Type:	Package
Title:	'LLM'-Assisted Data Cleaning with Multi-Provider Support
Version:	0.1.0
Date:	2026-04-15
Author:	Sadikul Islam [aut, cre]
Maintainer:	Sadikul Islam <sadikul.islamiasri@gmail.com>
Description:	Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models (LLMs) through a unified, provider-agnostic interface. Supported providers include 'OpenAI' ('GPT-4o', 'GPT-4o-mini'), 'Anthropic' ('Claude'), 'Google' ('Gemini'), 'Groq' (free-tier 'LLaMA' and 'Mixtral'), and local 'Ollama' models. The package identifies issues that rule-based tools cannot detect: abbreviation variants, typographic errors, case inconsistencies, and malformed values. Results are returned as tidy data frames with column, row index, detected value, issue type, suggested fix, and confidence score. An offline fallback using statistical and fuzzy-matching methods is provided for use without any API key. Interactive fix application with human review is supported via 'apply_fixes()'. Methods follow de Jonge and van der Loo (2013) https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf and Chaudhuri et al. (2003) <doi:10.1145/872757.872796>.
License:	GPL-3
Depends:	R (≥ 4.1.0)
Imports:	stats, utils, dplyr (≥ 1.0.0), rlang (≥ 1.0.0)
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), httr2 (≥ 1.0.0), jsonlite (≥ 1.8.0)
VignetteBuilder:	knitr
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-04-22 04:17:03 UTC; acer
Repository:	CRAN
Date/Publication:	2026-04-22 13:10:09 UTC

llmclean: LLM-Assisted Data Cleaning with Multi-Provider Support

Description

Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models through a unified, provider-agnostic interface.

The problem

Traditional data cleaning tools such as janitor, validate, and pointblank are rule-based: they catch type mismatches, range violations, and schema errors. They cannot understand *meaning*.

An LLM, by contrast, understands that "NYC", "New York", "new york", and "New Yrok" all refer to the same city. It recognises "jane@gmail.com" as a malformed email, and "actve" as a typo for "active". These semantic issues are the hardest to catch and the most damaging to downstream analyses.

Supported providers

OpenAI

GPT-4o, GPT-4o-mini (API key required). The most widely used commercial LLM with mature tooling.

Anthropic

Claude Sonnet, Claude Haiku (API key required). Large context window; strong instruction following.

Google

Gemini 2.0 Flash, Gemini 1.5 Pro (API key required). Free tier available.

Groq

LLaMA 3.1, Mixtral (free-tier API key). Fastest inference; suitable for large data frames.

Ollama

Any local model (llama3, mistral, phi3). Fully offline; no API key; complete data privacy.

Workflow

1. Configure provider: set_llm_provider()

2. Detect semantic issues: detect_issues()

3. Get suggested fixes: suggest_fixes()

4. Apply fixes interactively: apply_fixes()

5. Summary report: llmclean_report()

Offline fallback

offline_detect() provides statistical and fuzzy-matching detection without any LLM call, using Levenshtein string distances, frequency analysis, and regex pattern matching. Use this when no API key is available or for data privacy reasons.

References

de Jonge, E. and van der Loo, M. (2013). An introduction to data cleaning with R. Statistics Netherlands Discussion Paper, The Hague. https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf

Chaudhuri, S., Ganjam, K., Ganti, V. and Motwani, R. (2003). Robust and efficient fuzzy match for online data cleaning. Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, 313–324. doi:10.1145/872757.872796

van der Loo, M.P.J. and de Jonge, E. (2018). Statistical Data Cleaning with Applications in R. John Wiley & Sons, Chichester. doi:10.1002/9781118897126

OpenAI (2024). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774. https://arxiv.org/abs/2303.08774

Author(s)

Maintainer: Sadikul Islam sadikul.islamiasri@gmail.com (ORCID)

Apply Suggested Fixes to a Data Frame

Description

Applies the suggestions from detect_issues() or suggest_fixes() to the original data frame, either automatically or with interactive human review of each fix.

