Date/DateTime Validator — datevalidator • ClinicoPath

Comprehensive date and datetime field validation and quality assessment using multiple R packages (datefixR, anytime, lubridate). This module validates and diagnoses messy date/datetime formats commonly found in clinical databases: different separators, month representations, missing components, and ambiguous formats. Provides detailed validation reports and quality assessment. Perfect for clinical research data quality control and database audit workflows.

Usage

datevalidator(
  data,
  date_vars,
  correction_method = "datefixr",
  date_format = "auto",
  day_impute = 1,
  month_impute = 7,
  handle_excel = FALSE,
  timezone = "UTC",
  show_correction_table = FALSE,
  show_quality_assessment = FALSE,
  show_format_analysis = FALSE,
  show_correction_summary = FALSE,
  show_interpretation = FALSE
)

Arguments

data: The data as a data frame.
date_vars: Variables containing date or datetime information in various formats that need validation and quality assessment. Can handle character strings, numeric values, factors with date/datetime representations.
correction_method: Method for date/datetime validation. datefixR provides robust format detection, anytime offers flexible parsing, lubridate allows format specification, and consensus combines methods for maximum reliability.
date_format: Expected date or datetime format for ambiguous cases. Auto-detect tries to determine the most likely format based on the data patterns. HMS formats include time components (hours, minutes, seconds).
day_impute: Day of month to impute when day is missing (1-31). Default is 1st of month. If value exceeds days in month, last day of month will be used.
month_impute: Month to impute when month is missing (1-12). Default is 7 (July). Commonly used middle-year value for clinical research.
handle_excel: Whether to convert Excel numeric date values (days since 1900-01-01). Useful for data exported from Excel spreadsheets.
timezone: Timezone for output dates/datetimes. Use UTC for standardization, or local timezone if time-of-day information is critical. Only applies to anytime and consensus methods.
show_correction_table: Display detailed table showing original values, validated values, and validation status for each observation.
show_quality_assessment: Provide quality assessment including success rates, common problems, and recommendations for further validation.
show_format_analysis: Analyze detected date/datetime formats and patterns in the original data.
show_correction_summary: Summary statistics of the validation process including before/after comparison and data quality metrics.
show_interpretation: Display guidance on date/datetime validation methods, best practices, and recommendations for clinical research data. Includes information about the DateTime Converter module.

Value

A results object containing:

`results$todo`					a html
`results$corrected_data`					a table
`results$correction_table`					a html
`results$quality_assessment`					a html
`results$format_analysis`					a html
`results$correction_summary`					a html
`results$interpretation`					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$corrected_data$asDF

as.data.frame(results$corrected_data)

Details

For datetime conversion with component extraction (year, month, day, hour, minute, second), see the DateTime Converter module.

Examples

# \donttest{
# Example:
# 1. Select variables containing date/datetime information that need validation.
# 2. Choose validation method (automatic detection or specific format).
# 3. Configure missing value imputation settings.
# 4. Review validation results and quality assessment.
# }