check: Perform checks of potential errors in variable/dataset

In ekstroem/cleanR: A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process

Perform checks of potential errors in variable/dataset

Description

Run a set of validation checks to check a variable vector or a full dataset for potential errors. Which checks are performed depends on the class of the variable and on user inputs.

Usage

check(v, nMax = 10, checks = setChecks(), ...)

Arguments

v	the vector or the dataset (data.frame) to be checked.
nMax	If a check is supposed to identify problematic values, this argument controls if all of these should be pasted onto the outputted message, or if only the first nMax should be included. If set to Inf, all problematic values are printed.
checks	A list of checks to use on each supported variable type. We recommend using setChecks for creating this list and refer to the documentation of this function for more details.
...	Other arguments that are passed on to the checking functions. These includes general parameters controlling how the check results are formatted (e.g. maxDecimals, which controls the number of decimals printed for numerical, problematic values).

Details

It should be noted that the default options for each variable type are returned by calling e.g. defaultCharacterChecks(), defaultFactorChecks(), defaultNumericChecks(), etc. A complete overview of all default options can be obtained by calling setChecks(). Moreover, all available checkFunctions (including both locally defined functions and functions imported from dataMaid or other packages) can be viewed by calling allCheckFunctions().

Value

If v is a variable, a list of objects of class checkResult, which each summarizes the result of a checkFunction call performed on v. See checkResult for more details. If V is a data.frame, a list of lists of the form above is returned instead with one entry for each variable in v.

References

Petersen AH, Ekstrøm CT (2019). “dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R.” _Journal of Statistical Software_, *90*(6), 1-38. doi: 10.18637/jss.v090.i06 ( \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v090.i06")}).

See Also

setChecks, allCheckFunctions checkResult checkFunction, defaultCharacterChecks, defaultFactorChecks, defaultLabelledChecks, defaultHavenlabelledChecks, defaultNumericChecks, defaultIntegerChecks, defaultLogicalChecks, defaultDateChecks

Examples

x <- 1:5
check(x)

#Annoyingly coded missing as 99
y <- c(rnorm(100), rep(99, 10))
check(y)

#Check y for outliers and print 4 decimals for problematic variables
check(y, checks = setChecks(numeric = "identifyOutliers"), maxDecimals = 4)

#Change what checks are performed on a variable, now only identifyMissing is called
# for numeric variables
check(y, checks = setChecks(numeric = "identifyMissing"))

#Check a full data.frame at once
data(cars)
check(cars)

#Check a full data.frame at once, while changing the standard settings for
#several data classes at once. Here, we ommit the check of miscoded missing values for factors
#and we only do this check for numeric variables:
check(cars, checks = setChecks(factor = defaultFactorChecks(remove = "identifyMissing"),
  numeric = "identifyMissing"))

ekstroem/cleanR documentation built on June 12, 2025, 11:08 a.m.