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editrules (version 2.9.3)

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

1,726

Version

2.9.3

License

GPL-3

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Repository

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

Maintainer

Edwin Jonge

Last Published

July 1st, 2018

Functions in editrules (2.9.3)

Check data against a datamodel

Read edits edits from free-form textfile

Parse textual, categorical edit rules to an editarray

Retrieve values stricktly implied by rules

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

Returns the derivation history of an edit matrix or array

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

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

contains.boolmat

Determine if a boolean matrix contains var

The errorLocation object

editrules-package

An overview of the function of package editrules

get seprator used to seperate variables from levels in editarray

duplicated.editarray

Check for duplicate edit rules

Expand an edit expression

retrieve level names from editarray

Derive textual representation from (partial) indices

retrieve edit names from editarray

Returns augmented matrix representation of edit set.

Example editrules, used in vignette

Create an editmatrix

Get named logical array from editarray

get index list from editmatrix

Summarize data model of an editarray in a data.frame

isObviouslyRedundant

Find obvious redundancies in set of edits

isObviouslyInfeasible

Check for obvious contradictions in a set of edits

Parse a categorical edit expression

newerrorlocation

Generate new errorlocation object

getVars.cateditmatrix

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

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

Check which edits are dominated by other ones.

getVars.editarray

get variable names in editarray

parse categorial edit

print.cateditmatrix

print cateditmatrix

Normalizes an editmatrix

Workhorse function for localizeErrors

print.editarray

print editarray

Parse a numerical edit expression

removeRedundantDummies

Remove redundant dummy variables

Separate an editset into its disconnected blocks and simplify

Create an editmatrix object from its constituing attributes.

Read general edits

Eliminate a variable from a set of edit rules

Derive all essentially new implicit edits

Field code forest algorithm

Remove redundant variables and edits.

Create a backtracker object for error localization

print.violatedEdits

Print violatedEdits

Parse a character vector of edits

softEdits.cateditmatrix

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

print.backtracker

print a backtracker

errorLocalizer_mip

Localize errors using a MIP approach.

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.

getVars.editmatrix

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

getVars.editlist

get variable names

Parse a mixed edit

print.errorLocation

Print object of class errorLocation

Get upperbounds of edits, given the boundaries of all variables

print.locationsummary

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

get names of variables in a set of edits

Check if an editmatrix is normalized

Check consistency of set of edits

Localize errors on records in a data.frame.

Check object class

Compute index from array part of editarray

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

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.

Row index operator for editmatrix

print.editsummary

print.editmatrix

print editmatrix

Simplify logical mixed edits in an editset

Replace a variable by a value in a 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 data against constraints

Coerce a matrix to an edit matrix.

as.character.cateditmatrix

Coerce an cateditmatrix to a character vector

Backtracker: a flexible and generic binary search program

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

Coerce x to an editset

Write an editset into a mip representation

Coerces a mip object into an lpsolve object

Transform a found solution into a categorical record

Decouple a set of conditional edits

Decompose a matrix or edits into independent blocks

Get condition matrix from an editset.

Determine which edits contain which variable(s)

Returns the coefficient matrix A of linear (in)equalities

Derive adjecency matrix from collection of edits

duplicated.editmatrix

Check for duplicate edit rules

editrules.plotting

Graphical representation of edits

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

Create an editmatrix with categorical variables