AI Tools for R

Tools and services that integrate with R for enhanced AI capabilities

Overview

This page highlights external AI tools and services that can be leveraged in R workflows.

Featured Tools

OpenAI API

Access powerful language models like GPT-4 directly from R.

# Using httr2 package to call OpenAI API
library(httr2)
library(jsonlite)

openai_completion <- function(prompt, model = "gpt-4") {
  request <- request("https://api.openai.com/v1/chat/completions") %>%
    req_headers(
      "Content-Type" = "application/json",
      "Authorization" = paste("Bearer", Sys.getenv("OPENAI_API_KEY"))
    ) %>%
    req_body_json(list(
      model = model,
      messages = list(list(role = "user", content = prompt)),
      temperature = 0.7
    )) %>%
    req_perform()

  response <- resp_body_json(request)
  return(response$choices[[1]]$message$content)
}

HuggingFace Transformers

Use state-of-the-art NLP models in your R workflow.

# Using reticulate to access HuggingFace in R
library(reticulate)

# Initialize transformers
transformers <- import("transformers")

# Load a pre-trained model
model_name <- "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer <- transformers$AutoTokenizer$from_pretrained(model_name)
model <- transformers$AutoModelForSequenceClassification$from_pretrained(model_name)

# Function to analyze sentiment
analyze_sentiment <- function(text) {
  inputs <- tokenizer(text, return_tensors = "pt")
  outputs <- model(**inputs)
  predictions <- outputs$logits$argmax(dim = 2L)
  return(as.integer(predictions[1]) - 1) # 0 for negative, 1 for positive
}

RStudio AI Assistant

RStudio’s integrated AI assistance provides code suggestions, explanations, and more.

Coming Soon

• Integration tutorials
• Performance benchmarks
• Cost considerations