Data Validation Infrastructure
Translate a validatorComparison object to data frame
Coerce to data.frame
split-apply-combine for vectors, with equal-length outptu
Get messages from a confrontation object
Get or set event information metadata from a 'confrontation' object.
Extract or set names
Plot validation results
Aggregate and sort the results of a validation.
Replace a rule in a ruleseta
Replace a subset of an expressionset with another expressionset
Syntax to define validation or indicator rules
Get values from object
Get variable names
A consistent set membership operator
Compare similar data sets
Add indicator values as columns to a data frame
Aggregate validation results
Test if all validations resulted in TRUE
Test if any validation resulted in TRUE
Translate cellComparison objects to data frame
Coerce a confrontation object to data frame
Translate an expressionset to data.frame
Barplot of cellComparison object
Plot number of violations
Barplot of validatorComparison object
Cell counts and differences for a series of datasets
Simple data validation interface
Confront data with a (set of) expressionset(s)
Superclass storing results of confronting data with rules
Check records using a predifined table of (im)possible values
Creation timestamp
Rule description
Test for uniquenes of records
Test for (unique) existence
Export to yaml file
Get expressions
Superclass for storing a set of rich expressions.
Check whether a field conforms to a regular expression
Check number of code points
Hiridoglu-Berthelot function
Check aggregates defined by a hierarchical code list
Check variable range
Store results of evaluating indicators
Store a set of rich indicator expressions
Define indicators for data
Test for completeness of records
Check whether a variable represents a linear sequence
Get key set stored with a confrontation
Rule label
Logging object to use with the lumberjack package
Logging object to use with the lumberjack package
Determine the number of elements in an object.
Create matching subsets of a sequence of data
Get or set rule metadata
NACE classification code table
Check the layouts of numbers.
Origin of rules
Test whether details combine to a chosen aggregate
Line graph of a cellComparison object.
Plot a validator object
Line graph of validatorComparison object
Combine two indicator objects
Combine two validator objects
data on Dutch supermarkets
A rich expression
Run a file with confrontations. Capture results
Label objects for interpretation as pattern
Select records (not) satisfying rules
Get code list from an SDMX REST API endpoint.
Get URL for known SDMX registry endpoints
Select a subset
Define validation rules for data
Services for extending 'validate'
Create a summary
Data Validation Infrastructure
Store results of evaluating validating expressions
Extract a rule set from an SDMX DSD file
Store a set of rich validating rules.
Set or get options globally or per object.
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>.
Useful links