index.Rmd

---
title: "Home"
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---

My aim for the workshop is to introduce computational tools and demonstrate how they can be used to help promote reproducibility when performing bioinformatic analyses. Many of these tools, especially `workflowr` help adhere to these [Ten Simple Rules for Reproducible Computational Research](https://doi.org/10.1371/journal.pcbi.1003285):

* Rule 1: For Every Result, Keep Track of How It Was Produced
* Rule 2: Avoid Manual Data Manipulation Steps
* Rule 3: Archive the Exact Version Versions of All External Programs Used
* Rule 4: Version Control All Custom Scripts
* Rule 5: Record All Intermediate Results, When Possible in Standardised Formats
* Rule 6: For Analyses That Include Randomness, Note Underlying Random Seeds
* Rule 7: Always Store Raw Data behind Plots
* Rule 8: Generate Hierarchical Analysis Output, Allowing Layers of Increasing Detail to Be Inspected
* Rule 9: Connect Textual Statements to Underlying Results
* Rule 10: Provide Public Access to Scripts, Runs, and Results

Links to workshop material.

* [Introduction to Docker](docker.html)
* [Introduction to Conda](conda.html)
* [Introduction to workflowr](https://jdblischak.github.io/workflowr/articles/wflow-09-workshop.html)