# rstudio/pointblank

Data quality assessment and metadata reporting for data frames and database tables

Repository: https://github.com/rstudio/pointblank
Canonical: https://ross.abutalabs.com/products/pointblank
Homepage: https://rstudio.github.io/pointblank/
Language: R
License: NOASSERTION
License Family: other
Topics: data-validation, database-tables, data-dictionaries, easy-to-understand, data-frames, reporting-tool, data-profiler, data-management, schema-validation, data-verification, data-checker, data-assertions, yaml-configuration, data-inference, testing-tools, data-quality
Last push: 2026-08-21T19:59:31+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 62, longevity 100
- inputs: {"age_days": 3478, "days_push": 12, "days_rel": 43, "gap_med": 317.5, "n_releases_24m": 3}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1047, forks 60 (observed 2026-08-28T04:03:21.907554+00:00)

## What it is
pointblank is an R package for methodically validating data in data frames and database tables, producing data quality reports. It also maintains up-to-date table metadata and data dictionaries via informant objects.

## Use cases
- validate data quality of data frames and database tables
- generate data quality analysis reports
- profile tables and build data dictionaries
- check schema and column-level assertions on tables
- schedule periodic validation of on-disk data files
- email data quality reports when failure thresholds are exceeded

## When to choose
- you work in R and need systematic data validation with readable reports
- you need to keep table metadata and data dictionaries up to date
- you validate both local data frames and remote database tables
- you want YAML-configured validation workflows with alerting

## When to avoid
- you need data validation in Python or another non-R language
- you need streaming or real-time data quality monitoring at scale
- you only need simple unit tests rather than data quality checks

## Facets
- artifact type: library
- maturity: active
- function: testing, analytics, data-science, etl, monitoring
- domain: data-science, analytics, databases, developer-tools
- platform: cross-platform
- tags: data-validation, data-quality, data-profiling, data-dictionary, schema-validation, yaml-configuration, reporting, r-package, data-frames, database-tables, data-engineering, r

## Member repositories
- rstudio/pointblank (main) score 86

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:21.907554+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:01:09.260204+00:00, confidence not recorded.
  - readme: https://github.com/rstudio/pointblank (fetched 2026-08-28T04:03:21.907554+00:00, sha 622d20ae51d3)
  - homepage: https://rstudio.github.io/pointblank/ (fetched 2026-08-29T13:02:27.218577+00:00, sha ab650d558a28)
- Data as of 2026-08-30T08:39:29.467469+00:00.