Statistical Tableau (Ethan Lang) (Z-Library)

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Statistical Tableau How to Use Statistical Models and Decision Science in Tableau Ethan Lang

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Statistical Tableau by Ethan Lang Copyright © 2024 Ethan Lang. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800- 998-9938 or corporate@oreilly.com. Acquisitions Editor: Michelle Smith Development Editor: Sara Hunter Production Editor: Beth Kelly Copyeditor: nSight, Inc Proofreader: Krsta Technology Solutions Indexer: Sue Klefstad Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Kate Dullea May 2024: First Edition Revision History for the First Edition

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2024-05-02: First Release See http://oreilly.com/catalog/errata.csp? isbn=9781098151799 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Statistical Tableau, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-098-15179-9 [LSI]

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Preface I was first introduced to statistics and Tableau as an undergrad at the University of Kansas. I was enrolled in the engineering school, studying computer science, and I had to take a statistics course. That was really my first exposure to data and the power statistical analysis has. I told my guidance counselor in the semester after that course that I was interested in statistics and data in general. I asked if there was a degree that would lead to a career doing the types of analysis we conducted during the previous semester. At the time, the University of Kansas was just starting a brand new program called Business Analytics, and my counselor introduced me to the director of that program. I scheduled a call with him, and he told me all about the data industry and the growing career field available. I switched my major that afternoon and started my journey in data analytics. Throughout my coursework, I was eventually introduced to Tableau and, just like statistics, I immediately saw the impact it could have. I dove into the tool and eventually became the go-to resource for Tableau at work and in class. After that, I began finding ways to bring statistical analysis into Tableau. It is these strategies and tactics that inspired this book. Throughout my time in this career, I have had the opportunity to help solve some of the world’s biggest challenges using Tableau and statistical analysis. I have worked for brands all over the world and across every industry you can think of. I have had the honor to be named Tableau Ambassador, join the Veterans Advocacy Tableau

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User Group as colead, receive Tableau’s Viz of the Day several times, and become a member of the Tableau Speaker Bureau. All this has been made possible through my own curiosity and the amazing resources that the Tableau community has published. This Book’s Purpose Statistical analysis and data visualization are often considered two separate things. However, both disciplines rely on one another. To understand your data and make accurate predictions or assumptions through statistical analysis, you must visualize your data. On the other hand, to deliver actionable insights and enable your audience to get the most from data visualizations, we must back up our assumptions with some statistical analysis. This book is to help you bring statistics into your visualizations in Tableau. You will learn how to read and interpret statistical models, implement them in Tableau, and ultimately draw out actionable insights to present to your stakeholders. Most decisions using data come with risks; it is your responsibility to arm the decision maker with as much information as possible and to mitigate those risks. This Book’s Audience This book is best for readers who are looking to build on foundational knowledge about Tableau and statistics. As I mentioned before, it is your responsibility to arm your stakeholders with as much information as possible to help them mitigate the risks that come with decision making. That being said, I have two big caveats:

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Chapter 1 provides some of the foundational methods, definitions, and familiarization you need to get up to speed. For more advanced users, this chapter will likely be a review of core concepts that will be used in subsequent chapters. “With great power comes great responsibility.” This quote (attributed to Stan Lee) holds true for this book. Statistics is a very deep discipline. In this book, I will give you foundational knowledge to make predictions and assumptions about data. However, be careful when applying these tactics and always approach every analysis with caution. You need to mitigate risks, not increase them with incorrect assumptions. Do your research and conduct your analysis in an ethical manner. This Book’s Structure Chapter 1, “Introduction” Newer users of Tableau will get up to speed with the basics. I also introduce some definitions and foundational statistical concepts that we will build on as we progress through each chapter. Chapter 2, “Overview of the Analytics Pane” The Analytics pane is introduced in Tableau and gives you steps on how to access it from the authoring interface. Chapter 3, “Benchmarking in Tableau” I discuss what benchmarking is, how to apply benchmarking in your visualizations, and best practices when incorporating it in Tableau.

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Chapter 4, “Understanding Normal Distribution Using Histograms” In statistics, you often need to understand the distribution of your data to apply the appropriate method. In this chapter, I discuss how to quickly test data for normal distribution. Chapter 5, “Understanding Confidence Intervals” Confidence intervals are described, along with how to use them in Tableau and how to calculate them using custom calculated fields. Chapter 6, “Anomaly Detection on Normally Distributed Data” I introduce three methods you can implement to visually detect anomalies in your data. Chapter 7, “Anomaly Detection on Nonnormalized Data” Here are three more methods you can implement to visually detect anomalies in your data, even if it fails the normalization assumption. Chapter 8, “Linear Regression in Tableau” Linear regression is introduced, as is how to implement it in Tableau and how to understand the results of the model. Chapter 9, “Polynomial Regression in Tableau” Polynomial regression is discussed, along with how to implement it in Tableau and interpret the results of the model. Chapter 10, “Forecasting in Tableau”

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Exponential smoothing is discussed, as is how to implement this forecasting method in Tableau and how to interpret the results of the model. Chapter 11, “Clustering in Tableau” K-means clustering is introduced, along with how to implement this clustering method in Tableau and understand the results of the model. Chapter 12, “Creating an External Connection to R Using Tableau” I will show you how to download the appropriate software needed to make an external connection to R from Tableau. Chapter 13, “Creating an External Connection to Python Using Tableau” Here I will talk about Python and how to download the appropriate software needed to make an external connection from Tableau. Chapter 14, “Understanding Multiple Linear Regression in R and Python” Multiple linear regression is introduced, along with how to implement it in R and Python and interpret the results of the model. Chapter 15, “Using External Connections in Tableau” Here are several examples of using external connections in both R and Python to implement new modeling methods in Tableau. Conventions Used in This Book

