each department actually performed:

Figure 3.7: Department goals are stored in a spreadsheet as shown here

We’ll build a bullet graph using the Chapter 3 workbook, which contains the Hospital Visits and Hospital Goals spreadsheet data sources. We’ll use these two data sources to visualize the relationship between actual and target minutes to service as you follow these steps:

1. Navigate to the Revenue Per Department (Bullet Chart) sheet.
2. Create a basic bar chart of the total Revenue per Department. You can accomplish this by dragging Department to Rows and Revenue to Columns.
3. Sort Department from highest to lowest.
4. Filter Date of Admit by Year and keep only 2019 data. At this point, your view should look like this:

Figure 3.8: Interim steps in creating the bullet graph

5. In the Data pane, select the Goal field under the Hospital Goals.csv table.
6. Open Show Me and select the bullet graph option. At this point, Tableau will have created a bullet graph using the fields already in the view and the Goal field you selected in the data pane.

When you use Show Me to create a bullet chart, you may sometimes find that Tableau uses the fields in reverse order from what you intend (with the wrong measure defining the axis and bars, and the other defining the reference line). If this happens, simply right-click the axis and select Swap Reference Line Fields:

Figure 3.9: The Swap Reference Line Fields option

The completed bullet chart should look like the following:

Figure 3.10: The complete bullet graph

The completed bullet graph allows us to see which departments have met their goals and which are behind. Next, let’s consider how we can highlight this even more.

Calling out thresholds

With bullet charts, it can be helpful to visually call out the bars that fail to meet or exceed the threshold. We’ll look at calculations in depth in the next chapter, but for now, you can complete this example with the following steps:

1. Use the drop-down arrow in the Data pane and select Create Calculated Field…:

Figure 3.11: Creating a calculated field

2. Name the calculated field Goal Met? and add the following code:

   SUM([Revenue]) >= SUM([Goal])
   
3. Click OK and drag the new Goal Met? field from the Data pane and drop it on Color.

The calculation returns true when the Revenue value for a department is greater than the goal value, and false otherwise. With the calculated field on Color, it becomes very easy to see which departments have met the 2019 goals:

Figure 3.12: Departments that have met their goal are highlighted in this bullet chart

Color is one of the strongest ways to get attention with your visualizations. Use color with intent. Decide whether you want to highlight good or poor performance.

Bar-in-bar chart

Another possibility for showing relationships between two values for each category is with a bar-in-bar chart. Like the bullet chart, the bar-in-bar chart can show progress toward a goal. It can also be used to compare any two values.

For example, you might compare revenue to a target, or you might compare the revenue for the current year to the previous year:

Figure 3.13: Bar-in-bar chart

To create this view, continue in the same workbook and follow these steps:

1. Navigate to the Year over Year Revenue (Bar-in-Bar) sheet.
2. Drag and drop Revenue onto the horizontal axis in the view (which gives the same results as dropping it onto the Columns shelf).
3. Drag and drop Department Type onto Rows.
4. Drag and drop Date of Admit onto Color. We’ll discuss dates in more detail in the next section, but you’ll notice that Tableau uses the year of the date to give you a stacked bar chart that looks like this:

Figure 3.14: Interim steps in creating the bar-in-bar chart

5. For a bar-in-bar chart, we do not want the marks to be stacked. To turn off stacking, use the main menu to select Analysis | Stack Marks | Off.
6. All the bar segments now begin at 0, and some bars may completely obscure others. To see each bar, we’ll need to adjust another visual element. In this case, hold down the Ctrl key while dragging the YEAR(Date of Admit) field that is currently on Color in the Marks card to Size.

   Holding the Ctrl key while moving a field from one shelf to another creates a copy of the field instead.

   After completing the previous step, a size legend should appear. The bars will be sized based on the year, and we will be able to see all of the segments that are available, even if they overlap.

