% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DataAudit-package.R
\docType{package}
\name{DataAudit-package}
\alias{DataAudit}
\alias{DataAudit-package}
\title{DataAudit: Comprehensive Data Quality Auditing and Validation}
\description{
DataAudit provides tools for systematic assessment,
validation, and reporting of data quality in R.
}
\details{
The package supports both general-purpose data auditing
and user-defined validation rules.
}
\section{General data auditing}{


The main function \code{\link{audit_data}} performs a
general assessment of a data frame, including detection
of missing values, blank values, infinite values,
duplicated observations, constant variables, and
near-zero variance variables.
}

\section{Validation rules}{


Reusable validation rules can be defined using
\code{\link{audit_rules}} and evaluated using
\code{\link{apply_audit_rules}}.

Rules can be incorporated directly into
\code{\link{audit_data}} to combine automatic auditing
with study-specific validation requirements.
}

\section{Individual checks}{


DataAudit provides focused functions for checking
ranges, categories, data types, patterns, dates,
identifiers, lengths, character case, whitespace,
sequences, dependencies, uniqueness, duplicates,
outliers, and cross-variable consistency.
}

\section{Audit reports}{


\code{\link{audit_data}} returns a structured
\code{DataAuditReport} object containing dataset-level,
variable-level, duplicate-row, and optional rule-based
diagnostics.

Print and summary methods are provided for convenient
inspection of audit results.
}

\section{Data-quality scoring}{


\code{\link{audit_score}} calculates a standardized
data-quality score from a \code{DataAuditReport}.

Scores range from 0 to 100, with higher values
indicating fewer detected data-quality issues relative
to dataset size.
}

\section{Typical workflow}{


A typical workflow consists of:

\enumerate{
\item Inspect the dataset using \code{\link{audit_data}}.
\item Define study-specific rules using
\code{\link{audit_rules}}.
\item Integrate the rules into the audit.
\item Inspect the resulting \code{DataAuditReport}.
\item Calculate an overall score using
\code{\link{audit_score}} when appropriate.
}
}

\seealso{
Useful links:
\itemize{
  \item \url{https://github.com/vinodhpmd/DataAudit}
  \item Report bugs at \url{https://github.com/vinodhpmd/DataAudit/issues}
}

}
\author{
\strong{Maintainer}: Vinodh Kumar Obli Rajendran \email{vinodhkumar.rajendran@gmail.com} (\href{https://orcid.org/0000-0002-7232-4122}{ORCID})

Authors:
\itemize{
  \item Vinodh Kumar Obli Rajendran \email{vinodhkumar.rajendran@gmail.com} (\href{https://orcid.org/0000-0002-7232-4122}{ORCID})
  \item Keerthi Aaradhana \email{vkeerthi1817@gmail.com}
}

}
\keyword{internal}
