Subject Guides: Data Science: Working with R

Download R

In order to work with this programming language, you must first download R, which you can do here. Downloading RStudio, the integrated development environment (IDE) for R, is recommended too.

R Libraries

R has a lot of pre-existing packages (also called libraries) written by other R users and available for installation.

All official R packages are available through CRAN (Comprehensive R Archive Network). There are a lot of R packages available; this list of recommended R packages is a good starting point.

Be sure to also check out the available quick reference cheatsheets for R programming functions and commands.

R Packages by Hadley Wickham Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. In the process, you'll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr
Call Number: Online
ISBN: 9781491910566
Publication Date: 2015-03-26

Introductory Materials

R for Reproducible Scientific Analysis (Software Carpentry)
This set of lessons from Software Carpentry is an introduction to R for people with no programming background. It introduces R, the RStudio interface, working with data structures, organizing/subsetting data, making plots, and creating reports. This is a great "get up to speed quickly" set of lessons that use the same data throughout.
Programming with R (Software Carpentry)
Also from Software Carpentry, this set of lessons is more focused on programming basics and best practices (functions, loops, conditionals, etc.).
Introduction to R
This is an official R manual. If you want to learn R technicalities top to bottom, this is the right place.
RStudio's online learning guide
RStudio provides an extensive set of links to learning resources. From getting started with R, to making interactive plots with Shiny, to R code best practices, this page is definitely worth a look.

R for Data Science 2nd Edition by Hadley Wickham; Garrett Grolemund
Call Number: Online
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Advanced R eBooks

The library has a lot of eBooks on R. The following titles are just a small selection. Please search our catalog to find many more options or use the AU Library Search bar below.

Advanced R, Second Edition by Hadley Wickham
Call Number: Online
ISBN: 9780815384571
Publication Date: 2019-05-30
Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimizing your code.
Advanced R Solutions by Malte Grosser; Henning Bumann; Hadley Wickham
Call Number: Online
ISBN: 9781000409093
Publication Date: 2021-08-23
This book offers solutions to all 284 exercises in Advanced R, Second Edition. All the solutions have been carefully documented and made to be as clear and accessible as possible. Working through the exercises and their solutions will give you a deeper understanding of a variety of programming challenges, many of which are relevant to everyday work. This will expand your set of tools on a technical and conceptual level. You will be able to transfer many of the specific programming schemes directly and will discover far more elegant solutions to everyday problems.
R Data Mining by Andrea Cirillo
Call Number: Online
ISBN: 1787124460
Publication Date: 2017-11-29
Mine valuable insights from your data using popular tools and techniques in R.
About This Book
* Understand the basics of data mining and why R is a perfect tool for it.
* Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it.
* Apply effective data mining models to perform regression and classification tasks.
If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required.
Text Mining with R by Julia Silge; David Robinson
Call Number: Online
ISBN: 1491981652
Publication Date: 2017-07-18
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing (NLP) into effective workflows.