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

Measuring customer retention using cohort analysis in R


Within the e-commerce field, customer retention metrics can be considered crucial for several reasons. Among these, the virtual absence of a barrier to entry for competitors in the virtual arena makes online sellers very willing to build an enduring relationship with their customers.

This recipe gives you a straightforward way to compute retention metrics within the R environment.

From the possible methods available for these tasks, we will use one from the family of cohort methods.

In this method, customers are divided into homogenous groups (that is, cohorts) that share relevant segmentation attributes, such as sex or age.

Purchases made by those groups are monitored monthly over a period of time, and a retention rate is calculated each month using the following formula:

retention rate = (number of customers purchasing in a given month)/(number of customers within the cohort at the starting point)

Getting ready

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