R Data Science Essentials by Ravindran, Sharan Kumar -- Read -- Imperial Library of Trantor

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R Data Science Essentials

Table of Contents R Data Science Essentials Credits About the Authors About the Reviewers www.PacktPub.com

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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. Getting Started with R

Reading data from different sources Reading data from a database Data types in R

Variable data types

Data preprocessing techniques Performing data operations

Arithmetic operations on the data String operations on the data Aggregation operations on the data

Mean Median Sum Maximum and minimum Standard deviation

Control structures in R

Control structures – if and else Control structures – for Control structures – while Control structures – repeat and break Control structures – next and return

Bringing data to a usable format Summary

2. Exploratory Data Analysis

The Titanic dataset Descriptive statistics

Box plot Exercise

Inferential statistics Univariate analysis Bivariate analysis Multivariate analysis

Cross-tabulation analysis Graphical analysis

Summary

3. Pattern Discovery

Transactional datasets

Using the built-in dataset Building the dataset

Apriori analysis Support, confidence, and lift

Support Confidence Lift

Generating filtering rules Plotting

Dataset Rules

Sequential dataset Apriori sequence analysis Understanding the results

Reference

Business cases Summary

4. Segmentation Using Clustering

Datasets

Reading and formatting the dataset in R

Centroid-based clustering and an ideal number of clusters Implementation using K-means Visualizing the clusters Connectivity-based clustering Visualizing the connectivity Business use cases Summary

5. Developing Regression Models

Datasets Sampling the dataset Logistic regression Evaluating logistic regression Linear regression Evaluating linear regression Methods to improve the accuracy Ensemble models

Replacing NA with mean or median Removing the highly correlated values Removing outliers

Summary

6. Time Series Forecasting

Datasets Extracting patterns Forecasting using ARIMA Forecasting using Holt-Winters Methods to improve accuracy Summary

7. Recommendation Engine

Dataset and transformation Recommendations using user-based CF Recommendations using item-based CF Challenges and enhancements Summary

8. Communicating Data Analysis

Dataset Plotting using the googleVis package Creating an interactive dashboard using Shiny Summary

Index

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