GitHub Copilot in RStudio,
it’s finally here!

Tom Mock: Product Manager


This talk closes issue #10148

GitHub copilot integration with RStudio, the issue header

Ok, so what’s generative AI?

Generative AI refers to a category of AI models and tools designed to create new content, such as text, images, videos, music, or code. Generative AI uses a variety of techniques—including neural networks and deep learning algorithms—to identify patterns and generate new outcomes based on them. - GitHub blog

Midjourney prompt:

png transparent background, a seated, robotic android boston terrier wearing pilot goggles, in the style of pixel art, full body --style raw --stylize 50

Generative AI for text

For text generation, Generative AI just wants to predict the next word/token/string!

I might ask ChatGPT (asking is also known as “prompting”):

“Complete the sentence every good.”

‘Trust’ but verify

  • Generative AI doesn’t have a ‘brain’ or general intelligence
  • It’s just a model that’s been trained on a lot of data
  • It’s not always right, appropriate, or optimal
  • It can make up things that aren’t true, or use code that doesn’t actually exist (or run!)
  • So it’s important to verify the output before using it
  • But we can use it to quickly experiment and maybe provide a novel direction – basically “prompt” ourselves and our own knowledge

What is Copilot?

GitHub Copilot is an AI pair programmer that offers autocomplete-style suggestions and real-time hints for the code you are writing by providing suggestions as “ghost text” based on the context of the surrounding code - GitHub Copilot docs


  • Parses code and environment
  • Supplies possible completions
  • Static set of completions in popup
  • IDE provided from local disk


  • Parses code, environment and training data
  • Supplies likely completions
  • Dynamic set of completions via ‘ghost text’
  • Generative AI provided via API endpoint

Autocomplete vs Copilot

Autocomplete in RStudio, with a list of possible completions

Copilot in RStudio, with a list of possible completions

RStudio, now with ‘Ghost Text’


Copilot generated
‘ghost text’

Ghost text doesn’t “exist” in
document until accepted

Copilot in RStudio, with a list of possible completions

Copilot in RStudio

gif of Copilot autocompletion

Copilot in RStudio

Diagram of Copilot autocompletion

Get started

  • Get a subscription to GitHub Copilot, Personal or Business
  • Tools > Global Options > Copilot Tab

Sign in page GitHub Auth Signed-in

Don’t be afraid to play around!

Keyword is a word game, popularized by the Washington Post.

Keyword screenshot Keyword screenshot, solved

How can we solve the Keyword game?

With RStudio + Copilot of course!

Getting the most out of the generative loop

GitHub Copilot is a generative AI tool that can be used to generate (ie predict) output text, and more specifically, code.

Generative AI doesn’t understand anything. It’s just a prediction engine! - David Smith

  • Context - What prompts have been provided?
  • Intent - What is the user trying to do?
  • Output - What is actually returned?

Better context == closer to intent == better output

Simple, Specific, and use Comments!

Simple and specific: Break down complex tasks

Provide a high-level description of the project goal at the top level, and build off that with more specific tasks.

For example, I know how to play Keyword, and I know others have solved similar games (Wordle) with R.

# create a function to solve the Keyword game
# this game uses a 6 letter horizontal word at the
# intersection of 6 other vertical words, 
# where a missing letter from each of the vertical words
# accounts for one letter of the mystery 6 letter length horizontal word

# how to play
# Guess 6 letters across.
# Letters must spell words across and down.

Simple(st): Cheat Be clever

url <- ""
raw_json <- fromJSON(url)
[1] "staple"

Ok that’s cheating, but what else is in there?

[1] "_ee"    "chan_"  "b_re"   "s_ur"   "real_y" "scal_" 
[1] "[s]ee"    "chan[t]"  "b[a]re"   "s[p]ur"   "real[l]y" "scal[e]" 

Simple: Just get the hint words

Break it down into component parts, let’s work with the hint words!

json_url function screenshot with ghost-text

Simple: Just get the hint words

raw_json <- json_url(date = "2023-08-09") |> jsonlite::fromJSON(simplifyVector = FALSE)

hints <- as.character(raw_json$words)
[1] "_ee"    "chan_"  "b_re"   "s_ur"   "real_y" "scal_" 

Specific: Use expressive names (and comments)

  • Use expressive names for variables, functions, and objects (this is best practice anyway!)

