Toward a more open science practice with R

Recently I did a webinar with my colleague Joshua Rosenberg, hosted by the Center for Open Science, on Analyzing Educational Data with Open Science Best Practices, R, and OSF. You can find a recording of the webinar here and our slides and an example R Notebook are in an OSF repository here. I thought I would do this blog post to summarize some of the main things I talked about there and highlight some of the more important aspects.

This webinar was ostensibly about open science for educational data. I think most of us want to engage in more open science practices (which could include open data, open materials, preregistration of studies, replication, posting preprints, and reporting null results) but don’t know necessarily where to begin or what tools to use. I think we tried to make the argument that workflows, procedures, practices, and behaviors that are good practice for you by yourself, future you, and your internal team can also be good for open science. And that using R and its many packages and tools is a good way of achieving those goals.

I’ve written many times before about how much I love using R and how I want others to incorporate it more into their practice. I’ve now collected the series of blog posts as well as other related blog posts (like this one!) and slides onto one page for easy access. You can go to cynthiadangelo.com/r/ to see all of the R related stuff that I have worked on linked in one place.

In general, I’ve been thinking a lot lately about my values and commitments as a researcher and how I approach my work in a very basic way. What is important to me, my collaborators, and my field? How could I be doing things differently or looking at my assumptions differently?

This thinking led me to this set of things to consider for a more open science approach:

  • There are a lot of technical tools and solutions to some of the open science problems. But there are also philosophical/ethical/moral issues to consider.
  • Humans are participants that helped produce your data. All humans deserve respect and so do their data.
  • There’s no easy answer for some of these situations you might face. That’s ok. Part of what open science asks is to consider your options and document your decision making.
  • Reflect early on in your process about what your goals are and how you want to achieve them. What are your values? How do these match up?

Some of the tips and guidelines that I talk about in the webinar: (1) Documentation is so important. It’s also really difficult. Making things clearer for you and your future self will also make them clearer for others who might eventually see your code. (2) A tidy data structure will make things easier for you and easier for others to understand. If you’re not already on board the tidyverse train, it’s never too late to start. (3) Make sure you have a data dictionary somewhere that explains all of your variables. This sounds obvious, but it doesn’t usually happen because in the moment you think you understand all of your variables. But future you will not remember all of those things. Write it down. Preferably in a R Notebook (more on that later). (4) Pick a consistent and clear file naming convention early on in your project (preferably before you begin data collection). Think about the date format you use and think about the unit of analysis you will care about later and try to incorporate it directly into your filename to help with filtering and analysis later on. (5) Of course I want you to visualize your data. Descriptive statistics can be misleading sometimes and visualization is an important step in your process and is not just an end product.

The thing that ties all of this together is using a R Notebook within RStudio. R Notebooks make use of RMarkdown, a flavor of Markdown, my favorite way to write. It is a plain text file, so it’s easy to version control and easy to share, both things that are hugely important when thinking about open science. I really like R Notebooks because you can easily incorporate explanatory text alongside your code and figures/graphs are persistent across the page so you can scroll and easily refer back to something above or below where you are working. This, in my opinion, is a much better way to use R than the older way with scripts and the console.

R Notebooks can produce an html file that you can send to your colleague or friend who doesn’t have R installed and they will be able to open it up in a browser and see all of your wonderful thoughts and figures. It’s really great. You can also execute code in Python or JavaScript or D3 (or a few other programming languages) in addition to R, so it’s very versatile. There are a lot of output formats as well, including pdf, Word, slide decks, dashboards, and books. And they are all customizable. Check out the RMarkdown website to see all of the options and more details on how they work. For me, they dramatically changed (in a good way) how I do my work.

Maybe a good question to leave you with is to try and answer “What is the best way for you to work toward open science?” It doesn’t have to be a big thing; it can be a bunch of small changes over time. This hopefully shouldn’t feel too overwhelming. There are lots of us here to help.

Author: cynthiadangelo

I am a researcher, working on educational games, science education, and data visualization. I like photography, soccer, traveling, and teaching my dog new tricks.

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