Using R and GitHub

I have now, after what seems like a lifetime, found a nice conceptial way of working with the scientific workflow. The software and tools I use are open-source and reproducible. This comes at some costs, the main one that comes to mind is a general aspect of humanity that we can never actually get away from….if your spelling isn’t right or you math doesn’t match the current proof then its you not us….

This works for 99% of projects. The real challenge is knowning when 1% of the projects are coming up to support. Use these tools and you will work out how.

[Manual coming soon….]

Overview

Tools, software, hardware, its a lot to take in but dont try and separate them anymore (cite cloud status of the world).

• learning new tools (how to learn how to do things in R)

Generalised options

Computational Tools

These are the extensions or almost shorthand names for the applications and uses we are applying using the software. In an opensource enviroment there are alot of tools but only a few decent Software frameworks to achieve these tasks in the optonial manner (for the computer or the human as it turns out).

• GitHub (for tracking and documenting your work)
• RMarkdown (to easily build webpages and pdfs with or without R code)
• Beamer (pdf slides that blow away Powerpoint presentations)
• Regular Expressions (find-and-replace on steroids)
• LaTeX (for type-setting equations and improving your slides)

Software

I just think of these as the actual programs or files you have to add to your local/personal operating system to make the code or program you want to work (before packages for specific projects are added)

• Git
• LaTeX

Hardware

Windows and mac so far. The linux learning curve is coming for me soon I think

Resources

Learning new tools (how to learn how to do things in R)

Tips and tricks

These are the bits that help me once I got my head around the underlying concepts:

Local vs remote

## Local vs remote

Other external Resources

This blog was build off many other researchers work but these are the ones that have come out of the woodwork as great resources for my applications:

UoC.Git.2018-11-27

• info
• Github
• Rstudio
• Reports
• Websites

Using R and GitHub: reproducibility in research

Nov 27, 2018

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General Information

Statistical software that are also programming languages, such as R, are excellent tools for conducting analyses of biological data. However, many users are not taking full advantage of their capabilities. This workshop is an introduction to some of the resources that are available to R users that have been developed and implemented in the larger programming community. No prior knowledge of R will be necessary, but this workshop will not be an introduction to R basics. Instead, we will focus on using R Studio and Github to easily sync your data and analyses online. Within this workshop we will explore how to maximize reproducibility, collaborate internationally on statistical analyses, present data summaries, data management, and the promotion of open science

Who: The course is aimed at R beginners and novice to intermediate analysts. You do not need to have previous knowledge of R.

Where: University of Cincinnati: 2600 Clifton Ave, Cincinnati. 713 Rieveschl hall Map

Requirements: Participants should bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) with administrative privileges. If you want to work along during tutorial, you must have both Git & R studio installed on your own computer (See below for instructions). However, you are still welcome to attend because all examples will be presented via a projector in the classroom.

Contact: Please contact alex.filazzola@outlook.com for more information.

Schedule

LiveNotepad

Software

Please install Git before installing R Studio. This allows seamless integration between the two programs because R Studio looks for Git on your computer, but Git does not look for R Studio. In the past, installation in the opposite order has been known to create issues. If you already installed R Studio and Git, but do not see the Git Tab in R Studio then you can follow this support page to troubleshoot.

Git

Git is a version control system that lets you track who made changes to what when and has options for easily updating a shared or public version of your code on github.com. You will need a supported web browser (current versions of Chrome, Firefox or Safari, or Internet Explorer version 9 or above).

You will need an account at github.com for parts of the Git lesson. Basic GitHub accounts are free and premium accounts are free to students. We encourage you to create a GitHub account if you don’t have one already. Please consider what personal information you’d like to reveal. For example, you may want to review these instructions for keeping your email address private provided at GitHub.

Information on how to install Git for each OS is provided by Software Carpentry and can be found here

R

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio.

Other workshops

If you enjoyed this workshop and were interested in learning more, I also run a workshop on R-basics and Introduction to Generalized Linear Modelling (GLM) found here. I also have a short introduction on using Functions in R.

You can find similar style workshops, usually that are longer and go into more detail, with Software Carpentry. They have teachers available globally and cover all forms of programming beyond R.