## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  echo = TRUE,
  eval = FALSE,
  collapse = TRUE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
# # Option 1: Set in your R session
# Sys.setenv(OPENAI_API_KEY = "sk-...")
# 
# # Option 2: Add to your ~/.Renviron file (persists across sessions)
# # OPENAI_API_KEY=sk-...
# 
# # Verify the key is set
# nzchar(Sys.getenv("OPENAI_API_KEY"))

## -----------------------------------------------------------------------------
# library(sdcMicro)
# data(testdata)
# 
# # Step 1: AI-assisted variable classification
# sdc <- AI_createSdcObj(dat = testdata, policy = "open")
# 
# # Step 2: AI-assisted anonymization
# sdc <- AI_applyAnonymization(sdc, k = 3)
# 
# # Step 3: Extract the anonymized data
# anon_data <- extractManipData(sdc)
# head(anon_data)

## -----------------------------------------------------------------------------
# # OpenAI (default)
# query_llm(prompt, provider = "openai")
# 
# # Anthropic (native Messages API)
# query_llm(prompt, provider = "anthropic")
# 
# # Any OpenAI-compatible endpoint (Ollama, Azure, vLLM, etc.)
# query_llm(prompt, provider = "custom",
#           base_url = "http://localhost:11434/v1",
#           model = "llama3")

## -----------------------------------------------------------------------------
# sdc <- AI_createSdcObj(dat = testdata, policy = "open")

## -----------------------------------------------------------------------------
# library(sdcMicro)
# data(testdata)
# sdc <- AI_createSdcObj(dat = testdata, policy = "open")

## -----------------------------------------------------------------------------
# # Reject and modify
# roles <- AI_createSdcObj(dat = testdata, policy = "open")
# # User presses 'n' — roles is returned as a list
# roles$keyVars <- c(roles$keyVars, "age")  # Add age as key variable
# sdc <- createSdcObj(testdata,
#                     keyVars = roles$keyVars,
#                     numVars = roles$numVars,
#                     weightVar = roles$weightVar,
#                     hhId = roles$hhId)

## -----------------------------------------------------------------------------
# sdc <- AI_applyAnonymization(sdc, k = 3)

## -----------------------------------------------------------------------------
# library(sdcMicro)
# data(testdata)
# 
# # Step 1: Create sdcObj with AI-assisted variable classification
# sdc <- AI_createSdcObj(dat = testdata, policy = "open")
# 
# # Step 2: Apply AI-assisted anonymization
# sdc <- AI_applyAnonymization(sdc, k = 3, n_strategies = 3)

## -----------------------------------------------------------------------------
# # Extract anonymized data
# anon_data <- extractManipData(sdc)
# 
# # Review risk and utility
# print(sdc, "risk")

## -----------------------------------------------------------------------------
# # Minimize suppressions (prefer recoding over suppression)
# sdc <- AI_applyAnonymization(sdc, k = 3,
#   weights = c(0.6, 0.2, 0.2))
# 
# # Preserve categorical diversity (for cross-tabulations)
# sdc <- AI_applyAnonymization(sdc, k = 3,
#   weights = c(0.2, 0.6, 0.2))

## -----------------------------------------------------------------------------
# # OpenAI (default) — best strategy quality
# sdc <- AI_applyAnonymization(sdc, k = 3)
# 
# # Anthropic Claude — comparable quality, different provider
# sdc <- AI_applyAnonymization(sdc, k = 3, provider = "anthropic")
# 
# # Local Ollama instance — maximum privacy, no external communication
# sdc <- AI_applyAnonymization(sdc, k = 3,
#   provider = "custom",
#   base_url = "http://localhost:11434/v1",
#   model = "llama3")