Usage

apply_fixes(df, issues, confirm = TRUE, min_confidence = 0.7, dry_run = FALSE)

Arguments

Details

The function creates a copy of df, then iterates over each row of issues in order of descending confidence. For each issue:

• If confirm = FALSE and confidence >= min_confidence and suggestion != "REVIEW": the value at df[row_index, column] is replaced with suggestion.

• If confirm = TRUE: the user is presented with the current value, the suggested fix, and the context, then prompted to accept (y), skip (n), or type a custom replacement.

A "_applied" attribute is attached to the returned data frame recording which fixes were applied, for use by llmclean_report().

Value

A data.frame with the same structure as df but with accepted fixes applied. The original df is not modified.

References

van der Loo, M.P.J. and de Jonge, E. (2018). Statistical Data Cleaning with Applications in R. John Wiley & Sons. doi:10.1002/9781118897126

See Also

detect_issues, suggest_fixes, llmclean_report

Examples

set_llm_provider("offline", verbose = FALSE)
data(messy_employees)
issues <- offline_detect(messy_employees)

# Non-interactive: apply high-confidence fixes automatically
df_clean <- apply_fixes(messy_employees, issues,
                         confirm = FALSE, min_confidence = 0.85)
head(df_clean)

# Dry run: see what would change
plan <- apply_fixes(messy_employees, issues, dry_run = TRUE)
plan

Detect Semantic Inconsistencies in a Data Frame Using an LLM

Description

Sends a compact representation of the data frame to the configured LLM provider and requests detection of semantic inconsistencies: typographic errors, abbreviation variants, case inconsistencies, malformed values, cross-field contradictions, and implausible entries. Returns a tidy tibble of detected issues with location, type, and confidence.

Usage

detect_issues(
  df,
  columns = NULL,
  sample_n = NULL,
  issue_types = c("typo", "case", "abbreviation", "format", "outlier", "contradiction",
    "duplicate"),
  context = NULL,
  max_tokens = 2000L,
  verbose = TRUE
)

Arguments

Details

The function constructs a structured prompt containing:

1. Column names and inferred types.

2. The first sample_n rows in CSV-like format.

3. Instructions requesting JSON output with one object per issue.

The LLM is instructed to return only a JSON array with fields: column, row_index (1-based), value, issue_type, explanation, suggestion, and confidence (0–1).

If the LLM response cannot be parsed as valid JSON, the raw response is returned with a warning and offline_detect() is called as fallback.

Token usage note: Sending 100 rows of a 10-column data frame uses approximately 1,500–3,000 tokens. At gpt-4o-mini pricing (~$0.15/M input tokens as of 2025), this costs less than US$0.001.

Value

A tibble with one row per detected issue and columns:

column

Column name where the issue was found.

row_index

Row number (1-based).

value

The problematic value as a character string.

issue_type

One of the issue_types categories.

explanation

Human-readable explanation of the problem.

suggestion

Suggested corrected value.

confidence

Numeric 0–1. LLM-assigned confidence.

provider

The LLM provider used.

model

The specific model used.

Returns a zero-row tibble with the same columns if no issues are found.

References

de Jonge, E. and van der Loo, M. (2013). An introduction to data cleaning with R. Statistics Netherlands Discussion Paper. https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf

Chaudhuri, S., Ganjam, K., Ganti, V. and Motwani, R. (2003). Robust and efficient fuzzy match for online data cleaning. Proc. 2003 ACM SIGMOD, 313–324. doi:10.1145/872757.872796

See Also

set_llm_provider, suggest_fixes, apply_fixes, offline_detect

Examples

# Offline detection (no API key needed)
set_llm_provider("offline", verbose = FALSE)

data(messy_employees)
issues <- detect_issues(messy_employees)
issues

# With LLM (requires valid API key)
## Not run: 
set_llm_provider("openai", model = "gpt-4o-mini")
issues <- detect_issues(messy_employees,
                         context = "Employee records. Status: active/inactive.")
issues

# Groq free tier
set_llm_provider("groq", model = "llama-3.1-8b-instant")
issues <- detect_issues(messy_employees, sample_n = 50L)

## End(Not run)

Get Current LLM Provider Configuration

Description

Returns the current LLM provider, model, and base URL configured by set_llm_provider().

