14 January 2022
We’re off to a great start (book wise!) for 2022. Here’s 10 new additions to Big Book of R. Quite a few more paid versions of books in this round and they look good.
Before we begin – there’s two related items tha you may be interested in.
First is an interview I did with Isabella Velásquez of RStudio for their R Views blog. It covers the history of Big Book of R and what the future holds, so if you’re curious, take a look at Interview with Oscar Baruffa, Creator of the Big Book of R.
Second is that if you’re on Twitter, follow the official Big Book of R twitter account. Every few hours it tweets out an random entry from the Big Book of R. It’s a great way to discover books in the collection :).
This cookbook aims to provide a number of recipes showing how to perform common tasks using arrow.
by Paul Roback, Julie Legler
This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling.
Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from mega datasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public-domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of non-detections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it an invaluable resource for graduate students and researchers facing complex data analysis task.
A Survivor’s Guide to R provides a gentle yet thorough introduction to R. The book is structured around critical R tasks, and focuses on applied knowledge, rather than abstract concepts. The book’s easy-to-read approach helps students with little or no background in statistics or programming to develop real-world R skills through straightforward coverage of R objects and functions. Focusing on real-world data, the challenges of dataset construction, and the use of R’s powerful graphing tools, the guide is written in an accessible and sympathetic style that ensures students acquire functional R skills they can use in their own projects and carry into their work beyond the classroom. A Survivor’s Guide to R focusses on the challenges of learning R, rather than learning statistics. This makes it an effective complement for those who are using other statistics texts, or who already have a statistics background.
This book focuses on a basic theoretical framework dealing with the problems, solutions, and applications of text mining and its various facets in a very practical form of case studies, use cases, and stories. From understanding different types and forms of data to case studies showing the application of each text mining approach on data retrieved from various resources, this book is a must-read for all library professionals interested in text mining and its application in libraries. Additionally, this book will also be helpful to archivists, digital curators, or any other humanities and social science professionals who want to understand the basic theory behind text data, text mining, and various tools and techniques available to solve and visualize their research problems. Authors’ book website: https://textmining-infopros.github.io/
by Burak AYDIN, James ALGINA, Walter LEITE, Hakan ATILGAN
We aim to create a platform for the applied social scientists in which we can demonstrate basic statistical procedures using R and real data. We prefer to name this material as a platform given that (a) it is open for contribution, (b) it will have dynamic content and (c) it can serve as a mainboard for Plug-ins and Add-ons .
by Paul C. Bauer, Camille Landesvatter, many others
The present online book provide a review of APIs that may be useful for social scientists. Covers a wide selection of APIs from google, Instagram, Youtube and others. R code included.
This book will help you master R plots the easy way. We have spent a long time creating R plots with different tools (base, lattice and ggplot2) during different academic and working positions. If you want to create highly customised plots in R, including replicating the styles of XKCD, The Economist or FiveThirtyEight, this is your book.
Paid: Pay what you want, minimum $5
by Robert I. Kabacoff
Teaches you to use the R language, including the popular tidyverse packages, through hands-on examples relevant to scientific, technical, and business developers. Focusing on practical solutions to real-world data challenges, R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualisations. In this revised and expanded third edition, new coverage has been added for R’s state-of-the-art graphing capabilities with the ggplot2 package.
by Howard Baek
Mastering Shiny Solutions 2021, by Maya Gans and Marly Gotti, was released in early 2021. Since then, there have been various changes to the exercises in Mastering Shiny, and this book serves as an updated version. A few solutions in this book defer to those provided in Mastering Shiny Solutions 2021. Also, some exercises don’t contain solutions, and for these exercises, the author writes, “Not sure.”