Juretig - R statistics cookbook over 100 recipes for performing complex statistical operations with R 3.5

Copyright 2019 Packt Publishing

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First published: March 2019

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ISBN 978-1-78980-256-6

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Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.

Davor Lozic is a senior software engineer interested in a variety of subjects, in particular, computer security, algorithms, and data structures. He manages teams of more than 15 engineers and is a professor when it comes to teaching what there is to know about database systems. You can contact him at davor@warriorkitty.com. He likes cats! If you want to talk about any aspect of technology, or if you have funny pictures of cats, feel free to contact him.

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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.

If you are a quantitative researcher, statistician, data analyst, or data scientist looking to tackle common and not-so-common challenges in statistics, then this book is what you need! A solid understanding of R programming and a basic understanding of statistics and linear algebra.

, Getting Started with R and Statistics , reviews a variety of techniques in R for performing data processing, data analysis, and plotting. We will also explain how to work with some basic statistical techniques, such as sampling, maximum likelihood estimation, and random number generation. In addition, we will present some useful coding techniques, such as C++ functions using Rcpp, and R6Classes. The former will allow us to add high-performance compiled code, whereas the latter will allow us to perform object-oriented programming in R.

, Univariate and Multivariate Tests for Equality of Means , explains how to answer the most basic statistical question: do two (or possibly more) populations have the same mean? This arises when we want to evaluate whether certain treatment/policy is effective compared to a baseline effect. This can naturally be extended to multiple groups, and the technique used for this is called Analysis of Variance (ANOVA). ANOVA can itself be extended to accommodate multiple effects; for example, testing whether the background color of a website and the font style drive sales up. This is known as two-way ANOVA, and leads to additional complications: not only do we have multiple effects to estimate, but also we could have interaction effects happening between these two effects (for example, a certain background color could be effective when used in conjunction with a specific font type). ANOVA can also be extended in other dimensions, such as adding random effects (effects that originate from a large population and where we don't want to estimate a parameter for each one of them), or repeated measures for each observation.