Mastering RStudio – Develop, Communicate, and Collaborate with R

By Hillebrand Julian and Nierhoff Maximilian H.

Harness the power of RStudio to create web applications, R packages, markdown reports and pretty data visualizations

About This Book

- Discover the multi-functional use of RStudio to support your daily work with R code
- Learn to create stunning, meaningful, and interactive graphs and learn to embed them into easy communicable reports using multiple R packages
- Develop your own R packages and Shiny web apps to share your knowledge and collaborate with others

Who This Book Is For

This book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio’s functionality to ease their development efforts. R programming experience is assumed as well as being comfortable with R’s basic structures and a number of functions.

What You Will Learn

- Discover the RStudio IDE and details about the user interface
- Communicate your insights with R Markdown in static and interactive ways
- Learn how to use different graphic systems to visualize your data
- Build interactive web applications with the Shiny framework to present and share your results
- Understand the process of package development and assemble your own R packages
- Easily collaborate with other people on your projects by using Git and GitHub
- Manage the R environment for your organization with RStudio and Shiny server
- Apply your obtained knowledge about RStudio and R development to create a real-world dashboard solution

In Detail

RStudio helps you to manage small to large projects by giving you a multi-functional integrated development environment, combined with the power and flexibility of the R programming language, which is becoming the bridge language of data science for developers and analyst worldwide. Mastering the use of RStudio will help you to solve real-world data problems.
This book begins by guiding you through the installation of RStudio and explaining the user interface step by step. From there, the next logical step is to use this knowledge to improve your data analysis workflow. We will do this by building up our toolbox to create interactive reports and graphs or even web applications with Shiny. To collaborate with others, we will explore how to use Git and GitHub with RStudio and how to build your own packages to ensure top quality results. Finally, we put it all together in an interactive dashboard written with R.

Style and approach

An easy-to-follow guide full of hands-on examples to master RStudio.
Beginning from explaining the basics, each topic is explained with a lot of details for every feature.

• Language: English
• Publisher: Packt Publishing
• Release date: Dec 4, 2015
• ISBN: 9781783982554

Book preview

Table of Contents

Mastering RStudio – Develop, Communicate, and Collaborate with R

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Errata

Piracy

Questions

1. The RStudio IDE – an Overview

Downloading and installing RStudio

Installing R

For Ubuntu

Using RStudio with different versions of R

Windows

Ubuntu

Updating RStudio

Getting to know the RStudio interface

The four main panes

The Source editor pane

Syntax highlighting

Code completion

Executing R Code from the source pane

Code folding

Debugging code

The Environment and History panes

History pane

Console pane

The Files, Plots, Packages, Help, and Viewer panes

The Files pane

The Plot pane

The Packages pane

The Help pane

The Viewer pane

Customizing RStudio

Using keyboard shortcuts

Working with RStudio and projects

Creating a project with RStudio

Locating your project

Using RStudio with Dropbox

Preventing Dropbox synchronization conflicts

Creating your first project

Organizing your folders

Saving the data

Analyzing the data

Correcting the path for report exporting

Exporting your analysis as a report

Summary

2. Communicating Your Work with R Markdown

The concept of reproducible research

Doing reproducible research with R Markdown

What is Markdown?

What is literate programming?

A brief side note on Sweave

Dynamic report generation with knitr

What is R Markdown?

A side note about LaTeX

Configuring R Markdown

Getting started with R Markdown in RStudio

Creating your first R Markdown document

The R Markdown interface

Inspecting the R Markdowns panes

Explaining the R Markdown File pane settings

File tab arrows

Saving current document

Spell check

Find/replace

Question mark

Knit HTML

Gear icon

Output Format: HTML

Output Format – PDF

Output Format – Word

Run and re-run icons

Chunks

Jump to menu

Viewer pane options

Advanced R Markdown documents

Getting to know R code chunks

Customizing R code chunks

Chunk options

Avoiding errors, warnings, and other messages

Hiding distracting lines of code

Embedding R code inline

Labeling code chunks

Pandoc and knitr options

Output formats

Changing the look of the output

Using a custom CSS style sheet

Using R Markdown templates

Package vignette

The Tufte handout

Compiling R Notebooks

Generating R Markdown presentations

ioslides

Slidy

Beamer

Summary

3. R Lesson I – Graphics System

The graphic system in R

An introduction to the graphic devices

The R graphics package—base

Creating base plots

Using the base graphics

Base graphics parameters

Annotating with base plotting functions

Introducing the lattice package

Creating lattice plots

Getting to know the lattice plot types

The lattice panel functions

Lattice key points summary

Introducing ggplot2

Looking at the history of ggplot2

The Grammar of Graphics

Applying The Grammar of Graphics with ggplot2

Using ggplot2

Installing the ggplot2 package

Qplot() and ggplot()

