R Data Science Essentials电子书

R Data Science Essentials

Table of Contents

R Data Science Essentials

Credits

About the Authors

About the Reviewers

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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