7. We want 2019 to be in front and 2018 to be in the background, so drag and drop 2019 within the Size legend to reorder the values so that 2018 comes after 2019:

Figure 3.15: You can drag and drop items in legends to reorder them

8. Double-click the Color legend to edit the colors so that 2019 is emphasized. A darker orange or blue for 2019 with a light gray for 2018 would serve this purpose well (though you may find other color combinations that you prefer!).

At this point, your view should look like the bar-in-bar chart that was shown in Figure 3.13 at the beginning of this section. You may wish to further enhance the visualization by doing the following:

• Adding a border to the bars. Accomplish this by clicking the Color shelf and using the Border option.
• Adjusting the size range to reduce the difference between the large and small extremes. Accomplish this by double-clicking the Size legend (or using the caret dropdown and selecting Edit from the menu).
• Adjusting the sizing of the view. Accomplish this by hovering over the canvas, just over the bottom border, until the mouse cursor changes to a sizing cursor, and then click and drag to resize the view. You may also want to adjust how the view fills the space. Use the dropdown on the toolbar and experiment with the options:

Figure 3.16: This dropdown determines how the current view is sized

• Hiding the size legend. You may decide that the size legend does not add anything to this particular view as size was only used to allow overlapping bars to be seen.

To hide any legend, use the drop-down arrow on the legend and select Hide Card:

Figure 3.17: The Hide Card option for legends

The bar-in-bar chart gives you another way to compare values. Next, we’ll consider a variation that allows us to highlight areas of interest.

Highlighting categories of interest

Let’s say one of your primary responsibilities at the hospital is to monitor the number of patient visits to the ICU and Neonatal departments. You don’t necessarily care about the details of other departments, but you do want to keep track of how your two departments compare with others. You might design something like this:

Figure 3.18: A bar chart with two bars highlighted via color

Now, as the data is refreshed over time, you will be able to immediately see how the two departments of interest to you compared to other departments. To create this view, follow these steps:

1. Navigate to the ICU and Neonatal sheet.
2. Place Department on Rows and Number of Patient Visits on Columns. Sort the bar chart in descending order.
3. Click on the bar in the view for ICU, and while holding down the Ctrl key, click the bar for Neonatal.
4. Hover the cursor over one of the selected bars for a few seconds, and from the menu that appears, click the Create Group button (which looks like a paperclip):

   

   Figure 3.19: After Ctrl + clicking the two bars, use the paperclip icon to group them

   This will create a group, which results in a new dimension, named Department (group), in the left-hand data pane. Tableau automatically assigns this field to Color.

   Ad hoc groups are powerful in Tableau. You can create groups in the view (as you did previously) or by using the menu for a dimension in the data pane and selecting Create | Group. You can use them as you would any other dimension.

5. To add a label only to the bars for those two departments, right-click each bar and select Mark label | Always show. The label for the mark will always be shown, even if other labels are turned off for the view or the label overlaps marks or other labels.

The color will make monitoring easy. The label will only show for the two departments you selected and will update with the changing data.

Now that we’ve considered how bar charts can be used to compare values and have walked through several examples of variations, let’s turn our attention to visualizing dates and times.

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.

To see this in action, continue with the Chapter 3 workbook, navigate to the Built-in Date Hierarchy sheet, and create a view by dragging and dropping Number of Patient Visits to Rows and Date of Admit to Columns. The YEAR(Date of Admit) field on Columns will have a plus sign indicator, like this:

Figure 3.20: The field representing the Year part of the date hierarchy

You’ll also find a plus or minus indicator as you hover over headers, like this:

Figure 3.21: A plus icon on the column headers that could be used to expand the hierarchy

When you click it, the hierarchy expands by adding QUARTER(Date of Admit) to the right of YEAR(Date of Admit) on Columns, and the view is expanded to the new level of the hierarchy:

Figure 3.22: The expanded hierarchy with the year and quarter shown

The YEAR(Date of Admit) field now has a minus sign indicator that allows you to collapse the hierarchy back to the year level. The QUARTER field also has a plus sign, indicating that you can expand the hierarchy further. Starting with Year, the hierarchy flows as follows: Year | Quarter | Month | Day. When the field is a date and time, you can further drill down into Hour | Minute | Second. Any of the parts of the hierarchy can be moved within the view or removed from the view completely.

