Visual Analytics: Trends, Clustering, Distributions, and Forecasting

The rapid visual analysis that is possible using Tableau is incredibly useful for answering numerous questions and making key decisions. But it barely scratches the surface of the possible analysis. For example, a simple scatterplot can reveal outliers, but often, you want to understand the distribution or identify clusters of similar observations. A simple time series helps you to see the rise and fall of a measure over time, but often, you want to see the trend or make predictions for future values.

Tableau enables you to quickly enhance your data visualizations with statistical analysis. Built-in features such as trend models, clustering, distributions, and forecasting allow you to quickly add value to your visual analysis. Additionally, Tableau integrates with the R and Python platforms, which opens endless options for parsing and analyzing your data. We’ll look more at these in Chapter 13, Integrating...

Trends

World Population.xlsx is included in the Chapter 09 directory. It contains one record for each country for each year from 1960 to 2015, measuring population. Using this dataset, let’s take a look at the historical trends of various countries. Create a view similar to the one shown in the following screenshot, which shows the change in population over time for Afghanistan and Australia. You’ll notice that Country Name has been filtered to include only Afghanistan and Australia and the field has additionally been added to the Color and Label shelves:

Figure 9.1: Population values for Afghanistan and Australia over time

From this visualization alone, you can make several interesting observations. The growth of the two countries’ populations was fairly similar up to 1980. At that point, the population of Afghanistan went into decline until 1988, when it started to increase. At some point around 1996, the population of Afghanistan exceeded that...

Explain Data

With Explain Data, you can begin to uncover some of the reasons and explanations behind the data you see in your visualizations. Why are sales up so much at a specific point on the timeline? Why was profit down so much last month? Explain Data takes some of the tedious manual effort out of hunting for answers to questions like these.

To use Explain Data, simply select a mark in a view that you’d like to understand and select the Explain Data option on the tooltip toolbar (it looks like a lightbulb). For example, we’ll take the data shown in the Price vs. Size scatterplot we examined in Figure 9.13. Specifically we’ll examine the two most significant outliers.

First select the mark for 803 Parkhill Dr. Mansfield TX 76063 in the Testing predictions sheet (shown in Figure 9.21 and then click the Explain Data toolbar option as shown here:

Figure 9.21: Selecting a mark for Explain Data

This opens the Explain Data pane, which looks...

Clustering

Tableau gives you the ability to quickly perform clustering analysis in your visualizations. This allows you to find groups, or clusters, of individual data points that are similar based on any number of your choosing. This can be useful in many different industries and fields of study, as in the following examples:

• Marketing may find it useful to determine groups of customers related to each other based on spending amounts, frequency of purchases, or times and days of orders
• Patient care directors in hospitals may benefit from understanding groups of patients related to each other based on diagnoses, medication, length of stay, and the number of readmissions
• Immunologists may search for related strains of bacteria based on drug resistance or genetic markers
• Renewable energy consultants may like to pinpoint clusters of windmills based on energy production and then correlate that with geographic location

Tableau uses a standard...

Distributions

Analyzing distributions can be quite useful. We’ve already seen that certain calculations are available for determining statistical information such as averages, percentiles, and standard deviations. Tableau also makes it easy to quickly visualize various distributions, including confidence intervals, percentages, percentiles, quantiles, and standard deviations.

You may add any of these visual analytic features using the Analytics pane (alternately, you can right-click on an axis and select Add Reference Line). Just like reference lines and bands, distribution analytics can be applied within the scope of a table, pane, or cell. When you drag and drop the desired visual analytic, you’ll have options for selecting the scope and the axis. In the following example, we’ve dragged and dropped Distribution Band from the Analytics pane onto the scope of Pane for the axis defined by Sum(Price):

Figure 9.29: Defining the scope and axis as you...

Forecasting

As we’ve seen, trend models make predictions. Given a good model, you expect additional data to follow the trend. When the trend is over time, you can get some idea of where future values may fall. However, predicting future values often requires a different type of model. Factors such as seasonality can make a difference not predicted by a trend alone. Tableau includes built-in forecasting models that can be used to predict and visualize future values.

To use forecasting, you’ll need a view that includes a date field or enough date parts for Tableau to reconstruct a date (for example, a Year and a Month field). Tableau also allows forecasting based on integers instead of dates. You may drag and drop a forecast from the Analytics pane, select Analytics | Forecast | Show Forecast from the menu, or right-click on the view’s pane and select the option from the context menu.

Here, for example, is the view of the population growth over time for...

Summary

Tableau provides an extensive set of features for adding value to your analysis. Trend lines allow you to more precisely identify outliers, determine which values fall within the predictions of certain models, and even make predictions of where measurements are expected. Tableau gives extensive visibility into the trend models and even allows you to export data containing trend model predictions and residuals. Clusters enable you to find groups of related data points based on various factors. Distributions are useful for understanding the spread of values across a dataset. Forecasting allows a complex model of trends and seasonality to predict future results. Having a good understanding of these tools will give you the ability to clarify and validate your initial visual analyses.

Next, we’ll consider some advanced visualization types that will expand the horizons of what you are able to accomplish with Tableau and the way in which you communicate data to others...