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10  What next?

What Next slides

Instructor note

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It is one thing to learn the principles of how to do reproducible research. It is quite another thing to do so in daily practice. So, how can you practice these skills and tools you learned during the course?

• If you have your own data already, then it’s easy: Start using these tools bit by bit, step by step. It doesn’t have to be all at once. Slowly and steadily use the tools and skills from this course and continue learning. It isn’t a race, use what you can without getting totally overwhelmed.
• Check out the Learning page of the Guides website for resources on continued learning.
• If your collaborators or supervisor don’t use these tools from the course, are not supportive, or are supportive but not able to learn and use these tools themselves, e.g. they are too busy, there are several steps you can take. This situation is definitely challenging and is likely to be most commonly encountered. Use the tools as best you can, small bits at a time, so that you continue learning but don’t get completely overwhelmed with all the new things and ways to do things. Below are some potential small steps to take that you can choose from to start incorporating and using R and reproducibility in your work:
  • As much as possible, setup your projects, folders, and files in a more reproducible way (e.g. through using the structure created from the {prodigenr} package).
  • Create all your figures entirely in R and using R scripts or R Markdown files.
  • Write everything research related in Quarto and convert to a Word document when you need to send to co-authors. If they make edits or comments, include the edits in the original Quarto file, and do not keep them in the Word document.
  • Start slowly making use of Git, even if you can’t or are not comfortable yet with sharing on GitHub. Git and GitHub are two separate things and Git can still be used on your computer without putting it up online.
  • Use R entirely to wrangle and clean your data rather than, e.g. opening up Excel and editing the data there.
• If you’re restricted to working with your data in a virtual remote environment (e.g. in Denmark Statistics), you may not have authorization to install some programs. However, most remote environments have the latest software used for data analysis type tasks.

10.1 How do you share?

How do you share? Put your code up on any of these sites. We recommend a combination of GitHub and Zenodo, but the others are also quite good as well. And we’re already showing you how to use GitHub, so you’re one step closer to sharing on your own!

• GitHub
• GitLab
• Zenodo
• figshare
• Open Science Framework

Next question might, when do you share? Right away! No matter how ugly. If its ugly, that’s fine! The point is you start and that you get more comfortable doing it until it becomes second nature to share and in the process, your code gets better because you know someone might look at your code.

And even if your code isn’t reproducible, even if others can’t run it own their own, sharing is the first step to becoming better. At the least, others can inspect your code for its overall logic.

We as researchers try to find our niche, make our own space in the research world. Sometimes its a real struggle to find that niche… but this is one of those niches! Very few people are sharing their code! You start doing the simplest thing of sharing your code and you will be one of very very few people who do. And this isn’t a niche, this is a gaping hole in our modern scientific process.

So as soon as you have an analysis project set up, put it up on either GitHub or GitLab (another service like GitHub). Alternatively, you can upload it when you also finish your manuscript.

10.2 What else can you do?

The other things you can start doing is find or start building a community of people who also use R or are doing reproducibility or any other computational work. Use them as support and help and also give back too.

Start doing code reviews in your research group. Code review would be where you look over each others code, check that it works, check that it makes sense, that it’s readable and understandable. The nice thing with doing code reviews is that it dispels the mystery around code and about criticizing it and trying to improve it. We review manuscripts, why not code? Do note though, that this is way easier said than done!

You can teach! Teach others. Use these teaching materials. Or get involved with this course next year. Or now! Several participants from these courses are or will soon be helping to improve the material for next year. There are also so many other things you can get involved in, aside from this course. Let us know if you’re interested!

We also have an informal, once-monthly, Coding Club you can join or follow. Information about it is on the website. We do the sessions virtually on the r-cubed Discord server.