The hierarchy is made up of Date Parts, which is one of the three ways a date field can be used. When you right-click the date field in the view or by using the drop-down menu, you’ll see multiple date options, as follows:

Figure 3.23: The drop-down menu on an active date field demonstrates the various aspects of dates in Tableau

The three major date types are evident, though not explicitly labeled, in the menu:

• Date part: This field will represent a specific part of the date, such as the quarter or month. The part of the date is used by itself and without reference to any other part of the date. This means that the date of November 8, 1980, when used as a month date part, is simply November in the view. The November that’s selected in the view here represents all of the Novembers in the dataset, while the number of patient visits is the total for both 2018 and 2019:

Figure 3.24: This view uses Month as a date part. The number of patient visits is the total for the month, without regard to the year

• Date value: This field will represent a date value, but rolled up or truncated to the level you select. For example, if you select a date value of Month, then November 8, 2019, gets truncated to the month and year and is November 2019. You’ll notice that November 2018 and November 2019 each have a separate value in the header and a distinct bar:

Figure 3.25: This view uses Month as a date value. The number of patient visits is the total for the month with regard to the year

• Exact date: This field represents the exact date value (including time, if applicable) in the data. This means that November 8, 1980, 2:01 a.m. is treated as distinct from November 8, 1980, 3:08 p.m.

It is important to note that nearly any of these options can be used as discrete or continuous fields. Date parts are discrete by default. Date values and exact dates are continuous by default. However, you can switch between discrete and continuous as required to allow flexibility in the visualization.

For example, you must have an axis (requiring a continuous field) to create a reference line. Also, Tableau will only connect lines at the lowest level of row or column headers. Using a continuous date value instead of multiple discrete date parts will allow you to connect lines across multiple years, quarters, and months.

Let’s consider some various ways we might visualize dates and times next.

Variations of date and time visualizations

The ability to use various parts and values of dates and even mix and match them gives you a lot of flexibility in creating unique and useful visualizations.

For example, using the month date part for columns and the year date part for color gives a time series that makes a visual year-over-year comparison quite easy. The year date part has been copied to the label so that the lines can be labeled:

Figure 3.26: The comparison of two years, month by month

This kind of view allows for easy year-over-year comparison.

The following heat map is another example of using date parts on different shelves to achieve useful analysis. This kind of visualization can be quite useful when looking at patterns across different parts of time, such as hours in a day or weeks in a month. Here, we are looking at how many patients were admitted by month and day:

Figure 3.27: A heat map showing the intensity of patient visits by day and month

The year has not been included in the view, so this is an analysis of all years in the data and allows us to see whether there are any seasonal patterns or hotspots. We might notice patterns related to epidemics, doctors’ schedules, or the timing of insurance benefits. Perhaps the increased intensity of patient admissions in February corresponds to the flu season.

Observe that placing a continuous field on the Color shelf resulted in Tableau completely filling each intersection of Row and Column with the shade of color that encoded the sum of patient visits. Clicking on the Color shelf gives us some fine-tuning options, including the option to add borders to marks. In this view, a black border has been added to help distinguish each cell.

Gantt charts

Gantt charts can be incredibly useful for understanding any series of events with a duration, especially if those events have some kind of relationship. Visually, they are very useful for determining whether certain events overlap, have a dependency, or take more or less time than other events.

For example (not included in the workbook), the following Gantt chart shows a series of processes that run when an application is started.

Some of these processes run in parallel, and some are clearly dependent on others. The Gantt chart makes these dependencies clear:

Figure 3.28: A Gantt chart showing the time each process started and how long each took

Gantt charts use the Gantt mark type on the Marks card dropdown. A Gantt bar mark starts at the value that was specified by the field on Rows that defines the axis. The length of the Gantt bar is then determined by the field on the size card, with positive values stretching to the right and negative values to the left.

