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

Interpreting your results


Sometimes groupings in data make immediate sense. When clustering by income and age, one could come across a group that can be labeled as young professionals.

In UN development indicators dataset, using the Describe dialog, one can clearly see that Cluster 1, Cluster 2, and Cluster 3 correspond to Underdeveloped, Developing, and Highly Developed countries, respectively. By doing so we're using k-means to compress the information that is contained in three columns and 180+ rows to just three labels. Clustering can sometimes also find patterns your dataset may not be able to sufficiently explain by itself.

For example, as you're clustering health records, you may find two distinct groups and why? is not immediately clear and describable with the available data, which may lead you to ask more questions and maybe later realize that difference was because one group exercised regularly while the other didn't, or one had an immunity to a certain disease. It may even indicate...