RStudio for R Statistical Computing Cookbook

coverpage

RStudio for R Statistical Computing Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

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Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Chapter 1. Acquiring Data for Your Project

Introduction

Acquiring data from the Web – web scraping tasks

Accessing an API with R

Getting data from Twitter with the twitteR package

Getting data from Facebook with the Rfacebook package

Getting data from Google Analytics

Loading your data into R with rio packages

Converting file formats using the rio package

Chapter 2. Preparing for Analysis – Data Cleansing and Manipulation

Introduction

Getting a sense of your data structure with R

Preparing your data for analysis with the tidyr package

Detecting and removing missing values

Substituting missing values using the mice package

Detecting and removing outliers

Performing data filtering activities

Chapter 3. Basic Visualization Techniques

Introduction

Looking at your data using the plot() function

Using pairs.panel() to look at (visualize) correlations between variables

Adding text to a ggplot2 plot at a custom location

Changing axes appearance to ggplot2 plot (continous axes)

Producing a matrix of graphs with ggplot2

Drawing a route on a map with ggmap

Making use of the igraph package to draw a network

Showing communities in a network with the linkcomm package

Chapter 4. Advanced and Interactive Visualization

Introduction

Producing a Sankey diagram with the networkD3 package

Creating a dynamic force network with the visNetwork package

Building a rotating 3D graph and exporting it as a GIF

Using the DiagrammeR package to produce a process flow diagram in RStudio

Chapter 5. Power Programming with R

Introduction

Writing modular code in RStudio

Implementing parallel computation in R

Creating custom objects and methods in R using the S3 system

Evaluating your code performance using the profvis package

Comparing an alternative function's performance using the microbenchmarking package

Using GitHub with RStudio

Chapter 6. Domain-specific Applications

Introduction

Dealing with regular expressions

Analyzing PDF reports in a folder with the tm package

Creating word clouds with the wordcloud package

Performing a Twitter sentiment analysis

Detecting fraud in e-commerce orders with Benford's law

Measuring customer retention using cohort analysis in R

Making a recommendation engine

Performing time series decomposition using the stl() function

Exploring time series forecasting with forecast()

Tracking stock movements using the quantmod package

Optimizing portfolio composition and maximising returns with the Portfolio Analytics package

Forecasting the stock market

Chapter 7. Developing Static Reports

Introduction

Using one markup language for all types of documents – rmarkdown

Writing and styling PDF documents with RStudio

Writing wonderful tufte handouts with the tufte package and rmarkdown

Sharing your code and plots with slides

Curating a blog through RStudio

Chapter 8. Dynamic Reporting and Web Application Development

Introduction

Generating dynamic parametrized reports with R Markdown

Developing a single-file Shiny app

Changing a Shiny app UI based on user input

Creating an interactive report with Shiny

Constructing RStudio add-ins

Sharing your work on RPubs

Deploying your app on Amazon AWS with ramazon