Picture this: You need to create a violin plot and remember hearing it’s possible in R.
Excited by the possibility, you dive into learning R basics. However, after hours of tutorials and documentation, fatigue sets in before you even reach the violin plot section. Frustrated, you retreat to familiar point-and-click software, settling for basic plots instead.
“Maybe violin plots just aren’t for me,” you sigh. Like many beginners, you might even joke that learning to play an actual violin seems easier than creating one in R.
Many think they must master all of R before writing their first line of code. I once believed this too. But through my journey from clinician to programmer, I discovered a simpler truth: you only need to learn what serves your immediate needs. This realization came from an unexpected source – my medical school days.
Dr. V, a top performer in my medical college, revolutionized how I think about learning. While others scrambled to memorize entire textbooks, he took an unconventional approach: learning one disease per day, typically inspired by patients he encountered. This focused strategy both impressed and unnerved us.
His reasoning was brilliantly simple. Instead of cramming countless rare conditions, he mastered the common cases he’d actually encounter in practice and exams. For each condition, he built a complete understanding – absorbing lecture notes, filling knowledge gaps through library research, and creating detailed mental models that he could effectively translate onto paper during exams.
This method transformed my approach to R programming. Rather than attempting to learn everything at once, I focused on immediate needs. When I needed boxplots, I mastered just boxplots. This approach aligns perfectly with cognitive load theory, which shows that breaking complex skills into manageable chunks accelerates learning. Over time, I built a personal library of code snippets, making each new task easier than the last.
For fellow clinicians venturing into programming, here are crucial pitfalls to avoid:
- Memorizing syntax without practical context
- Switching between tasks before achieving mastery
- Neglecting to build a personal code reference library
Success in programming, like medicine, comes from mastering one concept at a time. To help you get started, I’ve created a 30-day visualization series that applies this exact approach – perfect for clinicians taking their first steps into R programming.