At the hospital, you might want to see each patient visit to the ER in 2019 and understand how long each visit lasted, whether any patients returned to the hospital, and how much time there was between visits. The following steps give an example of how you might create a Gantt chart:

1. Place Department on Filters and keep only ER.
2. Place Date of Admit on Filters, select Years as the option for filtering, and keep only 2019.
3. Place Date of Admit on Columns as a continuous Exact Date or as a Day value (not the Day part). Notice that Tableau’s automatic default for the mark type is Gantt bars:

Figure 3.29: In this case, Gantt bars are the automatic mark type

4. Place Doctor and Patient Name on Rows. The result is a row for each patient grouped by each doctor. A Gantt bar represents a stay in the hospital.

   In most cases, we’d also want to add a unique identifier to the view, such as Patient ID, to ensure that patients who happen to share the same name are distinguished in the visualization. This is not necessary with this dataset, as all names happen to be unique, but it may be vitally important when you work with your data.

5. The length of the Gantt bar is set by placing a field with a duration value on the Size shelf. There is no such field in this dataset. However, we have the Date of Discharge, and we can create a calculated field for the duration. We’ll cover calculations in more detail in the next chapter. For now, select Analysis from the menu and click Create Calculated Field.... Name the field Days in the Hospital and enter the following code:

   DATEDIFF('day', [Date of Admit], [Date of Discharge]) 
   
6. The new calculated field will appear under Measures in the data pane. Drag and drop the field onto the Size shelf. You now have a Gantt chart showing when patients were admitted and how long each visit lasted.

   Consider sorting the Patient Name dimension in the view. For example, sorting by field and selecting Date of Admit as the minimum would enable you to see patients who were admitted earlier toward the top and patients who were admitted later toward the bottom. It is based on the earliest (minimum) date of admission for the patient, even if they were admitted multiple times. Sorting can be a very useful technique for seeing patterns in the Gantt chart.

Your final view should look something like this:

Figure 3.30: The final Gantt chart, showing each patient, when they were admitted, how long they stayed, and whether they ever returned

This type of chart can be very useful in seeing patterns and relationships between entities over time.

With a good understanding of how Tableau works with dates and times, we’ve considered some different options for visualization. Let’s turn our focus on how to visualize parts-to-whole relationships next.

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 Tableau, where we noted one significant drawback: it is difficult to compare values across most categories. Except for the leftmost (or bottom-most) bars, the other bar segments have different starting points, so lengths are much more difficult to compare. It doesn’t mean stacked bars should never be used, but caution should be exercised to ensure clarity of communication.

Here, we are using stacked bars to visualize the makeup of the whole. We are less concerned with visually comparing across categories and more concerned with seeing the parts that make up a category.

For example, at the hospital, we might want to know what the patient population looks like within each type of department. Perhaps each patient was assigned a risk profile on admission.

We can visualize the number of visits broken down by risk profile as a stacked bar, like this:

Figure 3.31: A stacked bar chart showing the total number of patients per department and the breakdown of low and high risk

This gives a decent view of the visits for each department type. We can tell that more people visit the general departments and that the number of high-risk patients for both Specialty and Labs is about the same. Intensive Care sees fewer high-risk patients and fewer patients overall. But this is only part of the story.

Consider a stacked bar that doesn’t give the absolute value, but gives percentages for each type of department:

Figure 3.32: A stacked bar chart showing the relative number of high-risk and low-risk patients per department

Compare the previous two stacked bar charts. The fact that nearly 50% of patients in Intensive Care are considered High risk is evident in both charts. However, the second chart makes this immediately obvious.

None of the data has changed between the two charts, but the bars in the second chart represent the percentage of the total for each type of department. You can no longer compare the absolute values, but comparing the relative breakdown of each department type has been made much easier. Although there are fewer patients in Intensive Care, a much higher percentage of them are in the high-risk category.

