Prerequisites: Some programming experience recommended (beginner level, preferably but not limited to R or Python) for the Jupyter Notebooks, RMarkdown and Git/GitHub sessions.
What is reproducible research and how can I make my research and code reproducible? In this one-day workshop with JHU Data Services, learn how open tools like R Markdown, Jupyter Notebooks, Git and GitHub can help make your research reproducible. Join JHU Data Services for this full-day workshop about concepts and tools for doing reproducible research. This workshop will be split into four separate interactive sessions: “Introduction to Reproducible Research,” “Getting Started with Jupyter Notebooks,” “Getting Started with RMarkdown” and “Version Control: Using Git and GitHub.” Sessions are run independently of each other. You may attend all sessions or only those that fit your interests.
9:30-10:30am: Introduction to Reproducible Research (Required)
A brief introduction to reproducible research. What is reproducible research? Why is it important? This session provides several good and not so good examples of reproducible research. We also provide several tips and useful resources for you to start making your research more reproducible.
10:45-12:15pm: Getting Started with Jupyter Notebooks (Optional)
You are already writing comments for your Python code so you may be wondering why you need to document code with a Notebook tool, such as Jupyter Notebooks. Notebook tools help you document and share your work with fellow Python coders, non-coders or non-Python coders. This workshop will teach you the basics of using Jupyter Notebooks and will focus on the features of Jupyter Notebooks that help you create reproducible research and code for technical and non-technical audiences. Many Python users choose to document and share code with Jupyter Notebooks, but this tool can also be used with other programming languages, including Julia and R. You can find examples of Jupyter Notebooks here.
1:30-3:00pm: Getting Started with RMarkdown (Optional)
You are already writing comments for your R code so you may be wondering why you need to document code with a Notebook tool, such as RMarkdown. You can find several RMarkdown examples here. Notebook tools are particularly useful when you share your code and analysis with non-coders or non-R coders. RMarkdown can help you create interactive web document with text, code and results of executing that chunk of code all in one place. Many R users choose to document and share code with RMarkdown, but it can be used to document scripts writing in other programming languages, such as Python and SQL.
3:30-5:00pm: Version Control: Using Git and GitHub (Optional)
Do you use different file names to track versions of your files? Are you wondering why everyone else is using GitHub? Do you comment out a chunk of code to test out code or debug? Do you want a system that can do version control automatically for you? If you answer yes for any of the questions above, then you should take this session to learn version control with Git and use GitHub as a git-hosting platform. We use a graphic user interface (GUI) tool, GitHub Desktop, to get your started with using Git and GitHub. No knowledge of command lines is needed for this session.
Register once here for access to all four sessions: https://jhu.libcal.com/event/7499496