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

Level of Detail Calculations

When we talk about Level of Detail (LOD) calculations in Tableau, we usually mean three expressions: FIXED, INCLUDE, and EXCLUDE. These three expressions open a world of options by providing the ability to create calculations that target specific levels of granularity. In older versions of Tableau, data granularity for a worksheet was established by the dimensions in a view. If the view contained dimensions for Region, State, and Postal Code but the author wanted to create a City level calculation, the City dimension would need to be included on the view. Furthermore, there was no mechanism for excluding or ignoring a given dimension on a view. Admittedly, the desired results could normally be obtained through some complex and sometimes convoluted use of table calculations, data blends, and so on. Fortunately, LOD calculations greatly simplify these...