Data Cleaning Agent • LLMAgentR

What This Agent Does

build_data_cleaning_agent() recommends and applies cleaning steps, runs code, and can explain what was done.

Workflow Diagram

Generate Mermaid PNGs

library(LLMAgentR)

my_llm_wrapper <- function(prompt, verbose = FALSE) "LLM response placeholder"

workflow <- build_data_cleaning_agent(
  model = my_llm_wrapper,
  output = "both",
  direction = "LR"
)

save_mermaid_png(
  x = workflow,
  file = "pkgdown/assets/data-cleaning-agent-workflow.png"
)

Step 1: Build the Agent

library(LLMAgentR)

my_llm_wrapper <- function(prompt, verbose = FALSE) "LLM response placeholder"

data_cleaner <- build_data_cleaning_agent(
  model = my_llm_wrapper,
  human_validation = FALSE,
  bypass_recommended_steps = FALSE,
  bypass_explain_code = FALSE,
  verbose = FALSE
)

Step 2: Run with State

initial_state <- list(
  data_raw = mtcars,
  user_instructions = "Clean missing values and standardize numeric columns.",
  max_retries = 3,
  retry_count = 0
)

final_state <- data_cleaner(initial_state)
str(final_state)

Notes for Beginners

The agent expects a state list, not just a data frame.
Keep human_validation = TRUE if you want manual checkpoints.
Inspect both cleaned data and explanation fields in output state.