O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Advanced Analytics with R and Tableau

Book Description

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 advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.

Style and approach

Tableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced analytics by integrating Tableau with R and create beautiful data visualizations.

Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

Table of Contents

  1. Advanced Analytics with R and Tableau
    1. Table of Contents
    2. Advanced Analytics with R and Tableau
    3. Credits
    4. About the Authors
    5. About the Reviewers
    6. www.PacktPub.com
      1. eBooks, discount offers, and more
        1. Why subscribe?
    7. Customer Feedback
    8. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
        1. Downloading the example code
        2. Errata
        3. Piracy
        4. Questions
    9. 1. Advanced Analytics with R and Tableau
      1. Installing R for Windows
      2. RStudio
        1. Prerequisites for RStudio installation
      3. Implementing the scripts for the book
        1. Testing the scripting
      4. Tableau and R connectivity using Rserve
        1. Installing Rserve
        2. Configuring an Rserve Connection
      5. Summary
    10. 2. The Power of R
      1. Core essentials of R programming
        1. Variables
          1. Creating variables
          2. Working with variables
      2. Data structures in R
        1. Vector
        2. Lists
        3. Matrices
        4. Factors
      3. Data frames
      4. Control structures in R
        1. Assignment operators
        2. Logical operators
      5. For loops and vectorization in R
        1. For loops
      6. Functions
      7. Creating your own function
      8. Making R run more efficiently in Tableau
      9. Summary
    11. 3. A Methodology for Advanced Analytics Using Tableau and R
      1. Industry standard methodologies for analytics
      2. CRISP-DM
        1. Business understanding/data understanding
        2. CRISP-DM model — data preparation
        3. CRISP-DM — modeling phase
        4. CRISP-DM — evaluation
        5. CRISP-DM — deployment
        6. CRISP-DM — process restarted
        7. CRISP-DM summary
      3. Team Data Science Process
        1. Business understanding
        2. Data acquisition and understanding
        3. Modeling
        4. Deployment
        5. TDSP Summary
      4. Working with dirty data
      5. Introduction to dplyr
        1. Summarizing the data with dplyr
      6. Summary
    12. 4. Prediction with R and Tableau Using Regression
      1. Getting started with regression
        1. Simple linear regression
          1. Using lm() to conduct a simple linear regression
        2. Coefficients
        3. Residual standard error
      2. Comparing actual values with predicted results
        1. Investigating relationships in the data
        2. Replicating our results using R and Tableau together
      3. Getting started with multiple regression?
        1. Building our multiple regression model
        2. Confusion matrix
        3. Prerequisites
        4. Instructions
      4. Solving the business question
        1. What do the terms mean?
        2. Understanding the performance of the result
          1. Next steps
      5. Sharing our data analysis using Tableau
        1. Interpreting the results
      6. Summary
    13. 5. Classifying Data with Tableau
      1. Business understanding
      2. Understanding the data
        1. Data preparation
        2. Describing the data
          1. Data exploration
      3. Modeling in R
        1. Analyzing the results of the decision tree
      4. Model deployment
      5. Decision trees in Tableau using R
      6. Bayesian methods
      7. Graphs
        1. Terminology and representations
        2. Graph implementations
      8. Summary
    14. 6. Advanced Analytics Using Clustering
      1. What is Clustering?
      2. Finding clusters in data
        1. Why can't I drag my Clusters to the Analytics pane?
      3. Clustering in Tableau
        1. How does k-means work?
        2. How to do Clustering in Tableau
        3. Creating Clusters
      4. Clustering example in Tableau
        1. Creating a Tableau group from cluster results
        2. Constraints on saving Clusters
      5. Interpreting your results
      6. How Clustering Works in Tableau
        1. The clustering algorithm
      7. Scaling
      8. Clustering without using k-means
        1. Hierarchical modeling
      9. Statistics for Clustering
        1. Describing Clusters – Summary tab
          1. Testing your Clustering
        2. Describing Clusters – Models Tab
      10. Introduction to R
      11. Summary
    15. 7. Advanced Analytics with Unsupervised Learning
      1. What are neural networks?
        1. Different types of neural networks
      2. Backpropagation and Feedforward neural networks
      3. Evaluating a neural network model
      4. Neural network performance measures
        1. Receiver Operating Characteristic curve
        2. Precision and Recall curve
        3. Lift scores
      5. Visualizing neural network results
      6. Neural network in R
      7. Modeling and evaluating data in Tableau
        1. Using Tableau to evaluate data
      8. Summary
    16. 8. Interpreting Your Results for Your Audience
      1. Introduction to decision system and machine learning
      2. Decision system-based Bayesian
        1. Decision system-based fuzzy logic
      3. Bayesian Theory
      4. Fuzzy logic
      5. Building a simple decision system-based Bayesian theory
      6. Integrating a decision system and IoT project
      7. Building your own decision system-based IoT
        1. Wiring
        2. Writing the program
        3. Testing
        4. Enhancement
      8. Summary
      9. References
    17. Index