build_data_cleaning_agent: Build a Data Cleaning Agent

In LLMAgentR: Language Model Agents in R for AI Workflows and Research

Build a Data Cleaning Agent

Description

Constructs a multi-step agent workflow to recommend, generate, fix, execute, and explain robust R code for data cleaning tasks using LLMs and user-defined data.

Arguments

model	A function that accepts a prompt and returns a text response (e.g., OpenAI, Claude).
data_raw	A raw data.frame (or list convertible to data.frame) to be cleaned.
human_validation	Logical; whether to include a manual review step.
bypass_recommended_steps	Logical; whether to skip LLM-based cleaning step suggestions.
bypass_explain_code	Logical; whether to skip explanation of the generated code.
verbose	Logical; whether to print progress messages (default: TRUE)

Value

A compiled graph-based cleaning agent function that accepts and mutates a state list.

Examples

## Not run: 
# 1) Load the data
data <- read.csv("tests/testthat/test-data/churn_data.csv")

# 2) Create the agent
data_cleaner_agent <- build_data_cleaning_agent(
  model = my_llm_wrapper,
  human_validation = FALSE,
  bypass_recommended_steps = FALSE,
  bypass_explain_code = FALSE,
  verbose = FALSE
)

# 3) Define the initial state
initial_state <- list(
  data_raw = data,
  user_instructions = "Don't remove outliers when cleaning the data.",
  max_retries = 3,
  retry_count = 0
)

# 4) Run the agent
final_state <- data_cleaner_agent(initial_state)

## End(Not run)

LLMAgentR documentation built on June 8, 2025, 10:02 a.m.