Creating your first graph with ggplot2

Modifying ggplot objects with the plus operator

Setting the aesthetics parameter

Adding layers using geoms

Choosing the right geom

Modifying parameters

Changing the color of your plot

Changing the shape

Changing the size

Saving ggplot objects in variables

Using stats layers

Saving ggplot graphs

Customizing your charts

Subsetting your data

Setting titles

Changing the axis labels

Swapping the X and Y axes

Improving the look of ggplot2 charts

Creating graphs with the economist theme

Creating graphs with the wall street journal theme

Interactive plotting systems

Introducing ggvis

Our first ggvis graphic

Interactive ggvis graphs

A look at the rCharts package

Using googleVis

HTML widgets

dygraphs

Leaflet

rbokeh

Summary

4. Shiny – a Web-app Framework for R

Introducing Shiny – the app framework

Creating a new Shiny web app with RStudio

Creating your first Shiny application

Sketching the final app

Constructing the user interface for your app

Creating the server file

The final application

Deconstructing the final app into its components

The components of the user interface

The server file in detail

The connection between the server and the ui file

The concept of reactivity

The source and endpoint structure

The purpose of the reactive conductor

Discovering the scope of the Shiny user interface

Exploring the Shiny interface layouts

The sidebar layout

The grid layout

The tabset panel layout

The navlist panel layout

The navbar page as the page layout

Adding widgets to your application

Shiny input elements

A brief overview of the output elements

Individualizing your app even further with Shiny tags

Creating dynamic user interface elements

Using conditionalPanel

Taking advantage of the renderUI function

Sharing your Shiny application with others

Offering a download of your Shiny app

Gist

GitHub

Zip file

Package

Deploying your app to the web

Shinyapps.io

Setting up a self-hosted Shiny server

Diving into the Shiny ecosystem

Creating apps with more files

Expanding the Shiny package

Summary

5. Interactive Documents with R Markdown

Creating interactive documents with R Markdown

Using R Markdown and Shiny

Shiny Document

Shiny Presentation

Disassembling a Shiny R Markdown document

Embedding interactive charts into R Markdown

Using ggvis for interactive R Markdown documents

rCharts

googleVis

HTML widgets

dygraphs

Three.js and R

networkD3

metricsgraphics

Publishing interactive R Markdown documents

Summary

6. Creating Professional Dashboards with R and Shiny

Explaining the concept of dashboards

Introducing the shinydashboard package

Installing shinydashboard

Explaining the structure of shinydashboard

Showing the elements of shinydashboard

Header elements

Sidebar elements

Body elements

Boxes

FluidRows

InfoBox and valueBox

Building your own KPI dashboard

Creating our data architecture

Sketching the look of our dashboard

Transferring our plan into R code

Considering a file and folder structure

Accessing our data sources

MySQL – the customer data

Dropbox – our data storage system

Google Analytics – the website data

Twitter – the social data

Google Sheets – the inventory data

Putting it all together

Creating the Twitter engagement box

Summary

7. Package Development in RStudio

Understanding R packages

Understanding the package structure

Installing devtools

Building packages with RStudio

Creating a new package project with RStudio

Looking at the created files

Using Packrat with a project

Writing the documentation for a package

Creating Rd documentation files

Looking at an example documentation file

Adding examples

dontrun

dontshow

Editing the DESCRIPTION file

General information

Dependencies

License

Understanding the namespaces of a package

Building and checking a package

Checking a package

Customizing the package build options

Using roxygen2 for package documentation

Installing the roxygen2 package

Generating Rd Files

Testing a package

Using testthat in a package

Adding a dataset to a package

Creating .rda files

Using LazyData with a package

Writing a package vignette with R markdown

Creating vignette files

References for further information

Summary

8. Collaborating with Git and GitHub

Introducing version control

Installing Git

Installing Git on Windows