Let’s consider how the preceding charts can be created and even combined into a single visualization in Tableau. We’ll use a special kind of calculation called a quick table calculation. We’ll cover table calculations in extensive detail in Chapter 6, Diving Deep with Table Calculations. Here, simply follow these steps:

1. Create a stacked bar chart by placing Department Type on Rows, Number of Patient Visits on Columns, and Patient Risk Profile on Color. You’ll now have a single stacked bar chart.
2. Sort the bar chart in descending order.
3. Duplicate the Number of Patient Visits field on Columns by holding down Ctrl while dragging the Number of Patient Visits field in the view to a new spot on Columns, immediately to the right of its current location. Alternatively, you can drag and drop the field from the data pane to Columns. At this point, you have two Number of Patient Visits axes, which, in effect, duplicate the stacked bar chart:

Figure 3.33: An interim step when creating the stacked bars

4. Using the drop-down menu of the second Number of Patient Visits field, select Quick Table Calculation | Percent of Total. This table calculation runs a secondary calculation on the values that were returned from the data source to compute a percentage of the total. Here, you will need to further specify how that total should be computed.
5. Using the same drop-down menu, select Compute Using | Patient Risk Profile. This tells Tableau to calculate the percentage of each Patient Risk Profile within a given department. This means that the values will add up to 100% for each department.
6. Turn on labels by clicking the T (Text/label) button from the top-level toolbar. This turns on default labels for each mark:

Figure 3.34: This toolbar button toggles text labels on/off

After following the preceding steps, your completed stacked bar charts should appear as follows:

Figure 3.35: The final stacked bar view with absolute and relative values

Using both the absolute values and percentages in a single view can reveal significant aspects and details that might be obscured with only one of the charts.

Treemaps

Treemaps use a series of nested rectangles to display parts of the whole, especially within hierarchical relationships. Treemaps are particularly useful when you have hierarchies and dimensions with high cardinality (a high number of distinct values).

Here is an example of a treemap that shows the number of days spent in the hospital by patients. The largest rectangle sections show Department Type. Within those are departments and patients:

Figure 3.36: A treemap showing the part-to-whole relationship of Department Types/Departments/Doctors/Patients

To create a treemap, you simply need to place a measure on the Size shelf and a dimension on the Detail shelf. You can add additional dimensions to the level of detail to increase the detail of the view. Tableau will add borders of varying thickness to separate the levels of detail that are created by multiple dimensions. Note that in the preceding view, you can easily see the division of department types, then departments, then doctors, and finally individual patients. You can adjust the border of the lowest level by clicking the Color shelf.

The order of the dimensions on the Marks card defines the way the treemap groups the rectangles. Additionally, you can add dimensions to rows or columns to slice the treemap into multiple treemaps. The result is effectively a bar chart of treemaps:

Figure 3.37: Adding a dimension to Rows has effectively made a bar chart of treemaps

The preceding treemap not only demonstrates the ability to have multiple rows (or columns) of treemaps—it also demonstrates the technique of placing multiple fields on the Color shelf. This can only be done with discrete fields. You can assign two or more colors by holding down the Shift key while dropping the second field on color. Alternatively, the icon or space to the left of each field on the Marks card can be clicked to change which shelf is used for the field:

Figure 3.38: Clicking the icon next to a field on the Marks card allows you to change which shelf is used

Area charts

Take a line chart and then fill in the area beneath the line. If there are multiple lines, then stack the filled areas on top of each other. That’s how you might think of an area chart.

In fact, in Tableau, you may find it easier to create a line chart, like you’ve done previously, and then change the mark type on the Marks card to Area. Any dimensions on the Color, Label, or Detail shelves will create slices of the area that will be stacked on top of each other. The Size shelf is not applicable to an area chart.

