6 new books added to Big Book of R


Many thanks to Sergey Bolshakov and Mokandil for some of this update’s submissions!

I want to again give a special thanks to Niels Ohlsen for helping me vet books and adding them to the collection. Niels is the co-organiser of the Dataviz meetup in Bremen, Germany. If you’re in the area, why not look them up on LinkedIn and Meetup?

I’m applying for a grant to upgrade the Big Book of R. Have a look at the details and if you like, take two minutes to submit your statement of support!

Introduction to urban accessibility

by Rafael H. M. Pereira, Daniel Herszenhut

The aim of this book is to equip its readers with the fundamental concepts, the data analysis skills and the processing tools needed to perform urban accessibility analyses and transportation projects impact assessments. The book was written with the problems faced by public managers, policy makers, students and researchers working on urban and transportation planning in mind. Hence, the book is essentially practical. All the material in the book is presented with reproducible examples using open data sets and the R programming language.


Introduction to Data Science

by Hansjörg Neth

This book provides a gentle introduction to data science for students of any discipline with little or no background in data analysis or computer programming. Based on notions of representation and modeling, we examine some key data types and data structures, and then learn to clean, transform, summarize and visualize data to communicate our results.


R for Data Analytics

by Abhay Singh

This is compilation of notes for R for Data Analytics. These notes are used as learning material in R for Research, R for Financial Analytics and R for Data Analytics workshops.


A Little Book of R for Bioinformatics 2.0

by Avril Coghlan, Nathan L. Brouwer

This book is based on the original A Little Book of R for Bioinformatics by Dr. Avril Coghlan (Hereafter “ALBRB 1.0”). Dr. Coghlan’s book was one of the first and most thorough introductions to using R for bioinformatics and computational biology.


R for data analysis

by Trevor French

The content will start at the very beginning by showing you how to set up your R environment and the basics of programming in R. By the end of the book, you will be able to perform intermediate analytics techniques such as linear regresion and automatic report generation.


A Crash Course in Geographic Information Systems (GIS) using R

by Michael Branion-Calles

Introduction into concepts for GIS and spatial data in R. Later chapters are not finished.


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