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Getting Started with DataAudit

Vinodh Kumar Obli Rajendran and Keerthi Aaradhana

2026-07-28

Introduction

Data quality is an important prerequisite for reliable statistical analysis, visualization, modelling, and interpretation.

DataAudit provides a structured framework for identifying common data-quality problems in R. The package combines general data auditing, individual validation functions, reusable validation rules, structured audit reports, and data-quality scoring.

A typical workflow is:

inspect the dataset;
perform a general audit;
conduct focused checks when required;
define study-specific validation rules;
integrate the rules into the audit;
inspect the resulting report; and
calculate an overall data-quality score.

Load DataAudit

library(DataAudit)

Example dataset

Consider a small dataset containing several intentional data-quality problems.

dat <- data.frame(
  ID = c(
    "A001",
    "A002",
    "A002",
    "BAD",
    "A005"
  ),
  Species = c(
    "Dog",
    "Horse",
    "",
    "Cat",
    "Dog"
  ),
  Age = c(
    5,
    40,
    -2,
    10,
    NA
  ),
  stringsAsFactors = FALSE
)

dat
#>     ID Species Age
#> 1 A001     Dog   5
#> 2 A002   Horse  40
#> 3 A002          -2
#> 4  BAD     Cat  10
#> 5 A005     Dog  NA

The dataset contains examples of:

a duplicated identifier;
a value outside the intended species categories;
a blank value;
ages outside an intended range;
a missing age; and
an identifier that does not follow the intended pattern.

General data audit

The primary high-level function is audit_data().

report <- audit_data(
  dat,
  include_nzv = FALSE
)

report
#> DataAudit Report
#> ================
#> 
#> Rows:                  5
#> Variables:             3
#> Variables with issues: 2
#> Duplicated rows:       0
#> Total issues:          2
#> Rule violations:       0
#> Overall issues:        2
#> 
#> Variables with issues
#> ---------------------
#>  Variable     Class Missing MissingPercent Blank Infinite Unique Constant
#>   Species character       0              0     1        0      4    FALSE
#>       Age   numeric       1             20     0        0      4    FALSE
#>  NearZeroVariance Issue
#>             FALSE  TRUE
#>             FALSE  TRUE
#> 
#> No duplicated rows detected.

The resulting object inherits from the DataAuditReport class.

class(report)
#> [1] "DataAuditReport" "list"

The report contains several components.

names(report)
#> [1] "overview"   "variables"  "duplicates" "summary"    "rules"

These provide information about:

the overall dataset;
variable-level issues;
duplicated rows;
overall issue counts; and
custom validation-rule results when rules are supplied.

Dataset overview

The overview component provides high-level audit statistics.

report$overview
#>                         Metric Value
#> 1                         Rows     5
#> 2                    Variables     3
#> 3               Missing values     1
#> 4                 Blank values     1
#> 5              Infinite values     0
#> 6              Duplicated rows     0
#> 7           Constant variables     0
#> 8 Near-zero variance variables     0
#> 9        Variables with issues     2

Variable-level diagnostics

Variable-level diagnostics are stored in the variables component.

report$variables
#>         Variable     Class Missing MissingPercent Blank Infinite Unique
#> ID            ID character       0              0     0        0      4
#> Species  Species character       0              0     1        0      4
#> Age          Age   numeric       1             20     0        0      4
#>         Constant NearZeroVariance Issue
#> ID         FALSE            FALSE FALSE
#> Species    FALSE            FALSE  TRUE
#> Age        FALSE            FALSE  TRUE

This provides information such as missing values, blank values, infinite values, numbers of unique values, constant variables, near-zero variance variables, and whether a variable contains a detected issue.

Duplicate observations

Duplicated observations can be inspected through:

report$duplicates
#>   Row Duplicate
#> 1   1     FALSE
#> 2   2     FALSE
#> 3   3     FALSE
#> 4   4     FALSE
#> 5   5     FALSE

This checks duplicated rows. Identifier uniqueness can be assessed separately using unique_check() or through validation rules.

Individual checks

DataAudit functions can also be used independently.

Missing values

missing_check(dat)
#>   Variable Total Missing Complete MissingPercent CompletePercent
#> 1       ID     5       0        5              0             100
#> 2  Species     5       0        5              0             100
#> 3      Age     5       1        4             20              80

Blank values

blank_check(dat)
#>         Variable TextVariable Missing Blank BlankPercent HasBlank
#> ID            ID         TRUE       0     0            0    FALSE
#> Species  Species         TRUE       0     1           20     TRUE
#> Age          Age        FALSE       1     0            0    FALSE

Range validation

Suppose valid ages are expected to range from 0 to 30.

range_check(
  dat,
  variable = "Age",
  min = 0,
  max = 30
)
#>   Row Value BelowMinimum AboveMaximum OutOfRange
#> 1   1     5        FALSE        FALSE      FALSE
#> 2   2    40        FALSE         TRUE       TRUE
#> 3   3    -2         TRUE        FALSE       TRUE
#> 4   4    10        FALSE        FALSE      FALSE
#> 5   5    NA        FALSE        FALSE      FALSE