Copilot in RStudio, with a list of possible completions

Copilot in RStudio, with a list of possible completions

Specific: Use expressive names

top_words function with ghost-text

letters_from_blank function with ghost-text

S2C: guess_keyword() function

# Solved with Copilot and some human ingenuity in RStudio!
 The keyword is one of the following:
 recipe, repipe 

 The played words are some of the following:
 [r]aid, stat[e], [c]ap, gamb[i]t, [p]arent, decr[e]e
 [r]aid, stat[e], [p]ap, gamb[i]t, [p]arent, decr[e]e 

Getting stuck?

  • Add more context, and follow S2C (Simple, Specific, and use Comments)
  • Prompt again or in a different way
  • Add more top-level or inline comments
  • Build off your own momentum (write some of your own code)
  • Turn off Copilot for a bit

Better context == closer to intent == better output

More than one way to generate text

Ghost Text

Ghost text in RStudio, with a list of possible completions

Chat with {chattr}

Chat with chattr in RStudio, with a list of possible completions


Chat with chattr in RStudio viewer pane

{chattr} - enriched requests



chattr(preview = TRUE)
#> ── Preview for: Console
#> • Provider: Open AI - Chat Completions
#> • Path/URL:
#> • Model: gpt-3.5-turbo
#> • temperature: 0.01
#> • max_tokens: 1000
#> • stream: TRUE
#> ── Prompt:
#> role: system
#> content: You are a helpful coding assistant
#> role: user
#> content:
#> * Use the 'Tidy Modeling with R' ( book as main reference
#> * Use the 'R for Data Science' ( book as main reference
#> * Use tidyverse packages: readr, ggplot2, dplyr, tidyr
#> * For models, use tidymodels packages: recipes, parsnip, yardstick, workflows,
#> broom
#> * Avoid explanations unless requested by user, expecting code only
#> * For any line that is not code, prefix with a: #
#> * Keep each line of explanations to no more than 80 characters
#> * DO NOT use Markdown for the code
#> [Your future prompt goes here]

Diagram of chattr request

Generative AI tools with Posit Workbench and RStudio

  • For best outputs, follow S2C (Simple, Specific, and use Comments)

  • GitHub Copilot is an optional integration, available as a Preview feature in 2023.09 release of RStudio and Posit Workbench

  • To provide feedback or report bugs, please open a GitHub Issue on the RStudio repo

  • {chattr} provides integrations to OpenAI’s REST API models and locally hosted models such as LLaMA, and more backends are expected. Install via: remotes::install_github("mlverse/chattr")

Robot cat

Robot cat

Robot cat

Additional Slides

What about different backend models for RStudio?

There will be some options to use community-created RStudio add-ins to connect to other models and still make use of ghost text:

Example of arbitrary ghost text

What other things can Copilot generate?

# test the time_between function with testthat

test_that("time_between works", {
  expect_equal(time_between("2023-08-15", "2022-08-15", "days"), -365)
  expect_equal(time_between("2023-08-15", "2022-08-15", "weeks"), -52.1428571428571)
  expect_equal(time_between("2023-08-15", "2022-08-15", "months"), -12)
  expect_equal(time_between("2023-08-15", "2022-08-15", "years"), -1)
  expect_equal(time_between("2022-12-06", Sys.Date(), "decades"), "Please enter a valid time unit")

Example of Copilot generating code

Example of Copilot generating code

{chattr} interface

The main way to use chattr is through the Shiny Gadget app. By default, it runs inside the Viewer pane, and use as_job = TRUE in RStudio to run it in the background:

# Change default to run as job via: `options(chattr.as_job=TRUE)`
chattr::chattr_app(as_job = TRUE)

Screenshot of chattr running in RStudio

{chattr}: Available models

chattr provides two main integration with two main LLM back-ends. Each back-end provides access to multiple LLM types:

Provider Models Setup Instructions
OpenAI GPT Models accessible via the OpenAI’s REST API. chattr provides a convenient way to interact with GPT 3.5, and DaVinci 3. Interact with OpenAI GPT models
LLamaGPT-Chat LLM models available in your computer. Including GPT-J, LLaMA, and MPT. Tested on a GPT4ALL model. LLamaGPT-Chat is a command line chat program for models written in C++. Interact with local models

The idea is that as time goes by, more back-ends will be added.

What about GenAI for other IDEs in Posit Workbench?

  • Posit Workbench will be upgrading JupyterLab to v4, and dev team exploring JupyterAI
  • VS Code has many extensions for Generative AI, including Codeium, AWS Code Whisperer, Tabnine and more…