03 March 2023

Welcome to this new edition of Big Book of R additions! Thanks to Lluis Revilla and Gary for submitting books!

I also wanted to give a special shout-out to Niels Ohlsen (a long-time RStats twitter mutual!) who helped me review book submissions and add these to the collection.

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Population Health Data Science with R

Tomás J. Aragón

This book is divided into two parts. First, I cover how to process, manipulate, and operate on data in R. Second, I cover basic PHDS from an epidemiologic perspective. Data science is “the art and science of transforming data into actionable knowledge.” Here is where we can build on the strengths of epidemiology (descriptive and analytic studies). However, in public health practice we need much more than this.

https://www.bigbookofr.com/life-sciences.html#population-health-data-science-with-r

Reproducible Analytical Pipelines – Masters of Data Science

Bruno Rodrigues

The basic idea of a reproducible analytical pipeline (RAP) is to have code that always produces the same result when run, whatever this result might be. This is obviously crucial in research and science, but this is also the case in businesses that deal with data science/data-driven decision making etc.

A well documented RAP avoids a lot of headache and is usually re-usable for other projects as well.

https://www.bigbookofr.com/workflow.html#reproducible-analytical-pipelines—masters-of-data-science

Applied Statistics with R

David Dalpiaz

The book gives a basic introduction how to perform regression analysis in R. It is used in the context of an applied statistics class of University of Illinois Urbana-Champaign.

https://www.bigbookofr.com/statistics.html#applied-statistics-with-r

Comparative Methods

Brian O’Meara

A book for teaching people how to do comparative methods in R.  Written for a biology class to analyse evolutionary trees and finding patterns of divergence and common ancestry among species.

https://www.bigbookofr.com/life-sciences.html#comparative-methods

A Course in Exploratory Data Analysis

Jim Albert

This book contains the lecture notes for a course on Exploratory Data Analysis that Jim Albert taught for many years at Bowling Green State University. The book is based on John Tukey’s EDA book and illustrating with R.

It comes with a R package “”LearnEDAfunction”” that contains all of the course datasets and functions for performing some of the EDA methods and is available on author’s Github site.

https://www.bigbookofr.com/data-science.html#a-course-in-exploratory-data-analysis

Probability and Bayesian Modeling

Jim Albert

This book introduces Bayesian statistics in the undergraduate statistics curriculum. The book comes with a R Package “ProbBayes” and repos.

https://www.bigbookofr.com/statistics.html#probability-and-bayesian-modeling

R Without Statistics

David Keyes

R Without Statistics will show ways that R can be used beyond complex statistical analysis. Readers will learn about a range of uses for R, many of which they have likely never even considered.

https://www.bigbookofr.com/workflow.html#r-without-statistics

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