Usage

get_llm_provider(show_key = FALSE)

Arguments

Value

A named list with elements provider, model, base_url, and optionally api_key.

See Also

set_llm_provider

Examples

set_llm_provider("offline", verbose = FALSE)
get_llm_provider()

Generate a Summary Report of LLM-Assisted Data Cleaning

Description

Produces a tidy summary of detected issues and applied fixes, grouped by column and issue type. Suitable for inclusion in a data-quality audit log or reproducible report. Optionally prints a formatted table to the console.

Usage

llmclean_report(df_original, df_cleaned, issues, print = TRUE)

Arguments

Details

The report computes:

• Number of issues detected per column and issue type.

• Number of fixes applied vs skipped.

• Cell-level change summary (original value -> corrected value).

• Provider and model used.

• Data dimensions before and after cleaning.

The applied-fix provenance is read from the "_applied" attribute set by apply_fixes(). If the attribute is absent (e.g. when df_cleaned was not produced by apply_fixes()), the report uses the full issues tibble.

Value

Invisibly returns a named list with three elements: summary (tibble: column / issue_type / n_detected / n_applied), changes (tibble: column / row_index / original / corrected), metadata (list: provider, model, n_total, n_applied, n_skipped).

References

de Jonge, E. and van der Loo, M. (2013). An introduction to data cleaning with R. Statistics Netherlands Discussion Paper. https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf

See Also

detect_issues, apply_fixes

Examples

set_llm_provider("offline", verbose = FALSE)
data(messy_employees)
issues   <- offline_detect(messy_employees)
df_clean <- apply_fixes(messy_employees, issues,
                         confirm = FALSE, min_confidence = 0.85)
rpt <- llmclean_report(messy_employees, df_clean, issues)
rpt$summary

Hypothetical Messy Employee Records Dataset

Description

A hypothetical data frame of 20 employee records containing deliberate data quality issues across all common inconsistency types: capitalisation variants, typos, duplicate entries, malformed email addresses, out-of-range numeric values, and cross-field redundancies. Designed to illustrate the full range of issues detectable by detect_issues() and offline_detect().

Usage

messy_employees

Format

A data.frame with 20 rows and 8 variables:

emp_id

Integer. Unique employee identifier (1–20).

name

Character. Employee full name. Contains mixed-case inconsistencies (e.g. "Alice Johnson" vs "alice johnson" vs "ALICE JOHNSON").

department

Character. Department name. Contains case variants ("Finance" vs "finance"), abbreviations ("IT" vs "I.T."), synonym variants ("HR" vs "Human Resources"), and a typo ("Finanace").

email

Character. Email address. Contains malformed values: double @, missing TLD, and leading dot.

age

Integer. Age in years. Contains two impossible values: -5 and 150.

salary

Numeric. Annual salary in USD. Contains a data entry error (999999) as an implausible outlier.

status

Character. Employment status (active / inactive). Contains case variants and two typos ("actve").

hire_date

Character. Hire date. Contains a mixed-format date ("2015/07/22" vs "2018-03-15" ISO-8601 format).

Details

All data are entirely hypothetical and generated for illustrative purposes. The inconsistency types are based on the taxonomy in de Jonge and van der Loo (2013) and reflect common real-world data entry errors documented in Muller and Freytag (2003).

Source

Hypothetical data generated for illustration. See data-raw/generate_datasets.R.

References

de Jonge, E. and van der Loo, M. (2013). An introduction to data cleaning with R. Statistics Netherlands Discussion Paper. https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf

Muller, H. and Freytag, J.C. (2003). Problems, methods, and challenges in comprehensive data cleansing. Technical Report, Humboldt University Berlin, HUB-IB-164.

