gander is a high performance and low friction chat experience for data scientists in RStudio and Positron. The package supplies an addin that can be registered to a keybinding. Once that’s done, just trigger the addin while you’re working on documents and type a request; along with your request, the addin will automatically inject context about the document you’re working in and the objects in your R environment.
This interface excels beyond a typical LLM chat in two notable ways:
Higher-performance: Including information about your R environment and relevant code ensures that the model has all of the context it needs to supply an answer that’s as accurate as possible and aligns with your taste and style preferences in that it can mirror the code you’ve written already.
Lower-friction: gander eliminates the need to copy and paste relevant code into the chat window and its result back into your document. There’s also no need to type anything other than your request, e.g. descriptions of the style you’d like the response to take or bits of exposition like “Here’s some additional context:”. Further, iterating on pieces of content is easier in that the model’s output will automatically be selected after it’s streamed into your document; to iterate on a response, just trigger the addin again.
Choosing a model
The gander addin supports any model supported by ellmer. When choosing a model for use with gander, you’ll want to the use the most performant model possible that satisfies your privacy needs; gander automatically passes along your code and the objects in your R environment to your chosen model, so it’s especially important to consider data privacy when using LLMs with gander.
gander uses the .gander_fn and .gander_args
options to configure which model powers the addin.
.gander_fn is the name of an ellmer chat_*()
function as a string, and .gander_args is a list of
arguments to pass to that function. For example, to use OpenAI’s
GPT-4o-mini, you might write
options(.gander_fn = "chat_openai", .gander_args = list(model = "gpt-4o-mini")).
Paste that code in your .Rprofile via
usethis::edit_r_profile() to always use the same model
every time you start an R session.
If you’re using ellmer inside a organization, you’ll be limited to
what your IT department allows, which is likely to be one provided by a
big cloud provider, e.g. chat_azure(),
chat_bedrock(), chat_databricks(), or
chat_snowflake(). If you’re using ellmer for your own
exploration, you’ll have a lot more freedom, so we have a few
recommendations to help you get started:
As of early 2025, Anthropic’s Claude Sonnet 3.5 is a very powerful model for code assistance, and thus
chat_claude()is the default model supported by gander. If you want to use Claude, you’ll need to register an API key to the environment variableANTHROPIC_API_KEY. No need to set the.gander_*options in this case.Regarding OpenAI’s models,
chat_openai()defaults to GPT-4o, but you can usemodel = "gpt-4o-mini"for a cheaper, lower-quality model, ormodel = "o1-mini"for more complex reasoning; to use an OpenAI model, you’ll need to set the optionsoptions(.gander_fn = "chat_openai", .gander_args = list(model = "gpt-4o-mini"))and register your OpenAI API key with theOPENAI_API_KEYenvironment variable.You can use a local model with
chat_ollama(), which uses Ollama and allows you to run models on your own computer. While the biggest models you can run locally aren’t as good as the state of the art hosted models, they don’t share your data and are effectively free. To use an ollama model, run the model locally and then setoptions(.gander_fn = "chat_ollama", .gander_args = list(model = "model-name")).
What is gander actually doing?
gander automatically incorporates two kinds of context about your analyses under the hood:
- Code context: The assistant will automatically inform the model about the type of file you’re working in and relevant lines of code surrounding your cursor.
- Environment context: The assistant also interfaces directly with your global environment to describe the relevant data frames you have loaded, including their column names and type. This allows you to describe the analyses you’d like to carry out in plain language while gander takes care of describing your computational environment to the model.
For example, imagine you’re working in a .R file that looks like this:
stackoverflow is a data set containing results from the annual Stack Overflow Developer Survey.
You select stackoverflow, trigger the addin, and type
plot salary vs. years coded. To effectively respond to this
input, a model would need to know a few additional things:
- Code context:
- What framework does this user want to use to plot this data?
- In what style should the model respond? Notably, this is a
.Rfile, so any exposition would need to live inside of comments.
- What does the data look like? Specifically, what are the column names related to the salary and number of years coded?
From the surrounding contents in the file, we can answer
1a); the user likely wants to use ggplot2 to do so. We
can answer 1b) by inspecting the file extension—since
this is an .R file, the model should respond only with
code, omitting exposition and backticks. We can answer
2) using the user’s R environment; if they’ve run that
data("stackoverflow", package = "modeldata") line, then the
stackoverflow object will live in their environment and we
can print it out to learn more about it. The gander addin will
preemptively answer all of these questions for the model by including
context from each of these sources.