Category validation

Suppose only "Dog" and "Cat" are accepted categories.

category_check(
  dat,
  variable = "Species",
  allowed = c(
    "Dog",
    "Cat"
  )
)
#>   Row Value Missing ValidCategory InvalidCategory
#> 1   1   Dog   FALSE          TRUE           FALSE
#> 2   2 Horse   FALSE         FALSE            TRUE
#> 3   3         FALSE         FALSE            TRUE
#> 4   4   Cat   FALSE          TRUE           FALSE
#> 5   5   Dog   FALSE          TRUE           FALSE

Identifier uniqueness

unique_check(
  dat,
  variable = "ID"
)
#>   Row Value Missing Duplicate ValidUnique
#> 1   1  A001   FALSE     FALSE        TRUE
#> 2   2  A002   FALSE      TRUE       FALSE
#> 3   3  A002   FALSE      TRUE       FALSE
#> 4   4   BAD   FALSE     FALSE        TRUE
#> 5   5  A005   FALSE     FALSE        TRUE

Pattern validation

Suppose identifiers must begin with A followed by three digits.

pattern_check(
  dat,
  variable = "ID",
  pattern = "^A[0-9]{3}$"
)
#>   Row Value Missing Match InvalidPattern
#> 1   1  A001   FALSE  TRUE          FALSE
#> 2   2  A002   FALSE  TRUE          FALSE
#> 3   3  A002   FALSE  TRUE          FALSE
#> 4   4   BAD   FALSE FALSE           TRUE
#> 5   5  A005   FALSE  TRUE          FALSE

Reusable validation rules

For repeated or study-specific validation, multiple requirements can be defined together using audit_rules().

rules <- audit_rules(
  range = list(
    Age = c(0, 30)
  ),
  category = list(
    Species = c(
      "Dog",
      "Cat"
    )
  ),
  unique = "ID",
  required = "Species",
  pattern = list(
    ID = "^A[0-9]{3}$"
  )
)

rules
#> $range
#> $range$Age
#> [1]  0 30
#> 
#> 
#> $category
#> $category$Species
#> [1] "Dog" "Cat"
#> 
#> 
#> $unique
#> [1] "ID"
#> 
#> $required
#> [1] "Species"
#> 
#> $pattern
#> $pattern$ID
#> [1] "^A[0-9]{3}$"
#> 
#> 
#> attr(,"class")
#> [1] "DataAuditRules" "list"

The rules object can be reused with datasets having the same expected structure.

Apply validation rules

Rules can be evaluated directly using apply_audit_rules().

rule_results <- apply_audit_rules(
  dat,
  rules
)

rule_results
#> DataAudit Rule Validation
#> =========================
#> 
#> Status:           Violations detected
#> Total violations: 8
#> 
#> Rule summary
#> ------------
#>      Rule Violations
#>     Range          2
#>  Category          2
#>    Unique          2
#>  Required          1
#>   Pattern          1
#> 
#> Variables with rule violations
#> ------------------------------
#> - Range: Age
#> - Category: Species
#> - Unique: ID
#> - Required: Species
#> - Pattern: ID

The returned object contains the rule-specific results and total number of violations.

rule_results$summary
#>       Rule Violations
#> 1    Range          2
#> 2 Category          2
#> 3   Unique          2
#> 4 Required          1
#> 5  Pattern          1
rule_results$total_violations
#> [1] 8

A compact summary can also be obtained using:

summary(rule_results)
#> $status
#> [1] "Violations detected"
#> 
#> $total_violations
#> [1] 8
#> 
#> $by_rule
#>       Rule Violations
#> 1    Range          2
#> 2 Category          2
#> 3   Unique          2
#> 4 Required          1
#> 5  Pattern          1
#> 
#> $violations
#>       Rule Variable Row Value
#> 1    Range      Age   2    40
#> 2    Range      Age   3    -2
#> 3 Category  Species   2 Horse
#> 4 Category  Species   3      
#> 5   Unique       ID   2  A002
#> 6   Unique       ID   3  A002
#> 7 Required  Species   3      
#> 8  Pattern       ID   4   BAD
#> 
#> attr(,"class")
#> [1] "summary.DataAuditRuleResult" "list"

Integrated audit

The validation rules can be incorporated directly into audit_data().

report <- audit_data(
  dat,
  include_nzv = FALSE,
  rules = rules
)

report
#> DataAudit Report
#> ================
#> 
#> Rows:                  5
#> Variables:             3
#> Variables with issues: 2
#> Duplicated rows:       0
#> Total issues:          2
#> Rule violations:       8
#> Overall issues:        10
#> 
#> Variables with issues
#> ---------------------
#>  Variable     Class Missing MissingPercent Blank Infinite Unique Constant
#>   Species character       0              0     1        0      4    FALSE
#>       Age   numeric       1             20     0        0      4    FALSE
#>  NearZeroVariance Issue
#>             FALSE  TRUE
#>             FALSE  TRUE
#> 
#> No duplicated rows detected.
#> 
#> Custom rule validation
#> ----------------------
#>      Rule Violations
#>     Range          2
#>  Category          2
#>    Unique          2
#>  Required          1
#>   Pattern          1
#> 
#> Total rule violations: 8

