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

Core essentials of R programming


One of the reasons for R's success is its use of variables. Variables are used in all aspects of R programming. For example, variables can hold data, strings to access a database, whole models, queries, and test results. Variables are a key part of the modeling process, and their selection has a fundamental impact on the usefulness of the models. Therefore, variables are an important place to start since they are at the heart of R programming.

Variables

In the following section we will deal with the variables—how to create variables and working with variables.

Creating variables

It is very simple to create variables in R, and to save values in them. To create a variable, you simply need to give the variable a name, and assign a value to it.

In many other languages, such as SQL, it's necessary to specify the type of value that the variable will hold. So, for example, if the variable is designed to hold an integer or a string, then this is specified at the point...