Find errors in data given a set of validation rules.
errorlocate helps to identify obvious errors in raw datasets.
It works in tandem with the package
validate you formulate data validation rules to which the data must comply.
"age cannot be negative":
age >= 0
validate can identify if a record is valid or not, it does not identify
which of the variables are responsible for the invalidation. This may seem a simple task,
but is actually quite tricky: a set of validation rules form a web
of dependent variables: changing the value of an invalid record to repair for rule 1, may invalidate
the record for rule 2.
Errorlocate provides a small framework for record based error detection and implements the Felligi Holt algorithm. This algorithm assumes there is no other information available then the values of a record and a set of validation rules. The algorithm minimizes the (weighted) number of values that need to be adjusted to remove the invalidation.
errorlocate package translates the validation and error localization problem into
a mixed integer problem and uses #' a mip solver to find a solution.
Maintainer: Edwin de Jonge [email protected]
Mark van der Loo [email protected]
T. De Waal (2003) Processing of Erroneous and Unsafe Data. PhD thesis, University of Rotterdam.
Van der Loo, M., de Jonge, E, Data Cleaning With Applications in R
E. De Jonge and Van der Loo, M. (2012) Error localization as a mixed-integer program in editrules.
lp_solve and Kjell Konis. (2011). lpSolveAPI: R Interface for lp_solve version 220.127.116.11. R package version 18.104.22.168-5. http://CRAN.R-project.org/package=lpSolveAPI
Report bugs at https://github.com/data-cleaning/errorlocate/issues
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