We’ve done it folks!
Over 300 free R programming books are now available at www.BigBookofR.com.
Of the 343 entries available, 20 are paid products and the rest are all 100% free. Thanks to all the authors, contributors, users readers and cheerleaders who are helping build a rich ecosystem of material.
This latest edition of new entries is a whopper. 35 new entries in one go. To do them justice, I’m highlighting them in 3 separate posts over the next few days (that’s the plan anyway!).
If you’re on twitter, you might want to follow the Big Book of R twitter account which posts a random entry from the collection every couple of hours.
A big thank you to Ondrej Pekacek, Lluís Revilla, Daniel Sánchez, Soumya Ray for their contributions. I must also give a special thanks to a mysterious stranger identified only as “Gary”. In one fell swoop Gary submitted 26 books – the second highest contributor of all time :).
Without further ado, here’s the first 10 of the 35 entries recently added. Enjoy!
Complex Surveys: A Guide to Analysis Using R
by Thomas Lumley
Complex Surveys is a practical guide to the analysis of survey data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields.
An introduction to psychometric theory with applications in R
by William Revelle
My course in psychometric theory, on which much of this book is based, was inspired by a course of the same name by Warren Norman. The organizational structure of this text owes a great deal to the structure of Warren’s course. Warren introduced me, as well as a generation of graduate students at the University of Michigan, to the role of theory and measurement in the study of psychology.
An Introduction to Bayesian Reasoning and Methods
by Kevin Ross
Focus on statistical inference, the process of using data analysis to draw conclusions about a population or process beyond the existing data. “Traditional” hypothesis tests and confidence intervals that you are familiar with are components of “frequestist” statistics. This book will introduce aspects of “Bayesian” statistics. We will focus on analyzing data, developing models, drawing conclusions, and communicating results from a Bayesian perspective. We will also discuss some similarities and differences between frequentist and Bayesian approaches, and some advantages and disadvantages of each approach.
R bookdownplus Textbook
by Peng Zhao
‘bookdownplus’ is an extension of ‘bookdown’. It is a collection of multiple templates, which I have been collecting since years ago on the basis of LaTeX, and have been tailoring them so that I can work happily under the umbrella of ‘bookdown’. ‘bookdownplus’ helps you (and me) write varied types of books and documents. This book you are reading at the moment was exactly produced by ‘bookdownplus’.
Surrogates – Gaussian process modeling, design and optimization for the applied sciences
by Robert B. Gramacy
Surrogates is a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through simulation, “human out-of-the-loop” statistical support, management of dynamic processes, online and real-time analysis, automation, and practical application are at the forefront.
The R Series by CRC Press
This book series reflects the recent rapid growth in the development and application of R, the programming language and software environment for statistical computing and graphics.
Introduction to Computational Finance and Financial Econometrics with R
by Eric Zivot
This book is based on my University of Washington sponsored Coursera course Introduction to Computational Finance and Financial Econometrics that has been running every quarter on Coursera since 2013. This Coursera course is based on the Summer 2013 offering of my University of Washington advanced undergraduate economics course of the same name. At the time, my UW course was part of a three course summer certificate in Fundamentals of Quantitative Finance offered by the Professional Masters Program in Computational Finance & Risk Management that was video-recorded and available for online students. An edited version of this course became the Coursera course. The popularity of the course encouraged me to convert the class notes for the course into a short book.
Data Wrangling Essentials
by Mark Banghart
The R and Python communities have developed a set of tools in the tidyverse and the pandas packages respectively designed to wrangle table data. The intuitive nature of these packages makes learning to use them easy and the code easy to read and understand. These tools allow researchers to quickly and accurately complete data preparation for a wide variety of analysis. It is the application of these packages and their approaches to wrangling that are the subject of this book.
The Data Wrangling Essentials title was chosen to emphasize both the use of these new tools and the importance of the work of gathering and preparing data.
Data Integration, Manipulation and Visualization of Phylogenetic Trees
by Guangchuang Yu
A guide for data integration, manipulation and visualization of phylogenetic trees using a suite of R packages, tidytree, treeio, ggtree and ggtreeExtra.
The Saga of PLS
by Gaston Sanchez
The main motivating trigger behind this book has been my long standing obsession to understand the historical development of Partial Least Squares methods in order to find the who’s, why’s, what’s, when’s, and how’s. It is the result of an intermittent 10 year quest, tracking bits and pieces of information in order to assemble the story of such methods. Moreover, this text is my third iteration on the subject, following two of my previous works.
The second post is now available:
The third post is here:
If you’re looking for more awesome R content, check out RScreencasts.com
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