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
About the Authors
About the Reviewers
Customer Feedback

Clustering in Tableau

Tableau's power has always been its user-focused flexibility, and working with the user in order to achieve insights at the speed of thought. Tableau's clustering functionality continues the tradition of putting the user front-and-center of the analytics process. So, for example, Tableau allows us to quickly customize geographical areas, for example, which in turn can yield new insights and patterns held within the groups.

Tableau 10.0 comes with k-means clustering as a built-in function. K-means is a popular clustering algorithm that is useful, easy to implement, and it can be faster than some other clustering methods, particularly in the case of big datasets.

We can see the data being grouped, or clustered, around centers. The algorithm works firstly by choosing the cluster centers randomly. Then, it works out the nearest cluster centers, and arranges the data points around it.

K-means then works out the actual cluster center. It then reassigns the data points to the...