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

Chapter 6. Domain-specific Applications

In this chapter, we will cover the following topics:

  • Dealing with regular expressions

  • Analyzing PDF reports in a folder with the tm package

  • Creating wordclouds with the wordcloud package

  • Performing a Twitter sentiment analysis

  • Detecting fraud in e-commerce orders with Benford's law

  • Measuring customer retention using cohort analysis in R

  • Making a recommendation engine

  • Performing time series decomposition using the stl() function

  • Exploring time series forecasting with forecast()

  • Tracking stock movements using the quantmode package

  • Optimizing portfolio composition and maximizing returns with the Portfolio Analytics package

  • Forecasting the stock market