Book Image

RStudio for R Statistical Computing Cookbook

By : Andrea Cirillo
Book Image

RStudio for R Statistical Computing Cookbook

By: Andrea Cirillo

Overview of this book

The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time 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 hands-on 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.
Table of Contents (15 chapters)
RStudio for R Statistical Computing Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Constructing RStudio add-ins


RStudio add-ins are one of the newest and most promising developments introduced recently by the RStudio team. They add infinite possibilities for improvement to users' workflows through the enhancement of their IDE.

There are two main types of add-ins:

  • Text macros: These only produce a text insertion within your code (for instance the (){} structure to be added after the function token).

  • Shiny gadgets: These are little Shiny apps that are shown within the viewer pane, a pop-up window, or a browser window. They let you perform advanced activities such as statistical parameter definition or data-wrangling tasks.

In this example, we will develop the second type of add-in from the function definition, for deployment and installation as a package on GitHub.

Our example will be a funny one: we will develop an add-in that lets you see weather forecasts for a specified city within the R console.

Getting ready

Let's first install the shiny and miniUI packages:

install.packages...