8 New Books added to Big Book of R

29 May 2022

We’re closing in on the next milestone of 300 unique entries in Big Book of R!

Today’s update has a bunch of really interesting additions, and thanks to Staurt Lodge and Tim Hendriks for some of the submissions.

Before we dive into them, I want to let you know of a super short post I published recently giving some data cleaning tips for newbie analysts. I hope you find them helpful!

GT Cookbook Advanced

by Thomas Mock

This cookbook attempts to walk through many of the advanced applications for gt, and provide useful commentary around the use of the various gt functions. The full gt documentation has other more succinct examples and full function arguments.

https://www.bigbookofr.com/packages.html#gt-cookbook-advanced


reactablefmtr Cookbook

by Kyle Cuilla

A high-level overview of the functions and styling customization options available in {reactablefmtr}.

https://www.bigbookofr.com/packages.html#reactablefmtr-cookbook


Spatio-Temporal Statistics with R

by Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie

We live in a complex world, and clever people are continually coming up with new ways to observe and record increasingly large parts of it so we can comprehend it better (warts and all!). We are squarely in the midst of a “big data” era, and it seems that every day new methodologies and algorithms emerge that are designed to deal with the ever-increasing size of these data streams. It so happens that the “big data” available to us are often spatio-temporal data. That is, they can be indexed by spatial locations and time stamps. This book provides an accessible introduction, with hands-on applications of the methods through the use of R Labs at the end of each chapter.

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


sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series


by Gilberto Camara, Rolf Simoes, Felipe Souza, Alber Sanchez, Lorena Santos, et al

This book presents sits, an open-source R package for land use and land cover classification using big Earth observation data. Using time series derived from big Earth Observation data sets is one of the leading research trends in Land Use Science and Remote Sensing. One of the more promising uses of satellite time series is its application to classify land use and land cover. Information on land is critical for sustainable development because our growing demand for natural resources is causing significant environmental impacts. The target audience for sits is the new generation of specialists who understand the principles of remote sensing and can write scripts in R. Ideally, users should have basic knowledge of data science methods using R.


https://www.bigbookofr.com/geospatial.html#sits-data-analysis-and-machine-learning-on-earth-observation-data-cubes-with-satellite-image-time-series


Audit Analytics with R

by Jonathan Lin

This is the website for Audit Analytics in R. This audience of this book is for Audit leaders who are looking to design their environment to encourage cultivate collaboration and sustainability.
Audit data analytics practitioners, who are looking to leverage R in their data analytics tasks.
You will learn what tools and technologies are well suited for a modern audit analytics toolkit, as well as learn skills with R to perform data analytics tasks. Consider this book to be your roadmap of practical items to implement and follow.

https://www.bigbookofr.com/finance.html#audit-analytics-with-r-1


Writing R extensions

by R Core
This is a guide to extending R, describing the process of creating R add-on packages, writing R documentation, R’s system and foreign language interfaces, and the R API.

This manual is for R, version 3.4.2 (2017-09-28).

https://www.bigbookofr.com/r-programming.html#writing-r-extensions

Open Forensic Science in R

by Sam Tyner, Ph.D (editor)

This book is for anyone looking to do forensic science analysis in a data-driven and open way. Whether you are a student, teacher, or scientist, this book is for you. We take the latest research, primarily from the Center for Statistics and Applications in Forensic Evidence (CSAFE) and the National Institute of Standards and Technology (NIST) and show you how to solve forensic science problems in R.

https://www.bigbookofr.com/field-specific.html#open-forensic-science-in-r



Power Analysis with Superpower

by Aaron R. Caldwell, Daniël Lakens, Chelsea M. Parlett-Pelleriti, Guy Prochilo, Frederik Aust

This package, and book, expect readers to have some familiarity with R (2020). However, we have created two Shiny apps (for the ANOVA_power & ANOVA_exact functions respectively) to help use Superpower if you are not familiar with R. Reading through the examples in this book, and reproducing them in the Shiny apps, is probably the easiest way to get started with power analyses in Superpower.

The goal of Superpower is to easily simulate factorial designs and empirically calculate power using a simulation approach. The R package is intended to be utilized for prospective (a priori) power analysis. Calculating post hoc power is not a useful thing to do for single studies.

https://www.bigbookofr.com/statistics.html#power-analysis-with-superpower


Subscribe for updates. I write about R, data and careers.

Subscribers get a free copy of Project Management Fundamentals for Data Analysts worth $12

* indicates required

Back to Top