Validate Employment Data Types and Formats

Performs comprehensive validation of employment data types, formats, and basic logical consistency. Throws errors for critical issues and warnings for potential problems. Includes validation for over_id and duration calculation consistency.

Usage

validate_employment_data_types(
  dt,
  strict = FALSE,
  validate_over_id = TRUE,
  validate_duration = TRUE
)

Arguments

dt

Data.table with standardized employment columns

strict

Logical. If TRUE, throws errors for warnings

validate_over_id

Logical. If TRUE, validates over_id column consistency

validate_duration

Logical. If TRUE, validates duration calculation invariant

Value

Invisible validation results list

Examples

if (FALSE) { # \dontrun{
library(data.table)
library(vecshift)

# Create correctly typed data
good_data <- data.table(
  id = 1:3,
  cf = c("P001", "P001", "P002"),
  inizio = as.Date(c("2023-01-01", "2023-04-01", "2023-02-01")),
  fine = as.Date(c("2023-03-31", "2023-06-30", "2023-05-31")),
  prior = c(1, 0, 1)
)

# Validate - should pass
validation_good <- validate_employment_data_types(good_data)
print(validation_good)

# Create data with type issues
bad_data <- data.table(
  id = 1:3,
  cf = c("P001", "P001", "P002"),
  inizio = c("2023-01-01", "2023-04-01", "2023-02-01"),  # Character, not Date!
  fine = as.Date(c("2023-03-31", "2023-06-30", "2023-05-31")),
  prior = c("1", "0", "1")  # Character, not numeric!
)

# Validate - should identify issues
validation_bad <- validate_employment_data_types(bad_data)
print(validation_bad)

# Check validation results
if (!validation_bad$is_valid) {
  cat("\nType errors detected:\n")
  print(validation_bad)
}
} # }