See Also

messy_survey, detect_issues, offline_detect

Examples

data(messy_employees)
str(messy_employees)

# Quick overview of known issues
table(messy_employees$status)      # case inconsistency
table(messy_employees$department)  # variant forms
messy_employees$age[messy_employees$age < 0 | messy_employees$age > 100]

Hypothetical Messy Survey Response Dataset

Description

A hypothetical survey data frame of 15 respondents from 5 countries containing systematic data quality issues typical of free-text survey responses: country name variants ("USA" vs "United States"), satisfaction rating inconsistencies, spelling errors, and numeric outliers.

Usage

messy_survey

Format

A data.frame with 15 rows and 5 variables:

respondent_id

Integer. Unique respondent identifier (1–15).

country

Character. Respondent country. Contains abbreviations ("USA", "UK"), synonyms ("Deutschland" for "Germany"), case variants, and a typo ("Japn").

satisfaction

Character. Satisfaction rating (5-point Likert scale). Contains case inconsistencies and typos ("Satisified", "Nutral", "Very Dissatified").

age_group

Character. Age group bracket. Clean column for comparison.

income_usd

Numeric. Reported annual income in USD. Contains a negative value (-500) and an implausible outlier (999999).

Details

All data are entirely hypothetical. Survey inconsistency patterns are inspired by common free-text standardisation challenges described in Chaudhuri et al. (2003).

Source

Hypothetical data generated for illustration. See data-raw/generate_datasets.R.

References

Chaudhuri, S., Ganjam, K., Ganti, V. and Motwani, R. (2003). Robust and efficient fuzzy match for online data cleaning. Proc. 2003 ACM SIGMOD, 313–324. doi:10.1145/872757.872796

See Also

messy_employees, detect_issues

Examples

data(messy_survey)
str(messy_survey)
table(messy_survey$country)       # variant forms
table(messy_survey$satisfaction)  # case + typos

Offline Detection of Data Inconsistencies Without an LLM

Description

Detects data quality issues using statistical and fuzzy-matching methods, requiring no LLM API call or internet connection. Uses Levenshtein string distances for near-duplicate category detection, frequency analysis for rare/singleton values, regex patterns for format validation, and IQR-based outlier detection for numeric variables.

Usage

offline_detect(
  df,
  columns = NULL,
  issue_types = c("typo", "case", "abbreviation", "format", "outlier", "duplicate"),
  max_edit_distance = 2L,
  min_freq = 2L,
  outlier_iqr_mult = 3
)

Arguments

Details

Methods used:

Typo / near-duplicate detection

Levenshtein edit distance (Levenshtein, 1966) between all pairs of unique values in each column. Pairs within max_edit_distance edits but with different capitalisation-normalised forms are flagged as potential typos. Implemented via utils::adist().

Case inconsistency

Detects columns where the same word appears in multiple capitalisation forms (e.g. "active", "Active", "ACTIVE").

Format validation

Regex patterns for email addresses, phone numbers (international), URLs, and ISO dates. Any value not matching the majority pattern is flagged.

Numeric outliers

Modified Tukey outer fence: flags values beyond Q1 - k*IQR or Q3 + k*IQR where k = outlier_iqr_mult.

Singleton detection

Values appearing only once in a column with fewer than 10 unique values are flagged (likely erroneous entries).

The offline detector cannot catch semantic issues (e.g. knowing that "NYC" and "New York" are the same city) — that requires an LLM. Use detect_issues() with a configured LLM provider for full semantic detection.

Value

A tibble with the same structure as detect_issues(): columns column, row_index, value, issue_type, explanation, suggestion, confidence. The provider and model columns are set to "offline" and "statistical" respectively.

References

Levenshtein, V.I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10(8), 707–710.

Tukey, J.W. (1977). Exploratory Data Analysis. Addison-Wesley, Reading, MA. ISBN: 978-0-201-07616-5.