Installing Git on Linux

Configuring Git

Explaining the basic terminology

Repository

Commit

Diff

Branch

Merge

Fetch

Pull

Push

Using Git via shell

Using the shell from Rstudio

Using Git with RStudio

Using RStudio and GitHub via SSH

Creating a new project with Git

Explaining the gitignore file

Keeping track of changes

Recording changes

Introducing the Git drop-down menu

Undoing a mistake

Pushing to a remote repository on github.com

Using an existing GitHub project with RStudio

Using branches

Making a pull request

Reviewing and merging pull requests

Further resources

Summary

9. R for your Organization – Managing the RStudio Server

Managing the RStudio Server

Using Amazon Web Services as the server platform

Creating an AWS account

Using S3 to store our data

Creating our bucket

Uploading a dataset to the bucket

Launching our EC2 instance

Choosing An amazon Machine Image

Choosing an instance type

Configuring instance details

Creating a new IAM role

Adding storage

Tagging an instance

Configuring a security group

Reviewing

Creating a key pair

Launching the instance

Connecting with the new EC2 instance

What is SSH?

Bringing it all together

Setting up R, RStudio, and the Shiny Server

Choosing your RStudio version

Installing base R

Installing RStudio and the Shiny Server

RStudio and the Shiny Server in your browser

Administrating your RStudio server environment

Getting rid of the R memory problem

Connecting our S3 bucket with RStudio

Basic RStudio server management

Managing the Shiny Server

Basic commands for the Shiny Server

Summary

10. Extending RStudio and Your Knowledge of R

Extending RStudio, finding answers, and more

RStudio environment customizations

Customizing the Rprofile

Where to find your Rprofile

Adding custom functions

The first and last functions

More ideas for your Rprofile

R help is on the way

Getting questions and answers

Stack Overflow (Stack Exchange)

Data Science (Stack Exchange)

Cross Validated (Stack Exchange)

Open Data (Stack Exchange)

R mailing lists – R-help

Reddit

How to ask questions correctly

Learning more about packages, functions, and more

R FAQs

R and CRAN documentations

R search engines

RStudio cheat sheets

Sharing your R code

Improving your R knowledge

Learning R interactively

Try R

DataCamp

Leada

Swirl

Attending online courses

Coursera

Johns Hopkins University – Data Science Specialization

Johns Hopkins University – Genomic Data Science

Udacity

Other MOOC courses, related platforms, and programs

Staying up to date in the R world

R-Bloggers

The R Journal

Summary

Index

Mastering RStudio – Develop, Communicate, and Collaborate with R

---

Mastering RStudio – Develop, Communicate, and Collaborate with R

Copyright © 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: November 2015

Production reference: 1251115

Published by Packt Publishing Ltd.

Livery Place

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Birmingham B3 2PB, UK.

ISBN 978-1-78398-254-7

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Credits

Authors

Julian Hillebrand

Maximilian H. Nierhoff

Reviewer

Nicholas A. Yager

Commissioning Editor

Kartikey Pandey

Acquisition Editor

Tushar Gupta

Content Development Editor

Anish Dhurat

Technical Editor

Mohita Vyas

Copy Editor

Angad Singh

Project Coordinator

Harshal Ved

Proofreader

Safis Editing

Indexer

Rekha Nair

Graphics

Abhinash Sahu

Production Coordinator

Melwyn Dsa

Cover Work

Melwyn Dsa

About the Authors

Julian Hillebrand studied international business marketing management at the Cologne Business School in Germany. His interest in the current questions of the business world showed him the importance of data-driven decision-making. Because of the growing size of available inputs, he soon realized the great potential of R for analyzing and visualizing data. This fascination made him start a blog project about using data science, especially for social media data analysis, which can be found at http://thinktostart.com/. He managed to combine his hands-on tutorials with his marketing and business knowledge.

Julian is always looking for new technological opportunities and is also interested in the emerging field of machine learning. He completed several digital learning offerings to take his data science capabilities to the next level.

