Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / Jen Stirrup, Ruben Oliva Ramos.

Format:

Book

Author/Creator:

Stirrup, Jen, author.

Oliva Ramos, Rubén, author.

Language:

English

Subjects (All):

R (Computer program language).

Physical Description:

1 online resource (178 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham : Packt, 2017.

System Details:

text file

Biography/History:

Oliva Ramos Ruben: Ruben Oliva Ramos is a computer systems engineer from Tecnologico de Leon Institute, with a master's degree in computer and electronic systems engineering and a specialization in teleinformatics and networking from the University of Salle Bajio in Leon, Guanajuato, Mexico. He has more than 5 years of experience of developing web applications to control and monitor devices connected with Arduino and Raspberry Pi, using web frameworks and cloud services to build the Internet of Things applications. He is a mechatronics teacher at the University of Salle Bajio and teaches students of the master's degree in design and engineering of mechatronics systems. Ruben also works at Centro de Bachillerato Tecnologico Industrial 225 teaching subjects such as electronics, robotics and control, automation, and microcontrollers. He is a consultant and developer for projects in areas such as monitoring systems and datalogger data using technologies (such as Android, iOS, HTML5, and ASP. NET), databases (such as SQlite, MongoDB, and MySQL), web servers, hardware programming, and control and monitor systems for data acquisition and programming. Stirrup Jen: Jen Stirrup is a data strategist and technologist, a Microsoft Most Valuable Professional (MVP), and a Microsoft Regional Director, a tech community advocate, a public speaker and blogger, a published author, and a keynote speaker. Jen is the founder of a boutique consultancy based in the UK, Data Relish, which focuses on delivering successful business intelligence and artificial intelligence solutions that add real value to customers worldwide. She has featured on the BBC as a guest expert on topics relating to data.

Summary:

Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of R About This Book A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R Combine Tableau analytics and visualization with the power of R using this step-by-step guide Wondering how R can be used with Tableau? This book is your one-stop solution. Who This Book Is For This book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau. A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book. What You Will Learn Integrate Tableau's analytics with the industry-standard, statistical prowess of R. Make R function calls in Tableau, and visualize R functions with Tableau using RServe. Use the CRISP-DM methodology to create a roadmap for analytics investigations. Implement various supervised and unsupervised learning algorithms in R to return values to Tableau. Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions. In Detail 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 advan...

Contents:

Cover

Copyright

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Advanced Analytics with R and Tableau

Installing R for Windows

RStudio

Prerequisites for RStudio installation

Implementing the scripts for the book

Testing the scripting

Tableau and R connectivity using Rserve

Installing Rserve

Configuring an Rserve Connection

Summary

Chapter 2: The Power of R

Core essentials of R programming

Variables

Creating variables

Working with variables

Data structures in R

Vector

Lists

Matrices

Factors

Data frames

Control structures in R

Assignment operators

Logical operators

For loops and vectorization in R

For loops

Functions

Creating your own function

Making R run more efficiently in Tableau

Chapter 3: A Methodology for Advanced Analytics Using Tableau and R

Industry standard methodologies for analytics

CRISP-DM

Business understanding/data understanding

CRISP-DM model - data preparation

CRISP-DM - modeling phase

CRISP-DM - evaluation

CRISP-DM - deployment

CRISP-DM - process restarted

CRISP-DM summary

Team Data Science Process

Business understanding

Data acquisition and understanding

Modeling

Deployment

TDSP Summary

Working with dirty data

Introduction to dplyr

Summarizing the data with dplyr

Chapter 4: Prediction with R and Tableau Using Regression

Getting started with regression

Simple linear regression

Using lm() to conduct a simple linear regression

Coefficients

Residual standard error

Comparing actual values with predicted results

Investigating relationships in the data

Replicating our results using R and Tableau together

Getting started with multiple regression?.

Building our multiple regression model

Confusion matrix

Prerequisites

Instructions

Solving the business question

What do the terms mean?

Understanding the performance of the result

Next steps

Sharing our data analysis using Tableau

Interpreting the results

Chapter 5: Classifying Data with Tableau

Understanding the data

Data preparation

Describing the data

Data exploration

Modeling in R

Analyzing the results of the decision tree

Model deployment

Decision trees in Tableau using R

Bayesian methods

Graphs

Terminology and representations

Graph implementations

Chapter 6: Advanced Analytics Using Clustering

What is Clustering?

Finding clusters in data

Why can't I drag my Clusters to the Analytics pane?

Clustering in Tableau

How does k-means work?

How to do Clustering in Tableau

Creating Clusters

Clustering example in Tableau

Creating a Tableau group from cluster results

Constraints on saving Clusters

Interpreting your results

How Clustering Works in Tableau

The clustering algorithm

Scaling

Clustering without using k-means

Hierarchical modeling

Statistics for Clustering

Describing Clusters - Summary tab

Testing your Clustering

Describing Clusters - Models Tab

Introduction to R

Chapter 7: Advanced Analytics with Unsupervised Learning

What are neural networks?

Different types of neural networks

Backpropagation and Feedforward neural networks

Evaluating a neural network model

Neural network performance measures

Receiver Operating Characteristic curve

Precision and Recall curve

Lift scores

Visualizing neural network results

Neural network in R

Modeling and evaluating data in Tableau

Using Tableau to evaluate data

Summary.

Chapter 8: Interpreting Your Results for Your Audience

Introduction to decision system and machine learning

Decision system-based Bayesian

Decision system-based fuzzy logic

Bayesian Theory

Fuzzy logic

Building a simple decision system-based Bayesian theory

Integrating a decision system and IoT project

Building your own decision system-based IoT

Wiring

Writing the program

Testing

Enhancement

References

Index.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed August 31, 2017).

ISBN:

1-5231-2523-3

OCLC:

1002698280