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The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values determined by context. O’Reilly Online Learning NOTE For more than 40 years, O’Reilly Media has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-

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depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-889-8969 (in the United States or Canada) 707-827-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://www.oreilly.com/about/contact.html We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at https://oreil.ly/statistical-tableau. For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly- media

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Watch us on YouTube: https://youtube.com/oreillymedia Acknowledgments Thank you to the data community for always being supportive and encouraging. When getting started, I was lucky enough to have had so many fantastic resources I could turn to. Thank you to all the content creators and the folks who have supported me. Thank you to my mentors, Ryan Sleeper, Kaleb Gilliland, and many others. Without your support and guidance, it would have taken me years to get where I am professionally and personally. Thank you for taking the time and endowing me with your leadership and knowledge. Thank you to the technical reviewers of this book, Maddie Dierkes, Lorna Brown, Ann Jackson, and Christopher Gardner. Your feedback helped make this book 100 times better. Thank you Sara Hunter, development editor from O’Reilly. Your words of encouragement and continuous feedback helped make all this possible. Thank you so much; I couldn’t have done it without you. Thank you to my mom, dad, and brothers for believing in me and pushing me along the right path. I often needed a course correction, and you were always there to help steer me in the right direction, especially in the early days. Special thanks to my wife Sandra Lang and my kids Jameson, Ophelia, and Edalyn. Without your support and encouragement, I would never be able to create anything like this. Thank you guys for putting up with my crazy

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ideas, putting off home projects, and disappearing into my office for hours at a time to write. I love you all so much!

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Chapter 1. Introduction It is estimated that 70%–80% of job postings for a data analyst mention statistics as a desired skill or requirement. I haven’t found a way to prove those numbers myself, but looking at job postings, I would argue in favor of that estimate. With ever-increasing amounts of data, businesses are looking for ways to interpret and understand that data. Statistics is often the most scientific way to do that. However, I think many analysts and Tableau developers struggle to implement statistics into their analysis or data visualizations. There are many reasons for this, and I will be the first one to tell you that it is not for lack of trying. Statistics can be intimidating for both developers and the stakeholders who rely on their reports. Trying to explain and interpret complex statistical equations is tough without a firm understanding of the discipline. That is the exact purpose of this book. I want to equip you with that firm understanding of statistics and give you the confidence to speak to the equations and implement them in your work. In this book, I will be focusing on bringing data visualization in Tableau together with statistical analysis so that you can support your insights with scientific evidence. In this chapter, I will introduce you to some common Tableau terminology I will be using throughout the book. I am also going to introduce you to some basic statistical terms and ideas. Toward the end of the chapter, I will present you with a case study that ties both disciplines together, and I will discuss the importance of visualizing statistical results.

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Introduction to Tableau It is important to understand that Tableau is not simply a data visualization tool, but a company with a suite of tools to support data visualization and analytics at an enterprise level. There are many products within Tableau’s ecosystem, including Tableau Desktop, Tableau Cloud, Tableau Server, Tableau Prep Builder, Tableau Public, and more. Some of these products require a license to use, while others, such as Tableau Public, do not require you to purchase a license, but there are certain limitations. With a license, you can publish your workbooks to Tableau Server or Tableau Cloud from Tableau Desktop. This allows your users to view and interact with your data visualizations from a browser. Checkout the Tableau website for a full list of all Tableau’s products. Common Terms of the Authoring Interface of Tableau Desktop There are several common terms within Tableau Desktop that I want you to know and be familiar with. To begin with, when you open Tableau Desktop, you will land on the Start Page, as shown in Figure 1-1.

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Figure 1-1. Start Page of Tableau Desktop From the Start Page, you can connect to the data you want to visualize. Tableau has hundreds of connectors that you can use to access your data. A connector is basically like a built-in API that allows you to establish a connection to a database or file type to read that data into Tableau Desktop. On the lefthand side of the Start Page you can explore all the connectors that are available. For all the demonstrations in this book, I will be using the Sample - Superstore dataset. To connect to this dataset, simply click on Sample - Superstore, as shown in Figure 1-2.

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Figure 1-2. Connecting to Sample - Superstore

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It is important to note that if you are using a different version of Tableau Desktop than I am, you may get different results. Tableau will occasionally update the Sample - Superstore dataset. I will be using version 2023.2 throughout this book. If you want to follow along exactly, you can download this version from Tableau’s product support page. After clicking on the sample dataset, you will be navigated from the Start Page to Tableau Desktop’s authoring interface, as shown in Figure 1-3. Figure 1-3. Tableau Desktop’s authoring interface To introduce you to the terms I will use throughout the book, on the lefthand side, you will find the Data pane, as shown in Figure 1-4. At the top of the Data pane, you will see a list of the data sources you are connected to. Moving down, you will find a list of fields, including those that are calculated, separated by data source and whether Tableau believes that field is a measure or dimension.

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Figure 1-4. The Data pane of the authoring interface

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To the right of the Data pane, you will find the different components used to create visualizations called shelves. There is the Marks shelf, Filters shelf, Pages shelf, Columns shelf, Rows shelf, and canvas, as shown in Figure 1-5. Figure 1-5. Key features of the authoring interface To define each a little further, here is a brief explanation of each: Marks shelf The Marks shelf is a key element on the authoring interface and allows you to drag fields into different properties that