Maximilian H. Nierhoff is an analyst for online marketing with more than half a decade of experience in managing online marketing channels and digital analytics. After studying economics, cultural activities, and creative industries, he started building online marketing departments and realized quickly that future marketing forces should also have programming knowledge. He has always been passionate about everything related to the topics of data, marketing, and customer journey analysis. Therefore, he has specialized in using R since then, which is his first-choice language for programming, data science, and analysis capabilities. He considers himself a lifelong learner and is an avid user of MOOCs, which are about R and digital analytics.

About the Reviewer

Nicholas A. Yager is a biostatistician and software developer researching statistical genomics, image analysis, and infectious disease epidemiology. With an education in biochemistry and biostatistics, his experience in analyzing cutting-edge genomics data and simulating complex biological systems has given him an in-depth understanding of scientific computing and data analysis. Currently, Nicholas works for a personalized medicine company, designing medical informatics systems for next-generation personalized cancer tests. Aside from this book, Nicholas has reviewed Unsupervised Learning with R, Packt Publishing.

I would like to thank my friends, Lauren and Matt, and my mentor, Dr. Gregg Hartvigsen, for their help in reviewing this book.

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Preface

Data analysis, visualization, and the handling of complex statistical issues was reserved just for universities and very few organizations for a long time. In fact, an easy-to-use and free environment to make the concept of data analysis available to a broader audience was not available.

But in the early nineties, R saw the light of day, and since then, it has been on a meteoric rise. R has shaped the landscape of data science in recent years like no other programing language. Because of its open source nature, it became widely known and is often referred to as the lingua franca of data analysis. Another reason for this huge success is the availability of a sophisticated Integrated Development Environment (IDE) named RStudio.

The development of RStudio started in 2010, and now, it is the de facto, go-to IDE for everybody working with R. The mission statement of RStudio is "to provide the most widely used open source and enterprise-ready professional software for the R statistical computing environment."

But RStudio offers more than just a handy way to create R scripts; it grew to a real ecosystem by providing a variety of functionalities like package, application, interactive reporting creation, and more. Walking this way, RStudio has managed to bring data analysis to a broader audience. And because of its continuous desire to innovate R and its possibilities, it can be seen as a further development of the R language. RStudio combines the strong statistical power of R, the community, and open source spirit with cutting edge technologies of user interface development.

This made RStudio more than just a tool for statisticians; it became the platform for everybody who wants to generate insights from data and share them with others.

Therefore, we will hereafter guide you to develop, communicate, and collaborate with R by mastering RStudio.

What this book covers

Chapter 1, The RStudio IDE – an Overview, describes how to install RStudio, and gives a general overview of its user interface.

Chapter 2, Communicating Your Work with R Markdown, shows how to create R Markdown documents and presentations with the help of the concept of reproducible research.

Chapter 3, R Lesson I – Graphics System, gives an introduction to the landscape of plotting packages in R and the basic process of plot creation with different packages for interactive graphs.

Chapter 4, Shiny – a Web-app Framework for R, describes how to create web applications with the Shiny framework by explaining the basic concept of reactive programming.

Chapter 5, Interactive Documents with R Markdown, explains how to create interactive R Markdown documents with the Shiny framework and other R packages.

Chapter 6, Creating Professional Dashboards with R and Shiny, introduces the concept of dashboards, and how to build a professional dashboard with the shinydashboard package.

Chapter 7, Package Development in RStudio, describes the basic process of package development in R, and how to create R packages with RStudio.

Chapter 8, Collaborating with Git and GitHub, shows the fundamentals of Git and GitHub, and how to use them with RStudio.

Chapter 9, R for your Organization – Managing the RStudio Server, describes how to install R, RStudio, and the Shiny Server on a cloud server to create a fully flexible programming environment.

Chapter 10, Extending RStudio and Your Knowledge of R, explains where you can find additional resources to improve your work with R and RStudio.

What you need for this book

To fully apply the knowledge learned in this book, you will need a computer with access to the Internet, and the ability to install the R environment as well as the RStudio IDE. The first chapter will guide you through this process.

Who this book is for

This book is aimed at R developers and analysts who wish to work on R statistical development while taking advantage of RStudio's functionality to ease their development efforts. Experience with R programming is assumed, as well as being comfortable with R's basic structures and a number of functions.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: You can also export the analysis.R script as a report in the HTML, PDF, or MS Word format, and you will then find the report in your code folder.

A block of code is set as follows:

gaToken <- GoogleApiCreds(

                userName = [e-mail],