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feat: added 2nd function, visualization generating
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@@ -18,4 +18,6 @@ session-* | |
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| # R build ignore file | ||
| .Rbuildignore | ||
| .Rhistory | ||
| .Rhistory | ||
| .RData | ||
| TEST-CMD.R | ||
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| #' Generate a Visualization Recommendation and ggplot Code in RMarkdown | ||
| #' | ||
| #' This function generates a visualization recommendation and the corresponding ggplot code | ||
| #' for the given dataset using OpenAI's API. The generated R code with the ggplot is saved in a folder as an .Rmd file. | ||
| #' | ||
| #' @param data A data frame containing the dataset to analyze. | ||
| #' @param api_key Your OpenAI API key as a string. | ||
| #' @param output_name The name of the output file for the R code with ggplot. | ||
| #' @param additional_prompt Additional instructions for the OpenAI API. | ||
| #' @return The generated R code as a string. | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' data <- data.frame( | ||
| #' var1 = rnorm(100), | ||
| #' var2 = rnorm(100), | ||
| #' outcome = sample(c(0, 1), 100, replace = TRUE) | ||
| #' ) | ||
| #' api_key <- "your_openai_api_key" | ||
| #' openAIScientist_generate_visualization_rmd(data, api_key, "Visualization") | ||
| #' } | ||
| #' @importFrom httr POST add_headers content | ||
| #' @importFrom utils capture.output | ||
| #' @export | ||
| openAIScientist_generate_visualization_rmd <- function(data, api_key, output_name = "Visualization", additional_prompt = "") { | ||
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| cat("Generating data summary...\n") | ||
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| # Create a data summary | ||
| data_summary <- summary(data) | ||
| data_description <- paste(capture.output(data_summary), collapse = "\n") | ||
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| # Check if API key is provided | ||
| if (api_key == "") { | ||
| stop("API key not found. Please provide a valid OpenAI API key.") | ||
| } | ||
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| # Clean up any problematic characters in the data description | ||
| data_description <- gsub("[`*]", "", data_description) | ||
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| # Construct the prompt | ||
| prompt <- paste( | ||
| "You are provided with the following dataset summary: We are working in R \n\n", | ||
| data_description, | ||
| "\n\nYour tasks are enlisted below finish all of them one after another \n", | ||
| "- Explain what you are doing ", | ||
| "- Write your R code in Codeblocks with ```r ", | ||
| "- The data is created in the variable `dataset`. Reference it in your code. THIS IS VERY important. DO NOT OVERWRITE THE `dataset` variable (1:1).", | ||
| "- The data is created in the variable `dataset`. Reference it in your code. THIS IS VERY important. DO NOT OVERWRITE THE `dataset` variable (1:1).", | ||
| "- Analyze the data you and write a explenation about it ", | ||
| "- Now create plots with ggplot2 fitting to the analysiation you did before. Describe the plot and why you chose it.\n", | ||
| "- Structure your Response well with # Headers \n", | ||
| "- Write an if statement at the beginning to check if all the needed library is already installed and if not, install it.\n", | ||
| "- Always use the variable names, never use abbreviations.", | ||
| "- Try to ALWAYS cover all variables and correlations in a plot", | ||
| "- For every Plot write a Description and explenation on why this plot fits to the data", | ||
| "- Write an analysis of the data at the beginning", | ||
| "- Do not use grid.arrange", | ||
| "- Follow best practices while using colors for data visualization:\n", | ||
| " * Use Qualitative palettes for categorical data.\n", | ||
| " * Use Sequential palettes for numerical data with order.\n", | ||
| " * Use Diverging palettes for numerical data with a meaningful midpoint.\n", | ||
| " * Leverage the meaningfulness of color.\n", | ||
| " * Avoid unnecessary usage of color.\n", | ||
| " * Be consistent with color across charts.\n", | ||
| " * Try to not use bright neon colors\n", | ||
