Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Sharing our data analysis using Tableau


R gives you good diagnostic information to help you take the next step in your analysis, which is to visualize the results.

Interpreting the results

Statistics provides us with a method of investigation where other methods haven't been able to help, and their success or failure isn't clear to many people. If we see a correlation and think that the relationship is obvious, then we need to think again. Correlation can help people to insinuate causation. It's often said that correlation is not causation, but what does this mean? Correlation is a measure of how closely related two things are. We can use other statistical methods, such as structural equation modeling, to help us to identify the direction of the relationship, if it exists, using correlated data. It's a complex field in itself, and it isn't covered in this book; the main point here is to show that this is a complex question.

How does correlation help us here? For our purposes, the most interesting...