The resulting report now combines automatic data-quality assessment with user-defined validation rules.

report$summary
#>   Rows Variables VariablesWithIssues DuplicateRows TotalIssues RuleViolations
#> 1    5         3                   2             0           2              8
#>   OverallIssues
#> 1            10
report$rules
#> DataAudit Rule Validation
#> =========================
#> 
#> Status:           Violations detected
#> Total violations: 8
#> 
#> Rule summary
#> ------------
#>      Rule Violations
#>     Range          2
#>  Category          2
#>    Unique          2
#>  Required          1
#>   Pattern          1
#> 
#> Variables with rule violations
#> ------------------------------
#> - Range: Age
#> - Category: Species
#> - Unique: ID
#> - Required: Species
#> - Pattern: ID

Summarizing an audit

The report can be summarized using the standard summary() generic.

audit_summary <- summary(report)

audit_summary
#> $dataset
#>   Rows Variables VariablesWithIssues DuplicateRows TotalIssues RuleViolations
#> 1    5         3                   2             0           2              8
#>   OverallIssues
#> 1            10
#> 
#> $variables_with_issues
#>         Variable     Class Missing MissingPercent Blank Infinite Unique
#> Species  Species character       0              0     1        0      4
#> Age          Age   numeric       1             20     0        0      4
#>         Constant NearZeroVariance Issue
#> Species    FALSE            FALSE  TRUE
#> Age        FALSE            FALSE  TRUE
#> 
#> $duplicate_rows
#> [1] Row       Duplicate
#> <0 rows> (or 0-length row.names)
#> 
#> $rules
#>       Rule Violations
#> 1    Range          2
#> 2 Category          2
#> 3   Unique          2
#> 4 Required          1
#> 5  Pattern          1
#> 
#> attr(,"class")
#> [1] "summary.DataAuditReport" "list"

The summary object provides compact access to dataset-level information, variables with issues, duplicated rows, and rule results.

audit_summary$dataset
#>   Rows Variables VariablesWithIssues DuplicateRows TotalIssues RuleViolations
#> 1    5         3                   2             0           2              8
#>   OverallIssues
#> 1            10
audit_summary$variables_with_issues
#>         Variable     Class Missing MissingPercent Blank Infinite Unique
#> Species  Species character       0              0     1        0      4
#> Age          Age   numeric       1             20     0        0      4
#>         Constant NearZeroVariance Issue
#> Species    FALSE            FALSE  TRUE
#> Age        FALSE            FALSE  TRUE
audit_summary$duplicate_rows
#> [1] Row       Duplicate
#> <0 rows> (or 0-length row.names)
audit_summary$rules
#>       Rule Violations
#> 1    Range          2
#> 2 Category          2
#> 3   Unique          2
#> 4 Required          1
#> 5  Pattern          1

Data-quality score

An overall data-quality score can be calculated from a DataAuditReport.

score <- audit_score(report)

score
#> DataAudit Quality Score
#> =======================
#> 
#> Score:               60.00 / 100
#> Quality:             Poor
#> 
#> Dataset
#> -------
#> Rows:                5
#> Variables:           3
#> Data cells:          15
#> 
#> Issues
#> ------
#> Automatic issues:    2
#> Rule violations:     8
#> Overall issues:      10

The score ranges from 0 to 100, with higher values indicating fewer detected issues relative to dataset size.

The score is accompanied by one of five qualitative categories:

Excellent;
Good;
Moderate;
Poor; or
Critical.

The individual components can be inspected directly.

score$score
#> [1] 60
score$quality
#> [1] "Poor"
score$automatic_issues
#> [1] 2
score$rule_violations
#> [1] 8
score$overall_issues
#> [1] 10

The data-quality score is intended as a compact audit summary. It should not replace domain-specific assessment of whether a dataset is suitable for a particular scientific or analytical purpose.

Additional checks

DataAudit provides additional focused functions for different aspects of data quality, including:

constant_check();
nzv_check();
infinite_check();
outlier_check();
type_check();
date_check();
id_check();
length_check();
case_check();
whitespace_check();
sequence_check();
group_sequence_check();
dependency_check(); and
consistency_check().

These functions can be combined with the general audit when more detailed validation is required.

Recommended workflow

A practical workflow is:

Raw data
   |
   v
audit_data()
   |
   +---- General data-quality assessment
   |
   +---- Individual checks
   |
   v
audit_rules()
   |
   v
apply_audit_rules()
   |
   v
Integrated DataAuditReport
   |
   +---- print()
   |
   +---- summary()
   |
   v
audit_score()

This separates general data-quality screening from domain-specific validation while allowing both to be incorporated into a single structured report.

Conclusion

DataAudit provides a reproducible framework for detecting, organizing, and summarizing common data-quality problems in R.

The package can be used for rapid preliminary screening with audit_data(), focused validation through individual check functions, reusable validation with audit_rules(), and compact reporting through summary() and audit_score().

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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