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

Clustering example in Tableau


In the example, we are going to use clustering to drag the cluster pill from the sheet into the data pane on the left. You can treat the resulting field as a group. In your visualizations, Tableau will treat the cluster field like any other visualization.

These include standardization of inputs that automatically scale the data and multiple correspondence analyses (if you're curious about the details, you can find out more about the math behind clustering in product documentation at http://onlinehelp.tableau.com/v10.0/pro/desktop/en-us/help.html#clustering.html).

This means you can work with many different types of data with minimal preparation. You can include categorical fields such as education level in your clustering analysis alongside numeric variables such as income without worries or use it for clustering survey responses where all inputs could be categorical.

Creating a Tableau group from cluster results

Drag Clusters from the Marks card (or from any other...