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Reproducible-Research-RStudio.Rpres
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Reproducible Research in RStudio
========================================================
author: Caleb Kibet
date: `r format(Sys.time(), '%d %B, %Y')`
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
=================================
[](https://twitter.com/i/status/1027640401610661889)
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.
3. Explicitly tie your files together.
5. Have a plan to organize, store, and make your files available.
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](https://aberdeenstudygroup.github.io/studyGroup/lessons/SG-T1-GitHubVersionControl/VersionControl/)
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](https://rmarkdown.rstudio.com/authoring_quick_tour.html)
[RMarkdown CheatSheet](http://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf)
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
- [RNotebook Tutorial](https://rmarkdown.rstudio.com/r_notebooks)
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](https://shiny.rstudio.com/tutorial/written-tutorial/lesson1/)
Reference
===================
1. [Reproducible Research with R and RStudio Second Edition](https://englianhu.files.wordpress.com/2016/01/reproducible-research-with-r-and-studio-2nd-edition.pdf) 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