Checking a reproducible data analysis using make, git, and R: Testing with new data! (CC075)

The goal for any data analysis in science is that it is reproducible. In this episode of Code Club, Pat highlights how building his data analysis workflow using tools for reproducibility like make, git, and R make it robust to the inclusion of new data.

This episode is part of a larger arc of episodes investigating the sensitivity and specificity of amplicon sequence variants (ASVs), also known as exact sequence variants (ESVs) and operational taxonomic units (OTUs). ASVs are growing in popularity for analyzing microbial communities using 16S rRNA gene sequences. Proponents think that they should supplant operational taxonomic units (OTUs). What do you think? Pat demonstrates these concepts by live coding at the command line interface using GitHub Flow, Make, and RStudio.

The accompanying blog post can be found at http://www.riffomonas.org/code_club/2...

You can also find complete tutorials for learning R with the tidyverse using...
Microbial ecology data: http://www.riffomonas.org/minimalR/
General data: http://www.riffomonas.org/generalR/

0:00 Introduction
2:26 Updating the rrnDB version and getting more resolution
10:03 Regenerating manuscript.pdf (and solving any problems)
14:43 Assessing the outcome
19:15 Comparing differences with git diff
24:35 Generating a track changes document
29:22 Updating README.md
31:05 Conclusions