From Charts to Consistency: My R Journey to Reproducibility

TLDR: In this post, I provide beginners with practical tips to embrace reproducibility while using R.

I see a lot of folks stumbling in R. They find it difficult, complex, and intimidating. They start their data analysis or visualization and find it very hard to proceed. They give up and move to other tools. There is a secret to approach R in a better way. This will not make you a pro overnight, but it will make you realize why you should embrace R as a beginner, despite its steep learning curve.

Listen, folks: R isn’t rocket science.

I’m neither a programmer nor a coder, but I conduct all my statistical analysis in R. The possibilities and capacities of R never cease to amaze me.

R is a tool for reproducibility. That’s the secret.

In 2019, I approached R for its data visualization capabilities. I was fascinated by the beautiful and insightful graphs I could create. But as I delved deeper, I discovered R’s true potential: reproducibility.

To keep things simple, I would define reproducibility in the context of this blog as the ability to recreate an analysis you did by anyone, anywhere, at any time. This is the essence of R. I wrote a blog post on how conducting reproducible research even as a beginner can be a game-changer.

Viewing R as difficult software that requires coding, and feeling threatened by its steep learning curve, is why you make no progress. Approaching R for its reproducibility capabilities is the key to unlocking its potential as a beginner. Once this parallel world opens, you never want to go back.

Reproducibility is akin to making a rasam eveyrtime the similar way and others able to reproduce it by your recipe.

Reproducibility is producing the same results from the same data

R stands out among other software due to its strong emphasis on reproducibility. Unlike many other tools, R allows you to document every step of your analysis process within scripts, ensuring that your work can be exactly replicated by anyone, anywhere, at any time. This transparency and accuracy are vital for robust data analysis and scientific research.

I offer 3 best practices and 3 mistakes to avoid to embrace reproducibility as an R beginner:

3 Best Practices

1. Clarify Code with Comments: Always comment your code and document each step to ensure anyone can understand and reproduce your analysis. Writing a log is a much-needed practice during the initial stages of analysis. If you are keen to develop a logging practice, read my blog post on logging using the logr
2. Use Separate Scripts for Each Task: Organize your work by creating different scripts for data cleaning, analysis, and visualization. This practice keeps your project organized and makes it easier to track and debug your code. It also allows you to reuse and share specific parts of your workflow with others, enhancing collaboration and efficiency.
3. Treat Errors as Opportunities: This can be incredibly frustrating when you start with R. But I recall one of my friend’s words - Welcome mistakes! When you encounter errors, use them as chances to improve your code and make it more robust.

3 Mistakes to Avoid

1. Not Using Projects: Always use R projects to keep your work organized and paths relative. By using R projects, you maintain a structured workspace that helps avoid confusion and errors related to file paths. It also ensures that your scripts are portable and can be easily shared or transferred without breaking dependencies. If you are new to projects in R, read here to know more.
2. Editing Data Outside of R: Avoid modifying your data in other software. Keep all data manipulation within your R scripts. This ensures that all changes are documented and reproducible, maintaining the integrity of your workflow. It also allows for easier tracking of data transformations and provides a clear audit trail for verification and troubleshooting.
3. Not Using Relative Paths: Using relative paths ensures that your scripts will work on any system without needing modifications. The here package simplifies path management, making your project more robust and easier to share. For more details on how to implement this, read my blog post on the here package here.

I am a staunch advocate for reproducibility. I believe that reproducibility is a Non-Negotiable Imperative for Data Science. Embracing R as a tool for reproducibility changes the game for beginners and transforms your approach to data analysis.

One Thing for approaching R as a beginner

Embrace R as a tool for reproducibility. By centering your efforts on reproducibility, you’ll produce more robust and shareable work.