6 New books added to Big Book of R

I’m very happy to announce the addition of 6 new books to the Big Book of R collection, which now stands at about 420 books in total!

Thanks to Isabella Velásquez, Emil Hvitfeldt and Metehan GÜNGÖR for their submissions :).

If you’d like to help out a little to cover my annual costs for keeping Big Book of R going, you’re most welcome. I’m currently at 70% of the annual goal. Thanks to everyone who has donated, large and small.

Donate a few dollars on Kofi

Practical Statistics in Medicine with R

by Konstantinos I. Bougioukas, PhD

The textbook can be used as support material for practical labs on basic statistics in medicine using R. It can also be used as a support for self-teaching for students and researchers in biomedical field. Additionally, it may be useful for (under)graduate students with a science background (engineering, mathematics) who wants to move towards biomedical sciences.

https://www.bigbookofr.com/life-sciences#practical-statistics-in-medicine-with-r

Feature Engineering A-Z

by Emil Hvitfeldt

This book is written to be used as a reference guide to nearly all feature engineering methods you will encounter. This book is designed to be used by people involved in the modeling of data. These can include but are not limited to data scientists, students, professors, data analysts and machine learning engineers. The reference style nature of the book makes it useful for beginners and seasoned professionals. A background in the basics of modeling, statistics and machine learning would be helpful. Feature engineering as a practice is tightly connected to the rest of the machine learning pipeline so knowledge of the other components is key.

Many educational resources skip over the finer details of feature engineering methods, which is where this book tries to fill the gap.

https://www.bigbookofr.com/machine-learning#feature-engineering-a-z

Flexible Imputation of Missing Data

by Stef van Buuren

Multiple imputation of missing data has become one of the great academic industries. Many analysts now employ multiple imputation on a regular basis as a generic solution to the omnipresent missing-data problem, and a substantial group of practitioners are doing the calculations in mice. This book aspires to combine a state-of-the-art overview of the field with a set of how-to instructions for practical data analysis.

https://www.bigbookofr.com/getting-cleaning-and-wrangling-data#flexible-imputation-of-missing-data

Data Wrangling and Visualization Guide

by Max Ricciardelli

These modules are here to present a succinct guide to using R, RStudio, and R Markdown for data wrangling and visualization. This guide is meant for those who have little to no experience in programming. My purpose in designing these modules is to provide a brief yet clear guide to learning the basic theory of these tools and how to apply them in practice.

https://www.bigbookofr.com/getting-cleaning-and-wrangling-data#data-wrangling-and-visualization-guide-1

Modern Statistical Methods for Psychology

by Mine Çetinkaya-Rundel, Jo Hardin

This book is intended to help psychology students build a foundation for statistical thinking and methods. This textbook consists of 3 main parts: (1) descriptive statistics, (2) foundations for inference, and (3) statistical inference. Each part contains multiple chapters. Each chapter ends with a review section which contains a chapter summary as well as a list of key terms introduced in the chapter.

https://www.bigbookofr.com/social-science#modern-statistical-methods-for-psychology

R Programming for Psychometrics

by Susu Zhang

A good test developer should not only be well-versed with measurement theory and psychometric methods. Nowadays, programming skills are also essential. So, the aim of this book is to introduce R to you and improve your data wrangling and functional programming skills.

https://www.bigbookofr.com/social-science#r-programming-for-psychometrics

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