Package: validate 1.1.7

Mark van der Loo

validate: Data Validation Infrastructure

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. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6 and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.

Authors:Mark van der Loo [cre, aut], Edwin de Jonge [aut], Paul Hsieh [ctb]

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|manual.html
DESCRIPTION |NEWS
card.svg |card.png
validate/json (API)

# Install 'validate' in R:
install.packages('validate', repos = c('https://data-cleaning.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/data-cleaning/validate/issues

On CRAN:

Conda:

data-cleaningvalidation

12.19 score 432 stars 9 packages 652 scripts 2.1k downloads 2 mentions 83 exports 2 dependencies

Last updated from:cb1e94528d. Checks:12 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR132
linux-devel-x86_64ERROR176
source / vignettesERROR151
linux-release-arm64ERROR146
linux-release-x86_64ERROR179
macos-release-arm64ERROR85
macos-release-x86_64ERROR211
macos-oldrel-arm64ERROR107
macos-oldrel-x86_64ERROR151
windows-develERROR120
windows-releaseERROR119
windows-oldrelERROR91
wasm-releaseOK145

Exports:.blocks_expressionset.get_exprs.ini_expressionset_cli.ini_expressionset_df.ini_expressionset_yml.PKGOPT.show_expressionset%vin%add_indicatorsaggregateall_completeall_uniqueas_yamlas.data.framebarplotcellscheck_thatcompareconfrontcontains_at_leastcontains_at_mostcontains_exactlycreatedcreated<-descriptiondescription<-do_bydoes_not_containerrorsestat_codelisteventevent<-exists_anyexists_oneexport_yamlexprfield_formatfield_lengthglobglobal_codelisthbhierarchyin_linear_sequencein_rangeindicatoris_completeis_linear_sequenceis_uniquekeysetlabellabel<-lackinglbj_cellslbj_rulesmatch_cellsmax_bymean_bymetameta<-min_bynumber_formatoriginorigin<-part_whole_relationplotresetrun_validation_dirrun_validation_filerxsatisfyingsdmx_codelistsdmx_endpointsortsum_bysummaryvalidate_optionsvalidatorvalidator_from_dsdvaluesvariablesviolatingvoptionswarnings

Dependencies:settingsyaml

The Data Validation Cookbook
Preface | Prerequisites | Citing this work | Acknowledgements | Contributing | License | Introduction to validate | A quick example | Variable checks | Variable type | Missingness | Field length | Format of numeric fields | General field format | Numeric ranges | Ranges for times and periods | Code lists | Availability and uniqueness | Long data | Uniqueness | Availability of records | Gaps in (time) series | Multivariate checks | Completeness of records | Balance equalities and inequalities | Conditional restrictions | Forbidden value combinations | Statistical checks | Statistical and groupwise characteristics | Group properties | Code hierarchies and aggregation | General aggregates in long-form data | Notes | Aggregates of time series in long format | Indicators | A first example | Getting indicator values | Working with validate | Manipulating rule sets | Rule metadata | Rules in data frames | Validation rule syntax | Confrontation objects | Confrontation options | Using reference data | Rules in text files | Reading rules from file | Metadata in text files: YAML | Setting options | Including other rule files | Exporting validator objects | Rules from SDMX | SDMX and validate | SDMX and API locations | Code lists from SDMX registries | Derive rules from DSD | More on SDMX | Comparing data sets | Cell counts | Comparing rule violations | validate and lumberjack | Bibliographical notes

Last update: 2025-12-10
Started: 2020-11-19

Data Validation Infrastructure for R

Last update: 2021-06-09
Started: 2019-12-16