| "Think about using: scatter plots, line charts, box plots, heatmaps, bar charts, pie charts, histograms, area charts or barplots depending on the best usecase", | ||
| "Every attribute that can be plotted should be plotted", | ||
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| additional_prompt, | ||
| "The data is created in the variable `dataset`. Reference it in your code. THIS IS VERY important. DO NOT OVERWRITE THE `dataset` variable (1:1).", | ||
| "Reference the dataset like this:\n", | ||
| "data <- dataset" | ||
| ) | ||
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| cat("Sending request to OpenAI API (this might take a while)...\n") | ||
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| response <- POST( | ||
| url = "https://api.openai.com/v1/chat/completions", | ||
| add_headers(Authorization = paste("Bearer", api_key), 'Content-Type' = 'application/json'), | ||
| body = list( | ||
| model = "gpt-4o", | ||
| messages = list(list(role = "user", content = prompt)) | ||
| ), | ||
| encode = "json" | ||
| ) | ||
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| content <- content(response, "parsed") | ||
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| if (!is.null(content$choices)) { | ||
| r_code <- content$choices[[1]]$message$content | ||
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| # Replace ```r and ```R with ```{r} | ||
| rmd_code <- gsub("```[rR]", "```{r, message=FALSE}", r_code) | ||
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| # Ensure there is a blank space after each code block | ||
| rmd_code <- gsub("```\\{r, message=FALSE\\}\\n(.+?)\\n```", "```{r, message=FALSE}\\n\\1\\n```\n", rmd_code, perl = TRUE) | ||
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| # Create a folder for the output RMarkdown file | ||
| time_stamp <- format(Sys.time(), "%Y-%m-%d_%H-%M-%S") | ||
| folder_name <- paste0(output_name, "_", time_stamp) | ||
| dir.create(folder_name) | ||
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| file_path <- file.path(folder_name, paste0(output_name, ".Rmd")) | ||
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| # Write the header and dataset to the RMarkdown file | ||
| rmd_header <- c( | ||
| "---", | ||
| "output:", | ||
| " html_document:", | ||
| " code_folding: hide", | ||
| "---", | ||
| "", | ||
| "# Dataset", | ||
| "```{r, message=FALSE}", | ||
| "dataset <- ", deparse(data), | ||
| "```", | ||
| "" | ||
| ) | ||
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| # Save the RMarkdown code | ||
| writeLines(c(rmd_header, rmd_code), file_path) | ||
| cat(paste("RMarkdown file for visualization saved in:", file_path, "\n")) | ||
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| # Append additional text at the end of the file | ||
| additional_text <- "\n\n\n---\n\n\n\n\nThis analysis was created with [openAIScientist](https://github.com/noluyorAbi/openaAIScientist).\n\n Made with \u2665 by [noluyorAbi](https://github.com/noluyorAbi) for FortStaSoft @ LMU Munich" | ||
| cat(additional_text, file = file_path, append = TRUE) | ||
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| # Token usage and cost calculation | ||
| usage <- content$usage | ||
| if (!is.null(usage)) { | ||
| total_tokens <- usage$total_tokens | ||
| total_input_tokens <- usage$prompt_tokens | ||
| total_output_tokens <- usage$completion_tokens | ||
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| price_per_input_token <- 5.00 / 1e6 # $5 per 1M input tokens | ||
| price_per_output_token <- 15.00 / 1e6 # $15 per 1M output tokens | ||
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| total_cost <- (total_input_tokens * price_per_input_token) + (total_output_tokens * price_per_output_token) | ||
| cat("Initial call - Total tokens used:", total_tokens, "\n") | ||
| cat("Initial call - Total cost (USD):", total_cost, "\n") | ||
| } else { | ||
| cat("Initial call - Token usage information not available.\n") | ||
| } | ||
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| return(rmd_code) | ||
| } else { | ||
| cat("Failed to generate visualization. No content returned from OpenAI API.\n") | ||
| return(NULL) | ||
| } | ||
| } |
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8 changes: 4 additions & 4 deletions
8
man/generate_scientific_analysis.Rd → ...Scientist_generate_scientific_analysis.Rd
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