Book Image

R Statistics Cookbook

By : Francisco Juretig
2 (2)
Book Image

R Statistics Cookbook

2 (2)
By: Francisco Juretig

Overview of this book

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.
Table of Contents (12 chapters)

Anomaly detection

In recent years, a plethora of data collection systems have been set up in almost every company. This means that companies are not only collecting clicks/views/logins, but they are also gathering IT data such as server load/IoT sensors. All this massive influx of data has created a need to identify anomalies (outliers) in real time.

There are several ways of doing this in R, such as the methods provided in the tsoutliers package, but the currently-preferred method in the R community is to use the anomalize package due to its simplicity. This package offers spectacular and simple plots, separating out seasonality, outliers, trends, and residuals/remainders.

Getting ready

The anomalize package needs to...