R Learning Path

Return to R by learning the work you need to do, not every corner of the language.

Recommended path

Start at the top and move stage by stage. You can revisit any stage whenever your work exposes a gap.

Stage 1

1. Introduction

Mindset, role, and how this path works.

B

Goal

Replace the feeling of "I have to relearn everything" with "I already know the analysis, I just need to learn how R expresses it."

AI

AI Role

Use AI as a translator, explainer, code reviewer, and debugging partner while you learn in context.

U

Audience

Designed for people either returning to R or coming to R as a newcomer.

If you last touched R years ago at university, the fastest route back is not a long course taken in order. It is a guided, applied path: learn enough syntax to recognise what R is doing, use AI to translate what you already know from SAS, or other analysis workflows, then learn from real scripts and real tasks.
Stage 2

Learn enough syntax to recognise what R is doing

Spend a short burst of time on the core shapes of modern R code. The aim is recognition and confidence, not deep mastery.

Goal

Become comfortable seeing data frames, verbs like filter() and mutate(), pipes, and basic ggplot2 code.

What to do

  • Spend one or two days on introductory material only.
  • Focus on data frames, dplyr, ggplot2, pipes, and packages.
  • Do not worry if you cannot write everything from memory.

Ask AI this

"Explain data frames, dplyr, ggplot2, and pipes as if I am a clinical-trial analyst returning to R."
"Show me a tiny example that reads a dataset, filters rows, creates a new variable, and makes a simple plot in R."

Recommended resources

GSK: Intro to R and the tidyverse Practical, structured material for getting comfortable with modern R syntax. gsk-biostatistics.github.io R for Data Science Use selected sections, not necessarily front-to-back, to recognise common tidyverse patterns. r4ds.hadley.nz Introductory video walkthrough A lightweight way to re-familiarise yourself with what R code looks like in practice. youtube.com
If someone can look at a short tidyverse script and roughly follow its shape, this stage has done its job.
Stage 3

Learn through work that actually matters to you

The best way to keep progressing is to choose an analysis, output, or area you are genuinely likely to work on, then use AI to turn that into a learning loop.

Goal

Create a workflow that turns a relevant project into one connected cycle of planning, coding, debugging, and explanation.

What to do

  • Choose something you genuinely care about learning, such as meta-analysis, dashboards, survival analysis, repeated measures work, plotting, reporting, or reproducible pipelines.
  • Write the project or analysis plan in plain English first.
  • Ask AI to generate a first draft of the R code.
  • Run it, inspect the output, then debug with AI when needed.
  • Ask AI to explain the final script and tell you what R concepts you learned through the project.

Ask AI this

"I am likely to need to do meta-analysis in my role. Help me learn how to approach that in R step by step."
"I am interested in dashboards rather than modelling right now. What is a good beginner-friendly R project path for that?"
"Here is the analysis I need to do. Turn this English plan into tidyverse R code."
"Here is my current script and output. What should I check next?"
"Now that this works, explain the final code and point out the R ideas I should remember from this project."

Why this works

People learn faster when the work feels relevant. If meta-analysis is something you are likely to do, learn that in R. If dashboards, plots, reporting, or survival work matter more, start there instead.

The long-term aim is not dependence on AI. It is faster understanding, cleaner iteration, and better transfer from one real project to the next.
Stage 4

Learn from the real code your team already uses

The most relevant R education often comes from the scripts, packages, and conventions already present in your team, especially when they point toward the kinds of analyses or outputs you are likely to work on yourself.

Goal

Understand the actual style of R code used around you, and notice which analyses, reports, or tools feel most relevant to your own work.

What to do

  • Take short sections of shared scripts and study them with AI.
  • Notice the packages, naming patterns, and data-flow style your team prefers.
  • Pay attention to the types of work the code supports, such as meta-analysis, visualisation, dashboards, reporting workflows, or survival analysis.
  • Ask for beginner-friendly rewrites when a script feels too dense.

Ask AI this

"Explain what this code is doing and tell me which R concepts I need to understand first."
"What packages are being used here and why would a clinical-trial team choose them?"
"Looking at this script, what kind of analysis or project is this closest to, and would it be a good learning project for me?"
"Rewrite this in a more beginner-friendly way but keep the same analytical result."

Recommended resources

Posit Recipes Bite-sized worked examples that pair well with reading and adapting real scripts. posit.cloud/learn/recipes Posit cheat sheets Useful when you are reading team code and need a quick reminder of syntax or function patterns. posit.co/resources/cheatsheets
This is often where R starts to feel real: the learner can connect tutorial ideas to the scripts, analyses, and outputs their team actually cares about, then choose one of those areas as the next place to learn more deeply.
Stage 5

Use AI as a translator, not just a search engine

AI is most useful when you ask it to bridge from what you already know into R code, explanation, and debugging help.

Goal

Learn in context by translating familiar analytical tasks into R instead of trying to memorise syntax in isolation.

What to do

  • Describe the task the way you would describe it in SAS, or plain English.
  • Ask AI to explain code line by line, not only to generate code.
  • When you hit an error, paste the code and error together and ask for diagnosis.

Ask AI this

"I would do this in SAS. How do I do it in R using tidyverse code?"
"Explain this R code line by line as if I am an analyst new to R."
"I ran this code and got this error. Tell me what the error means, what probably caused it, and how to fix it."
"Rewrite this R code in a more beginner-friendly way and explain the packages being used."

Recommended resources

  • Save good prompts that worked well for translation, explanation, and debugging.
  • Keep asking AI to rewrite dense code in a simpler style until the structure feels familiar.
  • Use this stage alongside real datasets and real scripts, not only toy examples.
This stage is where learning speed usually jumps. AI lets people learn from real work instead of waiting until they feel fully trained.