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editrules (version 2.7.2)

R package for parsing, applying, and manipulating data cleaning rules

Description

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

410

Version

2.7.2

License

GPL-3

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Repository

https://github.com/edwindj/editrules

Maintainer

Edwin Jonge

Last Published

July 3rd, 2013

Functions in editrules (2.7.2)

Summarize data model of an editarray in a data.frame

contains.boolmat

Determine if a boolean matrix contains var

Derive all essentially new implicit edits

Derive adjecency matrix from collection of edits

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

Workhorse function for localizeErrors

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

getVars.editarray

get variable names in editarray

Check if an editmatrix is normalized

Derive textual representation from (partial) indices

Get upperbounds of edits, given the boundaries of all variables

Check data against a datamodel

The errorLocation object

Create an editmatrix object from its constituing attributes.

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

Read general edits

Parse a character vector of edits

get index list from editmatrix

as.character.cateditmatrix

Coerce an cateditmatrix to a character vector

Compute index from array part of editarray

get seprator used to seperate variables from levels in editarray

Check consistency of set of edits

Parse a numerical edit expression

Remove redundant variables and edits.

Coerce x to an editset

duplicated.editmatrix

Check for duplicate edit rules

Returns the derivation history of an edit matrix or array

Example editrules, used in vignette

Read edits edits from free-form textfile

print.backtracker

print a backtracker

print.editarray

print editarray

Get named logical array from editarray

newerrorlocation

Generate new errorlocation object

editrules-package

An overview of the function of package editrules

Parse a mixed edit

Determine which edits contain which variable(s)

removeRedundantDummies

Remove redundant dummy variables

Check data against constraints

Decompose a matrix or edits into independent blocks

Backtracker: a flexible and generic binary search program

Returns the coefficient matrix A of linear (in)equalities

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

Coerce a matrix to an edit matrix.

errorLocalizer.mip

Localize errors using a MIP approach.

Create an editmatrix with categorical variables

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

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

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

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

getVars.cateditmatrix

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

get names of variables in a set of edits

isObviouslyRedundant

Find obvious redundancies in set of edits

as.matrix.editarray

Parse textual, categorical edit rules to an editarray

Normalizes an editmatrix

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

Eliminate a variable from a set of edit rules

Transform a found solution into a categorical record

Get condition matrix from an editset.

print.editmatrix

print editmatrix

Localize errors on records in a data.frame.

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

Create a backtracker object for error localization

retrieve level names from editarray

Simplify logical mixed edits in an editset

Separate an editset into its disconnected blocks and simplify

softEdits.cateditmatrix

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

isObviouslyInfeasible

Check for obvious contradictions in a set of edits

Check which edits are dominated by other ones.

as.matrix.editmatrix

Create an editmatrix

retrieve edit names from editarray

Decouple a set of conditional edits

print.violatedEdits

Print violatedEdits

duplicated.editarray

Check for duplicate edit rules

Expand an edit expression

getVars.editmatrix

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

Field code forest algorithm

editrules.plotting

Graphical representation of edits

print.errorLocation

Print object of class errorLocation

Row index operator for editmatrix

Returns augmented matrix representation of edit set.

Parse a categorical edit expression

print.cateditmatrix

print cateditmatrix

Check object class

parse categorial edit

Create an editmatrix

Parse textual, categorical edit rules to an editarray

iter.backtracker

iterate over all solutions of a backtracker

Read general edits