LLM for Data Science in R

Learn to use Large Language Models in R for data science. Extract data, classify text, generate code, and build AI-powered workflows.

Large Language Models are transforming data science workflows. Instead of writing complex regex or manual rules, you can describe what you want in plain English.

Why Use LLMs in R?

Task	Traditional Approach	With LLMs
Extract names from text	Complex regex patterns	Describe the schema, get structured data
Classify text	Train ML model on labeled data	Zero-shot classification with prompts
Analyze sentiment	Sentiment lexicons or ML	Understands sarcasm and context
Clean messy data	Manual rules for each case	Describe the desired format
Generate code	Write it yourself	Describe what you need
Summarize documents	Statistical methods	Natural language summaries

Choose Your Provider

Provider	Best For	Cost	Setup
OpenAI (GPT-4)	General tasks, coding	Pay per token	API key
Claude	Coding, long documents	Pay per token	API key
Gemini	Long documents, free tier	Free tier available	API key
Ollama (Local)	Privacy, no costs	Free	Local install

Recommendation: Start with ellmer - it works with all providers using the same syntax.

Start Here

New to LLMs in R? Follow this path:

1. How to Use ellmer in R - Learn the unified interface
2. Pick a provider - OpenAI, Claude, or Ollama
3. Extract Structured Data - Most useful for data science

By Task

I want to…	Read this
Connect R to ChatGPT	OpenAI API Tutorial
Use Claude for coding help	Claude API Tutorial
Use Google’s Gemini	Gemini API Tutorial
Run models locally (free)	Local LLMs with Ollama
Classify text into categories	Text Classification
Analyze sentiment in reviews	Sentiment Analysis
Extract entities from text	Structured Data Extraction
Build semantic search	Embeddings & Semantic Search
Use one package for all providers	ellmer Package

All Tutorials