RStudio for R Statistical Computing Cookbook

Key Features

• 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 10-15 real-life, practical improvements for each user type

Book Description

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.

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 domain-based 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

About the Author

Andrea Cirillo is currently working as an internal auditor at Intesa Sanpaolo banking group. He gained a lot of financial and external audit experience at Deloitte Touche Tohmatsu and internal audit experience at FNM, a listed Italian company.

His current main responsibilities involve evaluation of credit risk management models and their enhancement mainly within the field of the Basel III capital agreement.

He is married to Francesca and is the father of Tommaso, Gianna, and Zaccaria.

Andrea has written and contributed to a few useful R packages and regularly shares insightful advice and tutorials about R programming.

His research and work mainly focuses on the use of R in the fields of risk management and fraud detection, mainly through modeling custom algorithms and developing interactive applications.

Table of Contents

1. Acquiring Data for Your Project
2. Preparing for Analysis – Data Cleansing and Manipulation
3. Basic Visualization Techniques
4. Advanced and Interactive Visualization
5. Power Programming with R
6. Domain-specific Applications
7. Developing Static Reports
8. Dynamic Reporting and Web Application Development