Data Analytics with R

Description

Data analysis is the method of examining, cleansing, and modeling with the objective of determining useful information for effective decision-making and operations. It includes diverse techniques and tools and plays a major role in different business, science and social science areas. R software provides numerous functions and packages for using different techniques for producing desired outcome. Data Analytics with R will enable readers gain sufficient knowledge and experience to perform analysis using different analytical tools available in R. Each chapter begins with a number of important and interesting examples taken from a variety of sectors.

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About the Author

Dr. Bharti Motwani is an analytical professional, IT and analytics consultant, result-driven and articulate academician who can think out of the box, with more than 21 years of experience in corporate world and academics. She has written more than 75 research papers in leading national and international journals and books, including journals indexed in Scopus. She is the recipient of Young Scientist of the Year award (2015) from the Institute of Research and Journals. She has proved dexterousness in research methodologies and software development by conducting various seminars and workshops related to latest tools in research and software and guiding various research and software projects.

Table of Contents

PART 1 Basics of R

Chapter 1 Introduction to R

1.1 Features of R

1.2 Installation of R

1.3 Getting Started

1.4 Variables in R

1.5 Input of Data

1.6 Output in R

1.7 In-Built Functions in R

1.8 Packages in R

Chapter 2 Data Types of R

2.1 Vectors

2.2 Matrices

2.3 Arrays

2.4 Lists

2.5 Factors

2.6 Data Frame

Chapter 3 Programming in R

3.1 Decision-Making Structures

3.2 Loops

3.3 User-Defined Functions

3.4 User-Defined Package

3.5 Reports using Rmarkdown

Chapter 4 Data Exploration and Manipulation

4.1 Missing Data Management

4.2 Data Reshaping through Melting and Casting

4.3 Special Functions across Data Elements

Chapter 5 Import and Export of Data

5.1 Import and Export of Data in Text File

5.2 Import and Export of Data in Excel

5.3 Import and Export of Data in XML

5.4 Import and Export of Data in JSON

5.5 Import and Export of Data in MySQL

5.6 Import and Export of Data in SPSS

5.7 Import and Export of Data in SAS

PART 2 Visualization Techniques

Chapter 6 Basic Visualization

6.1 Pie Chart

6.2 Bar Chart

6.3 Histograms

6.4 Line Chart

6.5 Kernel Density Plots

6.6 Quantile-Quantile (Q-Q) Plot

6.7 Box-and-Whisker Plot

6.8 Violin Plot

6.9 Dot Chart

6.10 Bubble Plot

6.11 Image Plot

6.12 Mosaic Plot

Chapter 7 Advanced Visualization

7.1 Scatter Plot

7.2 Corrgrams

7.3 Star and Segment Plots

7.4 Tree Maps

7.5 Heat Map

7.6 Perspective and Contour Plot

7.7 Using ggplot2 for Advanced Graphics

PART 3 Statistical Analysis

Chapter 8 Basic Statistics

8.1 Descriptive Statistics

8.2 Table in R

8.3 Correlation and Covariance

8.4 Simulation and Distributions

8.5 Reproducing Same Data

Case Study: Web Analytics using Goal Funnels: Asset for e-Commerce Business

Chapter 9 Compare Means

9.1 Parametric Techniques

Case Study: Green Building Certification

Case Study: Comparison of Personal Web Store and Marketplaces for Online Selling

Case Study: Effect of Training Program on Employee Performance

Case Study: Effect of Demographics on Online Mobile Shopping Apps

9.2 Non-Parametric Tests

Chapter 10 Time-Series Models

10.1 Time-Series Object in R

10.2 Smoothing

10.3 Seasonal Decomposition

10.4 ARIMA Modeling

10.5 Survival Analysis

Case Study: Foreign Trade in India

PART 4 Machine Learning

Chapter 11 Unsupervised Machine Learning Algorithms

11.1 Dimensionality Reduction

Case Study: Balanced Scorecard Model for Measuring Organizational Performance

Case Study: Employee Attrition in an Organization

11.2 Clustering

Case Study: Market Capitalization Categories

Case Study: Performance Appraisal in Organizations

Chapter 12 Supervised Machine Learning Problems

12.1 Regression

Case Study: Relationship between Buying Intention and Awareness of Electric Vehicles

Case Study: Application of Technology Acceptance Model in Cloud Computing

Case Study: Impact of Social Networking Websites on Quality of Recruitment

12.2 Classification

Case Study: Prediction of Customer Buying Intention due to Digital Marketing

Chapter 13 Supervised Machine Learning Algorithms

13.1 Naïve Bayes Algorithm

Case Study: Measuring Acceptability of a New Product

13.2 k-Nearest Neighbor’s (KNN) Algorithm

Case Study: Predicting Phishing Websites

Case Study: Loan Categorization

13.3 Support Vector Machines (SVMs)

Case Study: Fraud Analysis for Credit Card and Mobile Payment Transactions

Case Study: Diagnosis and Treatment of Diseases

13.4 Decision Trees

Case Study: Occupancy Detection in Buildings

Case Study: Artificial Intelligence and Employment

Chapter 14 Supervised Machine Learning Ensemble Techniques

14.1 Bagging

Case Study: Measuring Customer Satisfaction related to Online Food Portals

Case Study: Predicting Income of a Person

14.2 Random Forest

Case Study: Writing Recommendation/Approval Reports

Case Study: Prediction of Sports Results

14.3 Gradient Boosting

Case Study: Impact of Online Reviews on Buying Behavior

Case Study: Effective Vacation Plan through Online Services

Chapter 15 Machine Learning for Text Data

15.1 Text Mining

Case Study: Spam Protection and Filtering

15.2 Sentiment Analysis

Case Study: Determining Online News Popularity

Chapter 16 Neural Network Models (Deep Learning)

16.1 Steps for Building a Neural Network Model

16.2 Multilayer Perceptrons Model (2D Tensor)

Case Study: Measuring Quality of Products for Acceptance or Rejection

16.3 Recurrent Neural Network Model (3D Tensor)

Case Study: Financial Market Analysis

16.4 Convolutional Neural Network Model (4D Tensor)

Case Study: Facial Recognition in Security Systems

Answers to Objective Type Questions

Index

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Data Analytics with R

Description

About the Author