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

Understanding the data

We will use Tableau to look at data preparation and data quality. Though we could also do these activities in R, we will use Tableau since it is a good way of seeing data quality issues and capturing them easily. We can also see problematic issues such as outliers or missing values.

Data preparation

When confronted with many variables, analysts usually start by building a decision tree and then using the variables that the decision tree algorithm has selected with other methods that suffer from the complexity of many variables, such as neural networks. However, decision trees perform worse when the problem at hand is not linearly separable.

In this section, we will use Tableau as a visual data preparation in order to prepare the data for further analysis. Here is a summary of some of the things we will explore:

  • Looking at columns that do not add any value to the model

  • Columns that have so many missing categorical values that they do not predict the outcome reliably

  • Review...