Microbial Informatics

• Gain familiarity with RStudio
• Describe how to generate a document in markdown and the R flavor of markdown
• Understand the basics of version control software using git

• "Markdown is a text-to-HTML conversion tool for web writers. Markdown allows you to write using an easy-to-read, easy-to-write plain text format, then convert it to structurally valid XHTML (or HTML)." - John Gruber
• The advantage is that you can read it as a text document and it will make sense and you can use conversion software to generate other file formats including html, pdf, docx
• Can be rendered using:
  • A Perl script from Gruber
  • RStudio

• "R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports from R. It combines the core syntax of markdown (an easy-to-write plain text format) with embedded R code chunks that are run so their output can be included in the final document. R Markdown documents are fully reproducible (they can be automatically regenerated whenever underlying R code or data changes)." - RStudio website
• Keys...
  • Ability to format text
  • Embed R code and output as chunks or inline

• Let me pick a random number between 1 and 10.
• Hmmm, I pick 2
• 2 squared is 4
• All of the numbers (after 1 and 10) were generated within R
• The dynamic component is that you use R packages to allow a user to set the min and max values to bound your pick

• The chunks and inline approaches are implemented using an R package called knitr
• We will talk about knitr later when we start digging into R's syntax
• Documentation and a book (written in knitr) can be found on Yi Hui's website

• An R markdown "cheat sheet" is available for you to download and print from rstudio.com
• Other documentation, examples, tutorials are available at rmarkdown.rstudio.com
  • You can use existing document templates
  • You can design your own report templates
  • Possible to insert paper references
  • There's a lot more there
• We will be using R markdown tomorrow as part of the exercise

• Example scenarios:
  • You are working by yourself on the data analysis for your thesis. Your advisor wants to see the bleeding edge code for what you are doing.
  • You are working with collaborators on the data analysis for a paper. Since each has a different piece of the story to take care of, everyone has their own workflows, versions of the raw data, etc.
  • You discover a bug in your favorite R package and have found a fix for it
• Each of these problems can be solved using VCS

"A tool for managing changes to a set of files. Each set of changes creates a new revision of the files; the version control system allows users to recover old revisions reliably, and helps manage conflicting changes made by different users." -- Software Carpentry Workshop

• It's virtually impossible to lose old versions of text
• Can be used like the "undo/redo" features in Word, but with branches
• Keeps a record of who made changes and when
• Difficult to ignore other people's contributions since software notifies of conflicts
• git is one type of VCS, but it's the most popular

• Muck around with the files in your working directory. Possibly add or delete files.
• You stage the files, adding snapshots of them to your staging area. (git add)
• You take the files as they are in the staging area and store that to your Git directory. (git commit)

• Install appropriate software that we discussed on Tuesday
• Sign-up for an account on GitHub
• Check out Software Carpentry Git tutorial
• Read first two chapters of ProGit
  • Getting Started
  • Git Basics