As an example, consider a visualization of patient visits over time, segmented by hospital branch:

Figure 3.39: An area chart showing patient visits over time by hospital branch

Each band represents a different hospital branch location. In many ways, the view is aesthetically pleasing and it does highlight some patterns in the data. However, it suffers from some of the same weaknesses as the stacked bar chart. Only the bottom band (South) can be read in terms of the values on the axis.

The other bands are stacked on top and it becomes very difficult to compare. For example, it is obvious that there is a spike around February of each year. But is it at each branch? Or is one of the lower bands pushing the higher bands up? Which band has the most significant spike?

Now, consider the following view:

Figure 3.40: An area chart showing percentages instead of absolute values

This view uses a quick table calculation, like the stacked bars example. It is no longer possible to see the spikes, as in the first chart. However, it is much easier to see that there was a dramatic increase in the percentage of patients seen by the East branch (the middle band) around February 2019, and that the branch continued to see a significant number of patients through the end of the year.

It is important to understand what facets of the data story are emphasized (or hidden) by selecting a different chart type. You might even experiment in the Chapter 3 workbook by changing the first area chart to a line chart. You may notice that you can see the spikes as well as the absolute increase and decrease in patient visits per branch. Each chart type contributes to a certain aspect of the data story.

Pie charts

Pie charts can also be used to show part-to-whole relationships. To create a pie chart in Tableau, change the mark type to Pie. This will give you an Angle shelf, which you can use to encode a measure. Whatever dimension(s) you place on the Marks card (typically, on the Color shelf) will define the slices of the pie:

Figure 3.41: A pie chart showing total revenue broken down by branch

Observe that the preceding pie chart uses the sum of total revenue to define the angle of each slice; the higher the sum, the wider the slice. The Hospital Branch dimension is slicing the measure and defining slices of the pie. This view also demonstrates the ability to place multiple fields on the Label shelf. The second SUM(Revenue) field is the percentage of the total table calculation you saw previously. This allows you to see the absolute values of revenue, as well as a percentage of the whole.

With a good understanding of some techniques for visualizing part-to-whole relationships, let’s move on to visualizing distributions.

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 case, you most likely have a relatively high number of individuals. In data terms, you have a dimension (customer, patient, component, and student) representing a relatively large population of individuals and some measure (amount spent, length of stay, life expectancy, and test score) you’d like to compare. Using one or more of the following visualizations might be a good way to do this.

Circle charts

Circle charts are one way to visualize a distribution. Consider the following view, which shows how each doctor compares to other doctors within the same type of department in terms of the average days their patients stay in the hospital:

Figure 3.42: A circle chart showing the average length of stay for each doctor within each department type

Here, you can see which doctors have patients that stay in the hospital for longer or shorter durations than the average. It is also interesting to note that certain types of departments have longer average lengths of stay versus others. This makes sense as each type of department has patients with different needs. It’s probably not surprising that patients in Intensive Care tend to stay longer. Certain departments may have different goals or requirements. Being able to evaluate doctors within their type of department makes comparisons far more meaningful.

To create the preceding circle chart, you need to place the fields on the shelves that are shown and then simply change the mark type from Automatic (which was a bar mark) to Circle. Department Type defines the rows, and each circle is drawn at the level of Doctor, which is in the level of Detail on the Marks card.

Finally, to add the average lines, simply switch to the Analytics tab of the left-hand pane and drag Average Line to the view, specifically dropping it on the Cell option:

Figure 3.43: You can add reference lines and more by dragging from the Analytics tab to the view

You may also click one of the resulting average lines and select Edit to find fine-tuning options such as labeling, tooltips, and formatting.

Jittering

When using views like circle plots or other similar visualization types, you’ll often see that marks overlap, which can lead to obscuring parts of the true story. Do you know for certain, just by looking, how many doctors there are in Intensive Care who are above average? How many are below? Or could there be two or more circles exactly overlapping? One way of minimizing this is to click the Color shelf and add some transparency and a border to each circle. Another approach is a technique called jittering.

