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

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


Benford's law is a popular empirical law that states that the first digits of a population of data will follow a specific logarithmic distribution.

This law was observed by Frank Benford around 1938 and since then has gained increasing popularity as a way to detect anomalous alterations in a population of data.

Basically, testing a population against Benford's law means verifying that the given population respects this law. If deviations are discovered, the law performs further analysis for items related to those deviations.

In this recipe, we will test a population of e-commerce orders against the law, focusing on items deviating from the expected distribution.

Getting ready

This recipe will use functions from the well-documented benford.analysis package by Carlos Cinelli.

We therefore need to install and load this package:

install.packages("benford.analysis")
library(benford.analysis)

In our example, we will use a data frame that stores...