Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, chapter 6.
Version: | 1.0.1 |
Depends: | R (≥ 3.5.0), methods |
Imports: | stats, graphics, grid, settings, yaml |
Suggests: | tinytest (≥ 0.9.6), knitr, bookdown, lumberjack |
Published: | 2020-12-08 |
Author: | Mark van der Loo |
Maintainer: | Mark van der Loo <mark.vanderloo at gmail.com> |
BugReports: | https://github.com/data-cleaning/validate/issues |
License: | GPL-3 |
URL: | https://github.com/data-cleaning/validate |
NeedsCompilation: | yes |
Citation: | validate citation info |
Materials: | NEWS |
In views: | OfficialStatistics |
CRAN checks: | validate results |
Reference manual: | validate.pdf |
Vignettes: |
The Data Validation Cookbook Data Validation Infrastructure for R |
Package source: | validate_1.0.1.tar.gz |
Windows binaries: | r-devel: validate_1.0.1.zip, r-release: validate_1.0.1.zip, r-oldrel: validate_0.9.3.zip |
macOS binaries: | r-release: validate_1.0.1.tgz, r-oldrel: validate_0.9.3.tgz |
Old sources: | validate archive |
Reverse depends: | errorlocate, validatetools |
Reverse imports: | dcmodify, deductive, iNZightTools, rspa |
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