Skip to contents

Personnel Validation Rules

personnel_rules.Rd

A dataset containing validation rules for personnel data quality checks. These rules are designed to be used with the validate package to assess the quality and consistency of personnel records in HRMIS data.

Usage

personnel_rules

Format

A tibble with 9 rows and 5 variables:

rule

Character. The validation rule expression as a string that can be parsed by validate::validator().

name

Character. Unique identifier for the rule (e.g., "personnel_ref_date_valid").

description

Character. Detailed explanation of what the rule checks. This description appears in validation summaries.

label

Character. Short 2-3 word label for the rule, useful for plotting and quick reference.

required_vars

Character. Comma-separated list of minimum required variables for the rule to be evaluated. At minimum: personnel_id and birth_date.

Source

Created for the govhr package data quality framework

Details

The personnel validation rules check:

  • Column existence (personnel_id, ref_date, birth_date, status)

  • ID uniqueness (personnel_id + ref_date combination is unique)

  • Reference date validity (within reasonable historical bounds)

  • Age range (18-70 years calculated from birth_date and ref_date)

  • Birth date reasonableness

  • Employment status validity (active, inactive, retired, terminated)

These rules can be programmatically converted to validate::validator() objects for use in data quality pipelines.

See also

contract_rules, validate_data

Examples

# View all personnel validation rules
personnel_rules
#> # A tibble: 6 × 4
#>   rule                                                   name  description label
#>   <chr>                                                  <chr> <chr>       <chr>
#> 1 is_unique(personnel_id, ref_date)                      pers… combinatio… Uniq…
#> 2 ref_date >= as.Date('1900-01-01') & ref_date <= Sys.D… pers… ref_date i… Vali…
#> 3 as.numeric(difftime(ref_date, birth_date, units = 'da… pers… worker age… Mini…
#> 4 status == 'active' & as.numeric(difftime(ref_date, bi… pers… worker age… Maxi…
#> 5 birth_date >= as.Date('1920-01-01') & birth_date <= S… pers… birth_date… Vali…
#> 6 status %in% c('active', 'inactive', 'retired', 'termi… pers… employment… Vali…

# Filter to specific rule
personnel_rules[personnel_rules$name == "personnel_age_range", ]
#> # A tibble: 0 × 4
#> # ℹ 4 variables: rule <chr>, name <chr>, description <chr>, label <chr>