Here is one approach that uses the Index() function, computed along Doctor, as a continuous field on Rows. Since INDEX() is continuous (green), it defines an axis and causes the circles to spread out vertically.

Now, you can more clearly see each individual mark and have higher confidence that the overlap is not obscuring the true picture of the data:

Figure 3.44: Here, INDEX() has been added as a continuous field on Rows (the table calculation is computed along Doctor)

You can use jittering techniques on many kinds of visualizations that involve plotting fixed points that could theoretically overlap, such as dot plots and scatterplots. Next, we will move on to another useful distribution visualization technique: box and whisker plots.

Box and whisker plots

Box and whisker plots (sometimes just called box plots) add additional statistical context to distributions.To understand a box and whisker plot, consider the following diagram:

Figure 3.45: Explanation of a box and whisker plot

Here, the box plot has been added to a circle graph. The box is divided by the median, meaning that half of the values are above, and half are below. The box also indicates the lower and upper quartiles, which each contain a quarter of the values. The span of the box makes up what is known as the Interquartile Range (IQR). The whiskers extend to 1.5 times the IQR value (or the maximum extent of the data). Any marks beyond the whiskers are outliers.

To add box and whisker plots, use the Analytics tab on the left-hand sidebar and drag Box Plot to the view. Doing this to the circle chart we considered in Figure 3.42 yields the following chart:

Figure 3.46: A box plot applied to the previous circle chart

The box plots help us to see and compare the medians, the ranges of data, the concentration of values, and any outliers. You may edit box plots by clicking or right-clicking the box or whisker and selecting Edit. This will reveal multiple options, including how whiskers should be drawn, whether only outliers should be displayed, and other formatting possibilities.

Histograms

Another possibility for showing distributions is to use a histogram. A histogram looks similar to a bar chart, but the bars show a count of occurrences of a value. For example, standardized test auditors looking for evidence of grade tampering might construct a histogram of student test scores.

Typically, a distribution might look like the following example (not included in the workbook):

Figure 3.47: A histogram of test scores

The test scores are shown on the x axis and the height of each bar shows the number of students that made that particular score. A typical distribution often has a recognizable bell curve. In this case, some students are doing poorly and some are doing extremely well, but most have scores somewhere in the middle.

What if auditors saw something like this?

Figure 3.48: A histogram that does not have a typical bell curve, raising some questions

Something is clearly wrong. Perhaps graders have bumped up students who were just shy of passing to barely passing. It’s also possible this may indicate bias in subjective grading instead of blatant tampering. We shouldn’t jump to conclusions, but the pattern is not normal and requires investigation. Histograms are very useful in catching anomalies like this.

Now that we’ve seen an example of histograms, let’s shift our focus back to the hospital data and work through an example. What if you want to visualize the time it takes to begin patient treatment so that you can observe the patterns for different patient populations. You might want to create a histogram. In this example, we’ll start with a blank view and follow these steps:

1. Click to select the Minutes to Service field under Measures in the data pane
2. Expand Show Me if necessary and select the histogram

Upon selecting the histogram, Tableau builds the chart by creating a new dimension, Minutes to Service (bin), which is used in the view, along with a COUNT of Minutes to Service to render the view:

Figure 3.49: A histogram showing the distribution of patients according to minutes to service

For this view, Tableau automatically set the bin size to 3.47 minutes, which is not very intuitive. Experiment with different values by right-clicking or using the drop-down menu in the Minutes to Service (bin) field in the data pane and selecting Edit. The resulting window gives some information and allows you to adjust the size of the bins:

Figure 3.50: Options for editing a bin

Here, for example, is the same histogram with each bin sized to 2 minutes:

Figure 3.51: A histogram with a bin size of 2

You can see the curve, which peaks at just under 20 minutes and then tapers off with a few patients having to wait as long as 40 minutes.