Chaudhuri, S., et al. (2003). Robust and efficient fuzzy match for online data cleaning. Proc. 2003 ACM SIGMOD, 313–324. doi:10.1145/872757.872796

See Also

detect_issues, set_llm_provider

Examples

data(messy_employees)
issues <- offline_detect(messy_employees)
issues

# Only look for case and format issues
offline_detect(messy_employees, issue_types = c("case", "format"))

Configure the LLM Provider for Data Cleaning

Description

Sets the LLM provider, API key, and model to use for all subsequent detect_issues() and suggest_fixes() calls. Configuration is stored in a package-level environment and persists for the R session. Supports OpenAI, Anthropic (Claude), Google (Gemini), Groq, and local Ollama models.

Usage

set_llm_provider(
  provider = "offline",
  api_key = NULL,
  model = NULL,
  base_url = NULL,
  verbose = TRUE
)

Arguments

Details

Getting API keys (all free tiers available):

OpenAI

https://platform.openai.com/api-keys

Anthropic

https://console.anthropic.com/

Google Gemini

https://aistudio.google.com/app/apikey

Groq (free)

https://console.groq.com/keys

Ollama (local)

https://ollama.com/ — run ollama pull llama3

Best practice: Store API keys in environment variables rather than in scripts. Add to your .Renviron file:

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GROQ_API_KEY=gsk_...
GOOGLE_API_KEY=AIza...

Value

Invisibly returns a list with the configured provider settings.

References

OpenAI (2024). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774. https://arxiv.org/abs/2303.08774

Anthropic (2024). Claude Model Overview. https://platform.claude.com/docs/en/docs/about-claude/models/overview

See Also

get_llm_provider, detect_issues

Examples

# Use offline mode (no API key needed)
set_llm_provider("offline")

# Configure OpenAI (reads key from env var OPENAI_API_KEY)
## Not run: 
set_llm_provider("openai", model = "gpt-4o-mini")

# Configure Anthropic explicitly
set_llm_provider("anthropic",
                 api_key = Sys.getenv("ANTHROPIC_API_KEY"),
                 model   = "claude-haiku-4-5-20251001")

# Configure free Groq
set_llm_provider("groq", model = "llama-3.1-8b-instant")

# Local Ollama (no key needed)
set_llm_provider("ollama", model = "llama3")

## End(Not run)

Request Enriched Fix Suggestions for Detected Issues

Description

Takes the issues tibble from detect_issues() or offline_detect() and optionally sends it back to the LLM with the full data context to obtain higher-quality, rank-ordered suggestions for each issue. Useful when the initial detection pass returned suggestion = "REVIEW" or low-confidence suggestions.

Usage

suggest_fixes(
  df,
  issues,
  n_alternatives = 2L,
  filter_confidence = 0.8,
  verbose = TRUE
)

Arguments

Details

If the current provider is "offline", this function returns the input issues tibble unchanged with a message, since no LLM is available to enrich suggestions.

The function sends each low-confidence issue to the LLM along with the surrounding rows for context, and requests ranked alternatives. Results are merged back into the issues tibble, replacing the original suggestion with the top-ranked alternative and adding alternatives as a comma-separated string column.

Value

The input issues tibble with two additional columns: alternatives (comma-separated list of alternative suggestions) and confidence_revised (updated confidence after re-querying).

References

de Jonge, E. and van der Loo, M. (2013). An introduction to data cleaning with R. Statistics Netherlands Discussion Paper. https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf

See Also

detect_issues, apply_fixes

Examples

set_llm_provider("offline", verbose = FALSE)
data(messy_employees)
issues <- offline_detect(messy_employees)

# Offline: suggest_fixes returns issues unchanged
enriched <- suggest_fixes(messy_employees, issues)
enriched

## Not run: 
set_llm_provider("openai", model = "gpt-4o-mini")
issues   <- detect_issues(messy_employees)
enriched <- suggest_fixes(messy_employees, issues, n_alternatives = 3L)

## End(Not run)