This lesson is still being designed and assembled (Pre-Alpha version)

Robust Open Analysis in R

This lesson aims to enable you to do your research analysis reproducibly, at every stage of the process: as you plan/prepare, analyse, and draft/write.

This will:

During the lesson, we introduce you to an example reproducible workflow using Git, GitHub, RStudio, and the Open Science Framework. The flowchart below is the workflow that you will complete during the course:

We don’t aim to teach you any techniques for doing analysis (i.e. there is no statistics in this course). Instead, we aim to teach you a way to organise your work so that you can do your work with relative ease and efficiency, reducing errors and stress.

By the end of this course you will:

Prerequisites

This lesson assumes some basic familiarity with R. See the prerequisites section of the setup page for some recommended tutorials.

Schedule

Setup Download files required for the lesson
00:00 1. Core concepts in reproducibility What is the reproducibility crisis?
How can working Openly and reproducibly help me?
What are the key reasons for irreproducible research?
How can we solve these key problems in our research?
00:30 2. The RStudio integrated development environment What is an Integrated Development Environment (IDE) and how can it make data analysis easier?
01:15 3. Literate programming with RMarkdown What is literate programming and how can it be used for make reproducible analysis?
02:00 4. Version Control in RStudio Why use version control?
What is the difference between Git and GitHub?
How can I use Git from inside RStudio?
How can I link my local Git repository to GitHub
04:00 5. Putting it all together How do RMarkdown, Git, and Open Science Framework work together to produce a reproducible data analysis?
05:35 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.