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Reproducible Research in RStudio

author: Caleb Kibet date: 10 August, 2018 autosize: true

Introduction to Reproducibility

"Really reproducible research" in computational sciences means:

the data and code used to make a finding are available and they are sufficient for an independent researcher to recreate the finding

Reproducibility and replication

Reproducibility

Why Should Research Be Reproducible?

  1. It provides a standard to judge scientific claims
  2. Reproducibility enhances replicability.
  3. Helps avoid effort duplication & encourages cumulative knowledge development
  4. Higher research impact for the researcher
  5. Instils better work habits and teamwork

Future collaborator

![collaborator] (https://image.slidesharecdn.com/layton-repro-research-talk-2015-05-06-150507202403-lva1-app6892/95/reproducible-research-first-steps-6-638.jpg?cb=1431031632)

Tips for Reproducible Research

  1. Document everything!
  2. Everything is a (text) file.
  3. All files should be human readable.
  4. Explicitly tie your files together.
  5. Have a plan to organize, store, and make your files available.
  6. Report your research transparently

Keep track of things

  1. Use version Control
  2. Use proper documentation: README
  3. Use Literate programming: RMarkdown, Jupyter Notebooks

Share and license your work

  • Data: Adhere to FAIR data principles
  • Software: Github and other repos

Project Folders

  • Choose a file structure that works for you
  • Use relative paths when possible and organize your files
    • Makes paths less dependent on particular File or System structure.
  • Avoid putting spaces in your file and directory names
  • Include a README.md that describes the purpose and structure of your project

Before you start

  • Create project within a folder in your computer
  • Create folder for your code
  • Create folder for Data
    • Raw: Downloaded or gathered from the field
    • Derived: processed through your analysis

Continued

  • Create a folder for figures generated from your analysis
  • NB: Ensure separation of information

Literate Programming

Literate programming is a crucial part of a reproducible quantitative research. Being able to directly link your analyses, your results, and the code you used to produce the results makes tracing your steps much easier.

Quick Demo

A demo on:

  • Quick tour of RStudio
  • Creating a project
  • setting working directory
  • Creating folders

Version control in RStudio

  • RStudio has inbuilt version control support
  • Learn more here

RMarkdown

R Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents.

  • Uses Knitr to execute the embedded code and pandoc to convert to output
  • The flow is:

![RMarkdown] (https://d33wubrfki0l68.cloudfront.net/61d189fd9cdf955058415d3e1b28dd60e1bd7c9b/9791d/images/rmarkdownflow.png)

RMarkdown Output

![Output formats] (https://d33wubrfki0l68.cloudfront.net/00ed9c32053cbc805efa51b66be570558480a4c8/7a292/images/rmarkdownoutputformats.png)

Let's hop into RStudio's tutorial to learn more

RMarkdown Tutorial

RMarkdown CheatSheet

RNotebooks

An R Notebook is an R Markdown document with chunks that can be executed independently and interactively, with output visible immediately beneath the input

RShiny

Shiny is an R package that makes it easy to build interactive web applications (apps) straight from R.

Learn more at you own time

Reference

  1. Reproducible Research with R and RStudio Second Edition is a great reference text.
  2. https://rmarkdown.rstudio.com/lesson-11.html
  3. https://rmarkdown.rstudio.com/articles_intro.html
  4. Organizing Projects: http://kbroman.org/Tools4RR/assets/lectures/06_org_eda.pdf
  5. Research reproducibility: http://stm.sciencemag.org/content/8/341/341ps12.full