Basic Data Quality Checks in R
Table 1
Table
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Table 1
Table
Table 1
| Check type | Example rule | R approach | Follow-up action | |
|---|---|---|---|---|
| Missing required value | Consent date is missing | `filter(is.na(consent_date))` | Query site or confirm source documentation | |
| Duplicate ID | Same participant ID appears twice | `count(participant_id) | > filter(n > 1)` | Determine whether duplicate entry or repeated event |
| Range check | Age below 18 in adult study | `filter(age_years < 18)` | Review eligibility and source data | |
| Category check | Sex value outside expected list | `%in% expected_values` | Correct coding or update controlled terminology | |
| Date sequence | Discharge before admission | `filter(discharge_date < admission_date)` | Query date fields or review admission episode | |
| Site pattern | High missingness at one site | `group_by(site) | > summarise(...)` | Provide targeted retraining or workflow support |