To demonstrate, we can run this stackoverflow example
ourselves and then call gander::gander_peek() to peek at
everything gander actually sent to the model. When I do so:
gander::gander_peek()
#> <Chat turns=3 tokens=1077/52>
#>
#> ── system ─────────────────────────────────────────────────────────────────────
#> You are a helpful but terse R data scientist. Respond only with valid R code:
#> no exposition, no backticks. Always provide a minimal solution and refrain
#> from unnecessary additions. Use tidyverse style and, when relevant, tidyverse
#> packages. For example, when asked to plot something, use ggplot2, or when
#> asked to transform data, using dplyr and/or tidyr unless explicitly instructed
#> otherwise.
#>
#> ── user ───────────────────────────────────────────────────────────────────────
#> Up to this point, the contents of my r file reads:
#>
#> ```
#> library(ggplot2)
#>
#> data("stackoverflow", package = "modeldata")
#>
#> ```
#>
#> Now, plot salary vs. years coded in the following:
#>
#> stackoverflow
#>
#> Here's some information about the objects in my R environment:
#>
#> ```
#> stackoverflow
#> #> tibble [5,594 × 21] (S3: tbl_df/tbl/data.frame)
#> #> $ Country : Factor w/ 5 levels "Canada","Germany",..: 4 5 5 2 3 5 5 2 5 2 ...
#> #> $ Salary : num [1:5594] 100000 130000 175000 64516 6636 ...
#> #> $ YearsCodedJob: int [1:5594] 20 20 16 4 1 1 13 4 7 17 ...
#> #> $ OpenSource : num [1:5594] 0 1 0 0 0 0 0 1 1 1 ...
#> #> $ Hobby : num [1:5594] 1 1 1 0 1 1 1 0 1 1 ...
#> #> $ CompanySizeNumber : num [1:5594] 5000 1000 10000 1000 5000 20 20 5000 20 20 ...
#> #> $ Remote : Factor w/ 2 levels "Remote","Not remote": 1 1 2 2 2 2 2 2 2 2 ...
#> #> $ CareerSatisfaction : int [1:5594] 8 9 7 9 5 8 7 7 8 9 ...
#> #> $ Data_scientist : num [1:5594] 0 0 0 0 0 0 0 0 0 0 ...
#> #> $ Database_administrator : num [1:5594] 0 0 0 0 0 0 0 0 0 0 ...
#> #> $ Desktop_applications_developer : num [1:5594] 0 0 0 0 0 0 0 0 1 1 ...
#> #> $ Developer_with_stats_math_background: num [1:5594] 0 0 0 0 0 0 0 0 0 1 ...
#> #> $ DevOps : num [1:5594] 0 1 0 0 0 0 0 0 0 0 ...
#> #> $ Embedded_developer : num [1:5594] 1 1 0 0 0 0 0 0 0 0 ...
#> #> $ Graphic_designer : num [1:5594] 0 0 0 0 0 0 0 0 0 0 ...
#> #> $ Graphics_programming : num [1:5594] 0 0 0 0 0 0 0 0 0 0...
#> #> $ Machine_learning_specialist : num [1:5594] 0 0 0 0 0 0 0 0 0 0 ...
#> #> $ Mobile_developer : num [1:5594] 0 0 0 0 0 0 0 0 1 0 ...
#> #> $ Quality_assurance_engineer : num [1:5594] 0 1 0 0 0 0 0 0 0 0 ...
#> #> $ Systems_administrator : num [1:5594] 0 0 0 0 0 0 0 0 0 0 ...
#> #> $ Web_developer : num [1:5594] 0 1 1 1 1 1 1 1 0 0 ...
#> ```
#>
#> ── assistant ──────────────────────────────────────────────────────────────────
#> ggplot(stackoverflow, aes(x = YearsCodedJob, y = Salary)) +
#> geom_point() +
#> labs(x = "Years Coded", y = "Salary")Even though the user selected only a variable name and typed 5 words, that request was automatically situated in enough context for the model to reply only in R code that uses ggplot2 and references column names that the user hasn’t supplied.
First, gander knows the framework to use since the preceding lines in
the file have been inlined into the user prompt. (By default, gander
recommends that models lean on the tidyverse in their responses anyway.
You can adjust this with ?gander_options.) Then, with
respect to style, gander has detected that we’re working in a
.R file and thus writes “Respond only with valid R code” in
the system prompt. Finally, gander inspects the code selection and
describes variables from the global environment if they’re mentioned
either in the selection or in the text inputted in the addin. In this
case, that’s only the stackoverflow data. Altogether, this
allows for the model to tailor its response to the user’s development
environment without the user needing to supply any context
themselves.