You might pursue additional analysis, such as seeing how wait times vary for the majority of patients based on their risk profile, such as in this view:

Figure 3.52: Patient risk profile creates two rows of histograms, showing that most high-risk patients receive faster care (as we would hope)

You’ll also want to consider what you want to count for each bin and place that on Rows. When you used Show Me, Tableau placed the COUNT of Minutes to Service on Rows, which is just a count of every record where the value was not null. In this case, that’s equivalent to a count of patient visits because the dataset contains one record per visit. However, if you wanted to count the number of unique patients, you might consider replacing the field in the view with COUNTD([Patient ID]).

We’ve seen how to visualize distributions with circle plots, histograms, and box plots. Let’s turn our attention to using multiple axes to compare different measures.

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 the total revenue for a department and the number of patient visits:

Figure 3.53: A scatterplot showing the correlation between Revenue and Number of Patient Visits

The Department dimension is on the Text shelf and defines the view level of detail. Each mark in the view represents the total Revenue and total Number of Patient Visits for the department.

As you would expect, most departments have higher revenue with a higher volume of patients. Neurology and Cardiology stand out as having high revenue despite a lower volume of patients.

Let’s consider a few other types of charts that use multiple axes.

Dual-axis and combination charts

One very important feature in Tableau is the ability to use a dual axis. Scatterplots use two axes, but they are x and y. You also observed in the stacked bar example that placing multiple continuous (green) fields next to each other on Rows or Columns results in multiple side-by-side axes. Dual axis, on the other hand, means that a view is using two axes that are opposite each other with a common pane.

Here is an example (not included in the workbook) of using a dual axis for Sales and Profit:

Figure 3.54: A dual-axis chart with an indication of which field defines which axis

There are several key features of the view, which are as follows:

• The Sales and Profit fields on Rows indicate that they have a dual axis by sharing a flattened side.
• The axes defined by Sales and Profit are on opposing sides of the view. Also, note that they are not synchronized, which, in many cases, can give a distorted view of the data. It would be great if profit was that close to total sales! But it’s not. To synchronize the axes, right-click the right axis and select Synchronize Axis. If that option is grayed out, it is likely that one of the values is a whole number type and the other is a decimal type. You can change the data type of one of the fields by right-clicking it in the data pane and selecting Change Data Type | Number (Whole) or Number (Decimal).
• The Marks card is now an accordion-like control with an All section and a section for Sales and Profit. You can use this to customize marks for all measures or specifically customize marks for either Sales or Profit.

To create a dual axis, drag and drop two continuous (green) fields next to each other on Rows or Columns, then use the drop-down menu on the second, and select Dual Axis. Alternatively, you can drop the second field onto the canvas, opposite the existing axis.

A combination chart extends the use of dual axes to overlay different mark types. This is possible because the Marks card will give options for editing all marks or customizing marks for each individual axis.

As an example of a combination chart, consider the following visualization:

Figure 3.55: A combination chart that shows individual branch visits as lines and total visits as bars

This chart uses a combination of bars and lines to show the total number of patient visits over time (the bars) and the breakdown of patient visits by hospital branch over time (the lines). This kind of visualization can be quite effective at giving additional context to data.

There are several things to note about this view:

• The field on the Color shelf is listed as Multiple Fields and is gray on the Marks card. This indicates that different fields have been used for Color for each axis on Marks.
• The view demonstrates the ability to mix levels of detail in the same view. The bars are drawn at the highest level (patient visits for each month), while the lines have been drawn at a lower level (patient visits for each branch for each month).
• The view demonstrates the ability to use the same field (Patient Visits, in this case) multiple times on the same shelf (Rows, in this case).
• The second axis (the Patient Visits field on the right-hand side) has the header hidden to remove redundancy from the view. You can do this by unchecking Show Header from the drop-down menu on the field in the view or right-clicking the axis or header you wish to hide.

Dual-axis and combination charts open a wide range of possibilities for mixing mark types and levels of detail and are very useful for generating unique insights. We’ll see a few more examples of these throughout the rest of this book. Be sure to experiment with this feature and let your imagination run wild with all that can be done.

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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