Calling a Chatbot from R with ellmer

Integrating AI directly into your R workflow

R-Hacks N.13

Many analysts now use AI tools while working in R.

But the workflow often looks like this:

Copy code → paste into ChatGPT → copy result → paste back into R.

This breaks reproducibility and interrupts analytical flow.

Note

A better approach is simple: Call the model directly from R.

Packages like {ellmer} make this possible with just a few lines of code.

This R-Hack shows how to make AI part of your workflow instead of a separate tool.

1️⃣ A Minimal ellmer Example

The simplest way to call a chat model from R looks like this:

library(ellmer)

chat <- chat_openai()

chat$chat("Explain what a linear model is in simple terms")

You now have a chatbot inside your R session.

This is useful because it keeps:

• analysis
  
• prompts
  
• results

in the same environment.

2️⃣ A Quick Comparison with Python

Many AI examples appear first in Python:

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-4.1-mini",
    input="Explain what a linear model is"
)

print(response.output_text)

R can now do something very similar:

library(ellmer)

chat <- chat_openai()

chat$chat("Explain what a linear model is")

The point is not R vs Python.

The real advantage is workflow:

If your data, models, and reports already live in R, calling AI from R keeps everything in one place.

3️⃣ A Practical Analyst Example

One useful habit is asking AI to explain model output.

For example:

chat$chat("
Explain this model output in plain English:

Residual standard error: 2.31 on 98 degrees of freedom  
Multiple R-squared: 0.74  

Focus on interpretation, not formulas.
")

This can help when:

• preparing reports
  
• translating technical results
  
• checking interpretations
  
• drafting explanations

Treat this as a drafting assistant, not a final authority.

4️⃣ A Reusable Prompt Pattern

Instead of typing prompts interactively, store them:

prompt <- "
You are a senior data analyst.

Explain this regression output for a business audience.

Rules:
- Keep explanation under 120 words
- Avoid statistical jargon
- Focus on practical meaning
"

chat$chat(prompt)

This improves:

• reproducibility
  
• consistency
  
• documentation of reasoning

Prompts become part of your analysis pipeline.

5️⃣ When This Is Most Useful

Calling AI from R works especially well when you:

• document analysis steps
  
• generate explanations
  
• explore interpretations
  
• prepare communication text
  
• validate reasoning paths

It is less useful for:

• final decisions
  
• statistical validation
  
• replacing domain expertise

Tip

Think of AI as a workflow accelerator, not an analytical authority.

6️⃣ A Small Safety Rule

Always:

• review outputs critically
  
• verify claims
  
• check numbers manually
  
• keep prompts precise

AI performs best with clear constraints.

In Short

• Calling AI from R avoids copy-paste workflows
• ellmer makes LLM integration simple
• AI helps explain models and draft text
• Store prompts for reproducibility
• Treat AI as assistant, not authority

Small integrations like this often create the biggest workflow improvements.

Tip

If you want to stay up to date with the latest events and posts from the Rome R Users Group:

👉 https://www.meetup.com/rome-r-users-group/