Enable LLM to draft and execute R code

This function adds a prompt wrap to a tidyprompt() that instructs the LLM to answer the prompt with R code. There are various options to customize the behavior of this prompt wrap, concerning the evaluation of the R code, the packages that may be used, the objects that already exist in the R session, and if the console output that should be sent back to the LLM.

Usage

answer_using_r(
  prompt,
  add_text = "You must code in the programming language 'R' to answer this prompt.",
  pkgs_to_use = c(),
  objects_to_use = list(),
  list_packages = TRUE,
  list_objects = TRUE,
  skim_dataframes = TRUE,
  evaluate_code = FALSE,
  r_session_options = list(),
  output_as_tool = FALSE,
  return_mode = c("full", "code", "console", "object", "formatted_output", "llm_answer")
)

Arguments

prompt

A single string or a tidyprompt() object

add_text

Single string which will be added to the prompt text, informing the LLM that they must code in R to answer the prompt

pkgs_to_use

A character vector of package names that may be used in the R code that the LLM will generate. If evaluating the R code, these packages will be pre-loaded in the R session

objects_to_use

A named list of objects that may be used in the R code that the LLM will generate. If evaluating the R code, these objects will be pre-loaded in the R session. The names of the list will be used as the object names in the R session

list_packages

Logical indicating whether the LLM should be informed about the packages that may be used in their R code (if TRUE, a list of the loaded packages will be shown in the initial prompt)

list_objects

Logical indicating whether the LLM should be informed about the existence of 'objects_to_use' (if TRUE, a list of the objects plus their types will be shown in the initial prompt)

skim_dataframes

Logical indicating whether the LLM should be informed about the structure of dataframes present in 'objects_to_use' (if TRUE, a skim summary of each data.frame type object will be shown in the initial prompt). This uses the function skim_with_labels_and_levels()

evaluate_code

Logical indicating whether the R code should be evaluated. If TRUE, the R code will be evaluated in a separate R session (using 'callr' to create an isolated R session via r_session). Note that setting this to 'TRUE' means that code generated by the LLM will run on your system; use this setting with caution

r_session_options

A list of options to pass to the r_session. This can be used to customize the R session. See r_session_options for the available options. If no options are provided, the default options will be used but with 'system_profile' and 'user_profile' set to FALSE

output_as_tool

Logical indicating whether the console output of the evaluated R code should be sent back to the LLM, meaning the LLM will use R code as a tool to formulate a final answer to the prompt. If TRUE, the LLM can decide if they can answer the prompt with the output, or if they need to modify their R code. Once the LLM does not provide new R code (i.e., the prompt is being answered) this prompt wrap will end (it will continue for as long as the LLM provides R code). When this option is enabled, the resulting prompt_wrap() will be of type 'tool'. If TRUE, the return mode will also always be set to 'llm_answer'

return_mode

Single string indicating the return mode. One of:

• 'full': Return a list with the final LLM answer, the extracted R code, and (if argument 'evaluate_code' is TRUE) the output of the R code

• 'code': Return only the extracted R code

• 'console': Return only the console output of the evaluated R code

• 'object': Return only the object produced by the evaluated R code

• 'formatted_output': Return a formatted string with the extracted R code and its console output, and a print of the last object (this is identical to how it would be presented to the LLM if 'output_as_tool' is TRUE)

• 'llm_answer': Return only the final LLM answer

When choosing 'console' or 'object', an additional instruction will be added to the prompt text to inform the LLM about the expected output of the R code. If 'output_as_tool' is TRUE, the return mode will always be set to 'llm_answer' (as the LLM will be using the R code as a tool to answer the prompt)

Value

A tidyprompt() object with the prompt_wrap() added to it, which will handle R code generation and possibly evaluation

Details

For the evaluation of the R code, the 'callr' package is required. Please note: automatic evaluation of generated R code may be dangerous to your system; you must use this function with caution.

See also

answer_using_tools()

Other pre_built_prompt_wraps: add_text(), answer_as_boolean(), answer_as_integer(), answer_as_json(), answer_as_list(), answer_as_named_list(), answer_as_regex_match(), answer_as_text(), answer_by_chain_of_thought(), answer_by_react(), answer_using_sql(), answer_using_tools(), prompt_wrap(), quit_if(), set_system_prompt()

Other answer_using_prompt_wraps: answer_using_sql(), answer_using_tools()

Examples

if (FALSE) { # \dontrun{
  # Prompt to value calculated with R
  avg_miles_per_gallon <- paste0(
    "Using my data,",
    " calculate the average miles per gallon (mpg) for cars with 6 cylinders."
  ) |>
    answer_as_integer() |>
    answer_using_r(
      pkgs_to_use = c("dplyr"),
      objects_to_use = list(mtcars = mtcars),
      evaluate_code = TRUE,
      output_as_tool = TRUE
    ) |>
    send_prompt()
  avg_miles_per_gallon

  # Prompt to linear model object in R
  model <- paste0(
    "Using my data, create a statistical model",
    " investigating the relationship between two variables."
  ) |>
    answer_using_r(
      objects_to_use = list(data = mtcars),
      evaluate_code = TRUE,
      return_mode = "object"
    ) |>
    prompt_wrap(
      validation_fn = function(x) {
        if (!inherits(x, "lm"))
          return(llm_feedback("The output should be a linear model object."))
        return(x)
      }
    ) |>
    send_prompt()
  summary(model)

  # Prompt to plot object in R
  plot <- paste0(
    "Create a scatter plot of miles per gallon (mpg) versus",
    " horsepower (hp) for the cars in my data.",
    " Use different colors to represent the number of cylinders (cyl).",
    " Be very creative and make the plot look nice but also a little crazy!"
  ) |>
    answer_using_r(
      pkgs_to_use = c("ggplot2"),
      objects_to_use = list(mtcars = mtcars),
      evaluate_code = TRUE,
      return_mode = "object"
    ) |>
    send_prompt()
  plot
} # }