Chapter 4 Using R

4.1 Introduction

In addition to there being many resources available for using R to solve statistical and data science challenges, there are also many resources on how to maximize your effectiveness using R.

This chapter compiles what I consider to be the essential texts; articles and blog posts will be at a minimum.


4.2 The R community

The R Consortium " is a group organized under an open source governance and foundation model to support the worldwide community of users, maintainers and developers of R software. … The central mission of the R Consortium is to work with and provide support to the R Foundation and key organizations and groups developing, maintaining, distributing and using R software."

The R Foundation

is a not for profit organization working in the public interest. It has been founded by the members of the R Development Core Team in order to * Provide support for the R project and other innovations in statistical computing. We believe that R has become a mature and valuable tool and we would like to ensure its continued development and the development of future innovations in software for statistical and computational research. * Provide a reference point for individuals, instititutions or commercial enterprises that want to support or interact with the R development community. * Hold and administer the copyright of R software and documentation.

Martyn Plummer, The R Consortium and the R Foundation, The R Journal Vol. 7/2, December 2015

David Keyes, 2019-07-29, If You Care About Equity, Use R

Julia Stewart Lowndes, 2019-12-10, Open Software Means Kinder Science, Scientific American blog


4.3 General & all-encompassing resources

David Smale, Free R Reading Material, A Shiny app collection “of books about the R programming language and Data Science, that you can read for free!”

Jennifer Bryan and Jim Hester, What They Forgot to Teach You About R – “designed for experienced R and RStudio users who want to (re)design their R lifestyle. We focus on building holistic and project-oriented workflows that address the most common sources of friction in data analysis, outside of doing the statistical analysis itself.”

Colin Fay, 2018-09-24, Why do we use arrow as an assignment operator? – answering the second-most-asked question about R.

4.3.1 Journals

The R Journal—“the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.”

Journal of Statistical Software—not limited to R, the journal “publishes articles, book reviews, code snippets, and software reviews on the subject of statistical software and algorithms.”

4.3.2 Text books by subject

Jacob Kaplan (2020-01-15) Crime by the Numbers—“This book introduces the programming language R and is meant for undergrads or graduate students studying criminology. R is a programming language that is well-suited to the type of work frequently done in criminology - taking messy data and turning it into useful information. While R is a useful tool for many fields of study, this book focuses on the skills criminologists should know and uses crime data for the example data sets.”


4.4 R Release Names

The most-asked question about R?

Answer: “All of the release names are references to Peanuts strips/films.”

Lucy D’Agostino McGowan, 2017-07-28, R release names

Releases since 2017-07-28:

(Another source for the strips is the peanuts.fandom.com comics archive)


4.5 R Introductions

Nathaniel D. Phillips, YaRrr! The Pirate’s Guide to R


4.6 The R toolbox

4.6.1 CRAN

The Comprehensive R Archive Network – CRAN for short – “is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R.” This is the place to get your installation of R, and the production versions of your favourite packages.

4.6.2 RStudio – the IDE

“RStudio” can mean one of two things:

4.6.3 Packages

Antoine Bichat’s favoriteRpackages


4.7 R as a programming environment

Colin Gillespie and Robin Lovelace, Efficient R Programming (Gillespie and Lovelace 2017)

Hadley Wickham, Advanced R (Wickham 2015a)

Hadley Wickham, R Packages (Wickham 2015b)

4.7.1 Debugging R

Hadley Wickham, “Debugging”, Chapter 22 in Advanced R (Wickham 2015a)

Jonathan McPherson, 2019-05-20, Debugging with RStudio

Jennifer Bryan and Jim Hester, “Debugging R code”, Chapter 11 in What They Forgot to Teach You About R

Jenny Bryan (2020-01-30) Object of type ‘closure’ is not subsettable—talk at rstudio::conf 2020

4.7.2 R as part of the Data Science Toolbox

Jessica Minnier, 2019-07-29, Sharpening the Tools in Your Data Science Toolbox

4.7.3 The R-Python interface

4.7.3.1 {feather}

{feather} is designed to read and write feather files, a lightweight binary columnar data store designed for maximum speed. There is a parallel Python package.

4.7.3.2 {reticulate}

The reticulate package provides a comprehensive set of tools for interoperability between Python and R.

4.7.3.3 {rpy2}

JD Long, twitter thread on {rpy2} documentation: twitter thread

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References

Gillespie, Colin, and Robin Lovelace. 2017. Efficient R Programming: A Practical Guide to Smarter Programming. O’Reilly. https://csgillespie.github.io/efficientR/.

Wickham, Hadley. 2015a. Advanced R. CRC Press. https://adv-r.hadley.nz/.

Wickham, Hadley. 2015b. R Packages: Organize, Test, Document, and Share Your Code. O’Reilly. http://r-pkgs.had.co.nz/.