Home Page Icon
Home Page
Table of Contents for
Table of Contents
Close
Table of Contents
by Ruben Oliva Ramos, Jen Stirrup
Advanced Analytics with R and Tableau
Advanced Analytics with R and Tableau
Table of Contents
Advanced Analytics with R and Tableau
Credits
About the Authors
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
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
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
Summary
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
Summary
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
Summary
5. Classifying Data with Tableau
Business understanding
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
Summary
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
Summary
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
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
Summary
References
Index
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Cover
Next
Next Chapter
Advanced Analytics with R and Tableau
Table of Contents
Advanced Analytics with R and Tableau
Credits
About the Authors
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
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
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
Summary
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
Summary
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
Summary
5. Classifying Data with Tableau
Business understanding
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
Summary
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
Summary
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
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
Summary
References
Index
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset