Book Image

Mastering Tableau 2019.1 - Second Edition

By : Marleen Meier, David Baldwin
Book Image

Mastering Tableau 2019.1 - Second Edition

By: Marleen Meier, David Baldwin

Overview of this book

Tableau is one of the leading business intelligence (BI) tools used to solve BI and analytics challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. This book is also the second edition of the popular Mastering Tableau series, with new features, examples, and updated code. The book covers essential Tableau concepts and its advanced functionalities. Using Tableau Hyper and Tableau Prep, you’ll be able to handle and prepare data easily. You’ll gear up to perform complex joins, spatial joins, union, and data blending tasks using practical examples. Following this, you’ll learn how to perform data densification to make displaying granular data easier. Next, you’ll explore expert-level examples to help you with advanced calculations, mapping, and visual design using various Tableau extensions. With the help of examples, you’ll also learn about improving dashboard performance, connecting Tableau Server, and understanding data visualizations. In the final chapters, you’ll cover advanced use cases such as Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics, and learn to connect Tableau to R, Python, and MATLAB. By the end of this book, you’ll have mastered the advanced offerings of Tableau and be able to tackle common and not-so-common challenges faced in the BI domain.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Tableau Concepts, Basics
9
Section 2: Advanced Calculations, Mapping, Visualizations
16
Section 3: Connecting Tableau to R, Python, and Matlab

Introduction to quantiles

Quantiles are often considered to be synonymous with quartiles. They are not. Quantiles are the sets that make up an evenly-divided population of values. A quartile is a type of quantile—as is a quintile, a tercile, a decile, and so forth.

To understand how quantiles evenly divide a population of values, consider the following example from Tableau:

In the preceding example, our data points are 2 - 22 by even numbers. Quantiles are set to Quintiles. The Fourth Quintile is calculated thus: 22 * (4/5) = 17.6. The closest value in the population when rounding up is 18. Therefore, 18 is set as the rank that accounts for approximately four-fifths of the values of the total sorted population.

As evidenced in the preceding screenshot, Tableau allows you to view quantiles via right-clicking on an axis and choosing Add Reference Line > Distribution ...