Chapter 8. Adding Value to Analysis – Trends, Distributions, and Forecasting

Sometimes, quick data visualization needs a little deeper analysis. For example, a simple scatterplot can reveal outliers and clusters of values. However, often, you want to understand the distribution. A simple time series helps you see the rise and fall of a measure over time. But many times, you want to see the trend or make predictions of future values.

Tableau enables you to quickly enhance your data visualizations with statistical analysis. Built-in features, such as trend models, distributions, and forecasting, allow you to quickly add value to your visual analysis. Additionally, Tableau integrates with R, an extensive statistical platform that opens up endless options for the statistical analysis of your data. This chapter will cover built-in statistical models and analysis.

This chapter will cover the following topics:

• Trending

• Forecasting

• Distributions

We'll take a look at these concepts in the context of a...

Trends

Let's say you are analyzing populations of various countries using the World Population dataset in the provided workbook. This dataset produces one record containing the population for each country for each year from 1960 to 2013. Create a view similar to the one shown in the following screenshot, which shows you the change in population over time for Afghanistan and Australia. You'll notice that Country Name has been filtered and added to the Color and Label shelves.

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 the population of Afghanistan started to recover. At some point around 1996, the population of Afghanistan exceeded that of Australia. The gap has grown wider ever since.

While we have a sense of the two trends, they become even more obvious when we see them. Tableau offers several ways to...

Distributions

Analyzing distributions can be quite useful. We've already seen that certain table calculations are available to determine 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 tab (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 to select the scope and the axis. In the following example, we've dragged and dropped Distribution Band from the Analytics tab onto the scope of Pane for the axis defined by Sum(Price):

Once you have selected the scope and axis, you will be given options to change settings. You may...

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 an idea about 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. Starting with version 8.0, 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). You may drag and drop Forecast from the Analytics tab, navigate to Analysis | 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 of Afghanistan and Australia with forecasts shown over time:

Note that when you show the forecast...

Summary

Tableau provides an extensive set of features to add 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 you extensive visibility into the trend models and even allows you to export data containing trend model predictions and residuals. Distributions are useful to understand how measurements are distributed. 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 turn our attention back to the data. We considered very early on how to connect to data, and we've been working with data ever since. However, we've spent most of our time working with clean, well-structured data. In the next chapter, we'll consider how to deal with messy data.