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editrules (version 2.9.6)

Parsing, Applying, and Manipulating Data Cleaning Rules

Description

Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package.

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Install

install.packages('editrules')

Monthly Downloads

489

Version

2.9.6

License

GPL-3

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Repository

https://github.com/data-cleaning/editrules

Maintainer

Edwin Jonge

Last Published

November 28th, 2025

Functions in editrules (2.9.6)

Determine which edits contain which variable(s)

Get condition matrix from an editset.

contains.boolmat

Determine if a boolean matrix contains var

Parse textual, categorical edit rules to an editarray

Determine edittypes in editset based on 'contains(E)'

editrules.plotting

Graphical representation of edits

Check data against a datamodel

Read general edits

Create an editmatrix with categorical variables

Eliminate a variable from a set of edit rules

errorLocalizer_mip

Localize errors using a MIP approach.

Create a backtracker object for error localization

Field code forest algorithm

Derive all essentially new implicit edits

editrules_package

An overview of the function of package editrules

getVars.editarray

get variable names in editarray

getVars.cateditmatrix

Returns the variable names of an (in)equality editmatrix E

Example editrules, used in vignette

getVars.editlist

get variable names

getVars.editmatrix

Returns the variable names of an (in)equality editmatrix E

get index list from editmatrix

Get named logical array from editarray

Summarize data model of an editarray in a data.frame

Returns augmented matrix representation of edit set.

Returns the coefficient matrix A of linear (in)equalities

The errorLocation object

Expand an edit expression

Retrieve values stricktly implied by rules

Parse a character vector of edits

editarray: logical array where every column corresponds to one level of one variable. Every row is an edit. Every edit denotes a *forbidden* combination.

Parse a mixed edit

Create an editmatrix object from its constituing attributes.

Derive textual representation from (partial) indices

Get upperbounds of edits, given the boundaries of all variables

Check object class

Normalizes an editmatrix

Parse a categorical edit expression

Compute index from array part of editarray

newerrorlocation

Generate new errorlocation object

Simplify logical mixed edits in an editset

get names of variables in a set of edits

print.errorLocation

Print object of class errorLocation

print.locationsummary

Returns the derivation history of an edit matrix or array

Returns the constant part b of a linear (in)equality

retrieve edit names from editarray

print.editsummary

retrieve level names from editarray

Check consistency of set of edits

Derive editmatrix with soft constraints based on boundaries of variables. This is a utility function that is used for constructing a mip/lp problem.

Check if an editmatrix is normalized

Read edits edits from free-form textfile

Create an editmatrix

isObviouslyInfeasible

Check for obvious contradictions in a set of edits

isObviouslyRedundant

Find obvious redundancies in set of edits

Returns the operator part of a linear (in)equality editmatrix E

Localize errors on records in a data.frame.

get seprator used to seperate variables from levels in editarray

Number of edits Count the number of edits in a collection of edits.

softEdits.cateditmatrix

Derive editmatrix with soft constraints. This is a utility function that is used for constructing a mip/lp problem.

softEdits.editarray

Derive editmatrix with soft constraints based on boundaries of variables. This is a utility function that is used for constructing a mip/lp problem.

parse categorial edit

Rewrite an editset and reported values into the components needed for a mip solver

Check which edits are dominated by other ones.

print.violatedEdits

Print violatedEdits

print.cateditmatrix

print cateditmatrix

Remove redundant variables and edits.

Workhorse function for localizeErrors

print.editmatrix

print editmatrix

print.editarray

print editarray

Check data against constraints

Replace a variable by a value in a set of edits.

Parse a numerical edit expression

print.backtracker

print a backtracker

Row index operator for editmatrix

Separate an editset into its disconnected blocks and simplify

removeRedundantDummies

Remove redundant dummy variables

softEdits.editmatrix

Derive editmatrix with soft constraints based on boundaries of variables. This is a utility function that is used for constructing a mip/lp problem.

Backtracker: a flexible and generic binary search program

Write an editset into a mip representation

Transform a found solution into a categorical record

Decompose a matrix or edits into independent blocks

Coerce a matrix to an edit matrix.

Derive adjecency matrix from collection of edits

Coerces a mip object into an lpsolve object

Coerce x to an editset

Decouple a set of conditional edits

Bring an (edit) matrix to reduced row echelon form.

Add dummy variable to the data.frames, these are needed for errorlocations etc.

duplicated.editmatrix

Check for duplicate edit rules

as.character.cateditmatrix

Coerce an cateditmatrix to a character vector

duplicated.editarray

Check for duplicate edit rules