pointblank: Data Validation and Organization of Metadata for Local and Remote Tables (original) (raw)

Validate data in data frames, 'tibble' objects, 'Spark' 'DataFrames', and database tables. Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables.

Version: 0.12.1
Depends: R (≥ 3.5.0)
Imports: base64enc (≥ 0.1-3), blastula (≥ 0.3.3), cli (≥ 3.6.0), DBI (≥ 1.1.0), digest (≥ 0.6.27), dplyr (≥ 1.0.10), dbplyr (≥ 2.3.0), fs (≥ 1.6.0), glue (≥ 1.6.2), gt (≥ 0.9.0), htmltools (≥ 0.5.4), knitr (≥ 1.42), rlang (≥ 1.0.3), magrittr, scales (≥ 1.2.1), testthat (≥ 3.1.6), tibble (≥ 3.1.8), tidyr (≥ 1.3.0), tidyselect (≥ 1.2.0), yaml (≥ 2.3.7)
Suggests: arrow, bigrquery, covr, crayon, data.table, duckdb, ggforce, ggplot2, jsonlite, log4r, lubridate, RSQLite, RMySQL, RPostgres, readr, rmarkdown, sparklyr, dittodb, odbc
Published: 2024-03-25
DOI: 10.32614/CRAN.package.pointblank
Author: Richard Iannone ORCID iD [aut, cre], Mauricio Vargas ORCID iD [aut], June Choe ORCID iD [aut]
Maintainer: Richard Iannone
BugReports: https://github.com/rstudio/pointblank/issues
License: MIT + file
URL: https://rstudio.github.io/pointblank/,https://github.com/rstudio/pointblank
NeedsCompilation: no
Materials: NEWS
In views: Databases
CRAN checks: pointblank results

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