Book description
Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature
About This Book
 54 useful and practical tasks to improve working systems
 Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on
 Offers 1015 reallife, practical improvements for each user type
Who This Book Is For
This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable.
What You Will Learn
 Familiarize yourself with the latest advanced R console features
 Create advanced and interactive graphics
 Manage your R project and project files effectively
 Perform reproducible statistical analyses in your R projects
 Use RStudio to design predictive models for a specific domainbased application
 Use RStudio to effectively communicate your analyses results and even publish them to a blog
 Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product
In Detail
The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing realtime visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment.
This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get handson with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications.
Style and approach
RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment.
This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are:
 Accessing an API with R
 Substituting missing values by interpolation
 Performing data filtering activities
 R Statistical implementation for Geospatial data
 Developing shiny addins to expand RStudio functionalities
 Using GitHub with RStudio
 Modelling a recommendation engine with R
 Using R Markdown for static and dynamic reporting
 Curating a blog through RStudio
 Advanced statistical modelling with R and RStudio
Publisher resources
Table of contents

RStudio for R Statistical Computing Cookbook
 Table of Contents
 RStudio for R Statistical Computing Cookbook
 Credits
 About the Author
 About the Reviewer
 www.PacktPub.com
 Preface

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
 2. Preparing for Analysis – Data Cleansing and Manipulation

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
 4. Advanced and Interactive Visualization

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

6. Domainspecific 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 ecommerce 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
 7. Developing Static Reports
 8. Dynamic Reporting and Web Application Development
 Index
Product information
 Title: RStudio for R Statistical Computing Cookbook
 Author(s):
 Release date: April 2016
 Publisher(s): Packt Publishing
 ISBN: 9781784391034
You might also like
video
Python Fundamentals
51+ hours of video instruction. Overview The professional programmer’s Deitel® video guide to Python development with …
book
Fluent Python, 2nd Edition
Python’s simplicity lets you become productive quickly, but often this means you aren’t using everything it …
book
HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
book
Building Microservices, 2nd Edition
Distributed systems have become more finegrained as organizations shift from codeheavy monolithic applications to smaller, selfcontained …