Data7 Reproducible Research with GitHub and RStudio Book

Author

Greg Chism

UArizona Data7 Reproducible Research with GitHub and RStudio Book!

Here you will find a collection of materials prepared by the staff of the Data Science Institute

Table of contents:

Introduction

Introduction to the book and what you can expect to take away

Create a Research Compendium

Create a template Research Compendium from rrtools

Manage functionality as a package

Make your Compendium an R Package to ensure reproducibility

Reproduce a paper with Distill

Produce a Distill R Markdown version of a paper

Knowledge Level

Intermediate

Prerequisites:

Familiarity with Version Control through RStudio and R Markdown.

System Requirements:

Pandoc (>= 1.17.2)

LaTeX

If you don’t have LaTeX installed, consider installing TinyTeX, a custom LaTeX distribution based on TeX Live that is small in size but functions well in most cases, especially for R users.

install.packages('tinytex')
tinytex::install_tinytex()

Check docs before before installing.

devtools requirements

You might also need a set of development tools to install and run devtools. On Windows, download and install Rtools, and devtools takes care of the rest. On Mac, install the Xcode command line tools. On Linux, install the R development package, usually called r-devel or r-base-dev.

Disclaimer

This book is derived from materials authored by Anna Krystalli, and (Marwick 2019). The abridged materials here have been updated to current best practices. Additionally, the gillespie.csv dataset was replaced with the open source diabetes.csv Pima County Native American Diabetes dataset.

The original materials are licensed under a Creative Commons Attribution 4.0 International License.

CC BY Created: 8/22/2022 (G. Chism); Last update: 11/17/2022 ————————————————————————

Original workshop based on:

  • Research compendium cboettig/noise-phenomena: Supplement to: “From noise to knowledge: how randomness generates novel phenomena and reveals information” by Carl Boettiger licensed under CC BY 4.0. DOI

  • Marwick, B., Boettiger, C. & L. Mullen (2017). Packaging data analytical work reproducibly using R (and friends). PeerJ Preprints 5:e3192v1 https://doi.org/10.7287/peerj.preprints.3192v1