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

Introduction


A legendary tale says that Michelangelo found his Moses so real; and shouted at him "Why don’t you speak?!" Seeing that it wouldn’t, he slammed it down with his hammer.

Michelangelo's Mosè. Picture by Jörg Bittner Unna, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=46476418

From a similar desire of interaction, interactive visualization was born, making your data visualizations interact with you and your analysis users.

Using interactive visualizations, we can make our plots do something more than just displaying data, since we give them the ability to interact with the user, showing tooltips, navigation, and zooming controls, and even rearranging them according to users' needs and preferences.

Interactive data visualization can be useful at the beginning and at the end of your work.

At the beginning, in your exploratory data analysis job, you can leverage interactive visualizations in order to get a better understanding of your data without needing towrite a lot of...