Find errors in data given a set of validation rules.

errorlocate helps identify which cells in a record are likely causing rule violations.

Details

The package is designed to work with validate::validator() rules. While validate can determine whether a record is valid, errorlocate tries to identify which fields are most likely erroneous.

This is non-trivial because rules are interdependent: changing one value to satisfy one rule may violate another rule.

errorlocate implements record-level error localization based on the Fellegi-Holt approach. It translates the localization task into a mixed integer optimization problem and uses a MIP solver to find a minimum-weight set of fields to change.

Typical workflow:

• Define rules with validate::validator().

• Locate likely errors with locate_errors().

• Replace flagged cells with missing values using replace_errors().

References

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 5.5.2.0. R package version 5.5.2.0-5. http://CRAN.R-project.org/package=lpSolveAPI

See also

Useful links:

• https://github.com/data-cleaning/errorlocate

• Report bugs at https://github.com/data-cleaning/errorlocate/issues

Author

Maintainer: Edwin de Jonge edwindjonge@gmail.com (ORCID)

Authors:

• Mark van der Loo mark.vanderloo@gmail.com