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.
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.
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.
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.
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 = "[email protected]",
list(
client_id = "your client ID",
client_secret = "your client secret")
)
save(gaToken, file = "auth/gaToken")Any command-line input or output is written as follows:
$ sudo apt-key adv –keyserver keyserver.ubuntu.com –recv-keys E084DAB9
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Therefore, just click on the Publish button and RStudio will guide you through the process."
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