confront: Confront data with a (set of) expressionset(s)

In validate: Data Validation Infrastructure

Confront data with a (set of) expressionset(s)

Description

An expressionset is a general class storing rich expressions (basically expressions and some meta data) which we call 'rules'. Examples of expressionset implementations are validator objects, storing validation rules and indicator objects, storing data quality indicators. The confront function evaluates the expressions one by one on a dataset while recording some process meta data. All results are stored in a (subclass of a) confrontation object.

Usage

confront(dat, x, ref, ...)

## S4 method for signature 'data.frame,indicator,ANY'
confront(dat, x, key = NULL, ...)

## S4 method for signature 'data.frame,indicator,environment'
confront(dat, x, ref, key = NULL, ...)

## S4 method for signature 'data.frame,indicator,data.frame'
confront(dat, x, ref, key = NULL, ...)

## S4 method for signature 'data.frame,indicator,list'
confront(dat, x, ref, key = NULL, ...)

## S4 method for signature 'data.frame,validator,ANY'
confront(dat, x, key = NULL, ...)

## S4 method for signature 'data.frame,validator,environment'
confront(dat, x, ref, key = NULL, ...)

## S4 method for signature 'data.frame,validator,data.frame'
confront(dat, x, ref, key = NULL, ...)

## S4 method for signature 'data.frame,validator,list'
confront(dat, x, ref, key = NULL, ...)

Arguments

dat	An R object carrying data
x	An R object carrying rules.
ref	Optionally, an R object carrying reference data. See examples for usage.
...	Options used at execution time (especially 'raise'). See voptions.
key	(optional) name of identifying variable in x.

Reference data

Reference data is typically a list with a items such as a code list, or a data frame of which rows match the rows of the data under scrutiny.

See Also

voptions

Other confrontation-methods: [,expressionset-method, as.data.frame,confrontation-method, confrontation-class, errors(), event(), keyset(), length,expressionset-method, values()

Other validation-methods: aggregate,validation-method, all,validation-method, any,validation-method, barplot,validation-method, check_that(), compare(), event(), names<-,rule,character-method, plot,validation-method, sort,validation-method, summary(), validation-class, values()

Other indication-methods: event(), indication-class, summary()

Examples

# a basic validation example
v <- validator(height/weight < 0.5, mean(height) >= 0)
cf <- confront(women, v)
summary(cf)
plot(cf)
as.data.frame(cf)

# an example checking metadata
v <- validator(nrow(.) == 15, ncol(.) > 2)
summary(confront(women, v))

# An example using reference data
v <- validator(weight == ref$weight)
summary(confront(women, v, women))

# Usging custom names for reference data
v <- validator(weight == test$weight)
summary( confront(women,v, list(test=women)) )

# Reference data in an environment
e <- new.env()
e$test <- women
v <- validator(weight == test$weight)
summary( confront(women, v, e) )

# the effect of using a key
w <- women
w$id <- letters[1:nrow(w)]
v <- validator(weight == ref$weight)

# with complete data; already matching
values( confront(w, v, w, key='id'))

# with scrambled rows in reference data (reference gets sorted according to dat)
i <- sample(nrow(w))
values(confront(w, v, w[i,],key='id'))

# with incomplete reference data
values(confront(w, v, w[1:10,],key='id'))

validate documentation built on July 4, 2024, 9:07 a.m.