OptimizeRcode.R

#' Optimize and Complete R Code
#'
#' This function takes a snippet of R code and optimizes it for performance and readability.
#' It uses a machine learning model to generate the optimized code.
#'
#' @title Optimize and Complete R Code
#' @description Optimizes and completes the R code from the selected code or clipboard.
#' @param Model A string specifying the machine learning model to use for code optimization. Default is "gpt-4-0613".
#' @param SelectedCode A boolean indicating whether to get the code from RStudio or the clipboard. Default is TRUE.
#' @param verbose A logical value indicating whether to print the result to the console, default is TRUE.
#' @param verbose_Reasons4change A boolean indicating whether to provide detailed reasons for the changes made. Default is FALSE
#' @param SlowTone A boolean indicating whether to print the output slowly. Default is FALSE.
#' @importFrom assertthat assert_that is.string noNA is.count is.flag
#' @importFrom rstudioapi isAvailable getActiveDocumentContext insertText
#' @importFrom clipr read_clip write_clip
#' @return A message indicating the completion of the optimization process.
#' @export OptimizeRcode
#' @author Satoshi Kume
#' @examples
#' \dontrun{
#' #Copy your R code then run the following function.
#' OptimizeRcode(SelectedCode = FALSE)
#' }

OptimizeRcode <- function(Model = "gpt-4-0613",
                          SelectedCode = TRUE,
                          verbose = TRUE,
                          verbose_Reasons4change = FALSE,
                          SlowTone = FALSE) {

  # Validate that RStudio API is available if SelectedCode is TRUE
  if(SelectedCode){
    assertthat::assert_that(rstudioapi::isAvailable())
    input <- rstudioapi::getActiveDocumentContext()$selection[[1]]$text
  } else {
    input <- paste0(clipr::read_clip(), collapse = " \n")
  }

  if(verbose){
    cat("\n", "OptimizeRcode: ", "\n")
    pb <- utils::txtProgressBar(min = 0, max = 3, style = 3)
  }

  # Validate input parameters
  assertthat::assert_that(
    assertthat::is.string(input),
    assertthat::noNA(input),
    assertthat::is.string(Model),
    assertthat::is.flag(SelectedCode),
    assertthat::is.flag(verbose_Reasons4change),
    assertthat::is.flag(SlowTone),
    Sys.getenv("OPENAI_API_KEY") != ""
  )

  # Initialize temperature for text generation
  temperature <- 1
  if(verbose){utils::setTxtProgressBar(pb, 1)}

  # Template for text generation
  template <- "
  You are an excellent assistant and a highly skilled genius R programmer.
  You should understand the input R code carefully, line by line.
  You should consider the use of vectorization and vector manipulation, the selection of appropriate data structures, and the readability and maintainability of the code.
  You must provide only the core R code as a deliverable in English, without code blocks and other explanations.
  Uncommented text and other R grammars that are not appropriate for the R file format of the R package with roxygen2 should be handled appropriately.
  "

  # Create the prompt
  template1 = "
  Please optimize the following R code input as R file format in English.:
  "

  template1s <- paste0(template1, input, sep=" ")

  # Initialize history for chat
  history <- list(list('role' = 'system', 'content' = template),
                  list('role' = 'user', 'content' = template1s))

  if(verbose){utils::setTxtProgressBar(pb, 2)}

  # Execute text generation
  res <- chat4R_history(history = history,
                        Model = Model,
                        temperature = temperature)

  if(verbose){
    utils::setTxtProgressBar(pb, 3)
    cat("\n")}

  # Output the optimized code
  if(SelectedCode){

    rstudioapi::insertText(text = as.character(res))

    if(verbose_Reasons4change){
      # Additional code for verbose reasons
      history[[3]] <- list('role' = 'assistant', 'content' = res)
      template2 = "Please describe in detail the changes you previously made to the R code without apology."
      history[[4]] <- list('role' = 'user', 'content' = template2)

      res1 <- chat4R_history(history = history,
                             Model = Model,
                             temperature = temperature)

      if(SlowTone) {
        d <- ifelse(20/nchar(res1) < 0.3, 20/nchar(res1), 0.3) * stats::runif(1, min = 0.95, max = 1.05)
        slow_print_v2(res1, delay = d)
      } else {
        d <- ifelse(10/nchar(res1) < 0.15, 10/nchar(res1), 0.15) * stats::runif(1, min = 0.95, max = 1.05)
        slow_print_v2(res1, delay = d)
      }
    }
  } else {
    return(clipr::write_clip(res))
  }
}