Book Image

Tableau 10 Complete Reference

By : Joshua N. Milligan, Tristan Guillevin
Book Image

Tableau 10 Complete Reference

By: Joshua N. Milligan, Tristan Guillevin

Overview of this book

Graphical presentation of data enables us to easily understand complex data sets. Tableau 10 Complete Reference provides easy-to-follow recipes with several use cases and real-world business scenarios to get you up and running with Tableau 10. This Learning Path begins with the history of data visualization and its importance in today's businesses. You'll also be introduced to Tableau - how to connect, clean, and analyze data in this visual analytics software. Then, you'll learn how to apply what you've learned by creating some simple calculations in Tableau and using Table Calculations to help drive greater analysis from your data. Next, you'll explore different advanced chart types in Tableau. These chart types require you to have some understanding of the Tableau interface and understand basic calculations. You’ll study in detail all dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. Last but not least, you'll learn about the power of storytelling through the creation of interactive dashboards in Tableau. Through this Learning Path, you will gain confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau. This Learning Path includes content from the following Packt products: • Learning Tableau 10 - Second Edition by Joshua N. Milligan • Getting Started with Tableau 2018.x by Tristan Guillevin
Table of Contents (20 chapters)
Title Page
About Packt
Contributors
Preface
8
Deeper Analysis - Trends, Clustering, Distributions, and Forecasting
Index

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).

 

Bar charts

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...