5 New books added to Big Book of R

30 September 2022

The Big Book of R has just had 5 new additions to the collection! Thanks to Gary and Adejumo for some of them!

PS for those who may be interested, I have posted my replies to some great questions about cleaning a messy dataset.


by David Gohel

This book deals with reporting from R with the packages {officer}, {officedown}, {flextable}, {rvg} and {mschart}. These packages have been developed to facilitate the production of Word documents and PowerPoint presentations from and with R. It was written specifically to offer a competitive solution to SAS ODS for tabular and graphical reporting.


Spatial Data Science with R

by RSpatial

This website provides materials to learn about spatial data analysis and modeling with R. R is a widely used programming language and software environment for data science. R has advanced capabilities for managing spatial data; and it provides unparalleled opportunities for analyzing such data.


Using R for Social Work Research

by Jerry Bean

Our goal for this document is to illustrate the importance of good data analysis practices and how R and companion packages support these practices. We think the R system has many benefits for social work research. R has become the flagship computing environment for many areas of science and has great appeal because it is free and open-access. In addition, free tools like RStudio and R Markdown promote a replication commitment and open science philosophy important to our work.


Public Speaking for Data and Tech Professionals

by Eva Murray

Most people dread public speaking and they’re missing out on the benefits it can have for their personal and professional life and their career.

Without the right tools and frameworks to improve your preparation, build your confidence and get you on stage to tell your story, public speaking is hard.

This book helps you fix that.


Introduction to Empirical Bayes: Examples from Baseball Statistics

by David Robinson

Learn to use empirical Bayesian methods for estimating binomial proportions, through a series of examples drawn from baseball statistics. These methods are effective in estimating click-through rates on ads, success rates of experiments, and other examples common in modern data science. You’ll learn both the theory and the practice behind empirical Bayesian methods, including computing credible intervals, performing Bayesian A/B testing, and fitting mixture models. Each example comes with R code that can be used to analyze your own data.


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