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VIN Model Year Code Validation (Position 10)

formathigh

Validates that position 10 of the VIN contains a valid model year character. The year code follows a 30-year cycle defined by NHTSA: digits 1-9 and letters A-H, J-N, P, R-T, V-Y (excluding I, O, Q, U, and Z). For example, 'R' = 2024, 'S' = 2025, 'T' = 2026, '1' = 2027 in the current cycle.

v1.0.0by dqhub505 downloads4.9 (46)
vinyear-codevehicleautomotivenhtsamodel-year
Try This Rule

Parameters

column_namestringrequired

The column containing email addresses

thresholdfloatdefault: 0.99

Minimum fraction of valid emails (0.0 to 1.0)

Compliance Mapping

NHTSA49 CFR Part 565.15(b) - Year of manufacture

ISOISO 3779:2009 Annex B

Install

soda
checks for {{table_name}}:
  - invalid_percent({{column_name}}) < {{(1 - threshold) * 100}}:
      valid regex: '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
dbt
{% test valid_email(model, column_name) %}
select {{ column_name }}
from {{ model }}
where {{ column_name }} not regexp '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
{% endtest %}
sql
SELECT COUNT(*) as total,
  SUM(CASE WHEN {{column_name}} REGEXP
    '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
    THEN 1 ELSE 0 END) as valid
FROM {{table_name}}
Great Expectations
{
  "expectation_type": "expect_column_values_to_match_regex",
  "kwargs": {
    "column": "{{column_name}}",
    "regex": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$",
    "mostly": {{threshold}}
  }
}
spark
from pyspark.sql.functions import col
pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
invalid = df.filter(~col("{{column_name}}").rlike(pattern)).count()

Test Data

Passing Examples

idvalue
1alice@example.com
2bob.smith@company.co.uk
3charlie+tag@domain.org

Failing Examples

idvalue
1not-an-email
2@missing-local.com
3spaces in@email.com

CLI

Terminal
npx dqhub install vin-year-code --format soda --table YOUR_TABLE
npx dqhub install vin-year-code --format dbt --model YOUR_MODEL
npx dqhub install vin-year-code --format sql --dialect snowflake