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

Making a recommendation engine


Recommendation engines are a powerful way to boost sales on e-commerce websites, since they allow us to suggest to customers products that are likely to meet their preferences.

These suggestions are produced by looking at previous purchases (or wish lists and visited products) and comparing them with other customers and their purchases.

Basically, recommendation engines state that if you bought those products, you are similar to these other customers who also bought these products. So, probably, you will like these products as well.

Getting ready

In this recipe, we will compute the cosine measure of a matrix in order to measure similarity of vectors composing the matrix.

The lsa package provides a specific cosine() function for this purpose. In order to use it, we first have to install and load the package:

install.packages("lsa")
library(lsa)

Our recommendation engine will be applied to a data frame that stores movie reviews from five critics, ranging from 1 to...