5 tips for aspiring R users

Motivation

As a Statistical Programming Trainer, one of the most common questions I get asked is where do I start??

More than that, where do I start and how do I find the motivation to keep going?

Many aspiring R users are excited to dive into R, dreaming of beautiful plots and elegant data wrangling…only to hit a wall.

Maybe you get stuck on syntax, or maybe you find yourself crawling back to SAS, SPSS or Excel just this once. Before you know it, R becomes that thing you meant to learn.

But don’t give up just yet! As someone who’s been teaching R for nearly a decade, I’ve seen what works, and what doesn’t.

If you are serious about learning R, here are my top 5 tips to help you stay on track and actually enjoy the journey.

Tip 1. Practice, Practice, Practice!

It sounds obvious, but this is where most people stumble.

Learning R is a lot like learning a new language. If you switch back to your “mother tongue” (your comfort language) every time you hit a bump, you’ll never become fluent.

Yes, your early scripts might be clunky. Yes, you might feel like a toddler trying to form sentences. But that’s exactly how you grow.

Start small.

Pick tasks you’d normally do in another language or tool and commit to doing them in R. Not sure where to begin? Find a dataset that interests you and start to explore it using R

Great sources of datasets include:

• The datasets package
• The TidyTuesday project
• Kaggle Datasets

Tip 2. Tap Into the R Community

One of R’s greatest strengths is its vibrant, generous community. Whether you’re stuck on a bug or looking for inspiration, there’s a wealth of free resources out there.

If you are not sure where to start looking, refer to popular forums, such as Stack Overflow or the Posit Community, discover popular R blogs on the R-Bloggers website, or refer to this curated list of materials from Nicola Rennie.

Don’t go it alone! Someone out there has probably faced (and solved) the exact problem you’re dealing with.

Tip 3. Develop Good Habits Early

Most of us start by hacking together code just to get the job done. But messy code becomes a nightmare when you revisit it weeks later.

Instead, aim to write scripts that are human-readable rather than simply computer-readable:

• Use sections (Ctrl + Shift + R in RStudio) to organize your workflow
• Add comments to explain your logic
• Stick to a consistent style, e.g. using the Tidyverse style guide
• Use descriptive variable names that make your code self-explanatory

Your future self (and anyone else reading your code) will thank you!

Tip 4. Read Other People’s Code

One of the best ways to learn is by seeing how others solve problems. Whether it’s a colleague’s script or a stranger’s GitHub repo, studying real-world code can spark new ideas and reveal tricks you hadn’t considered.

A great place to start? Check out submissions for the weekly Tidy Tuesday challenge for visualisation inspiration.

Tip 5. Use AI Tools Sparingly

Yes, AI tools like Copilot and ChatGPT can be helpful. But they’re not a substitute for understanding.

Over reliance on AI can lead to bad habits, insecure code, and a false sense of confidence. Use them as a last resort, not your first step and always double-check the output. AI can (and does) make mistakes.

Also worth noting: these tools come with environmental and ethical concerns, so use them mindfully.

Final Thoughts

Learning R isn’t about finding shortcuts, it’s about building confidence through consistent practice, curiosity, and community.

Stick with it, and you’ll be amazed at how far you can go.