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dataMaid (version 1.1.2)

A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process

Description

Data screening is an important first step of any statistical analysis. dataMaid autogenerates a customizable data report with a thorough summary of the checks and the results that a human can use to identify possible errors. It provides an extendable suite of test for common potential errors in a dataset.

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Install

install.packages('dataMaid')

Monthly Downloads

1,211

Version

1.1.2

License

GPL-2

Issues

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Repository

https://github.com/ekstroem/dataMaid

Maintainer

Claus Ekstrom

Last Published

May 3rd, 2018

Functions in dataMaid (1.1.2)

defaultFactorChecks

Default checks for factor variables

Produce distribution plots in the base R (graphics) style using plot and barplot

identifyMissing

A checkFunction for identifying miscoded missing values.

defaultFactorSummaries

Default summary functions for factor variables

defaultCharacterSummaries

Default summary functions for character variables

Extract the contents of the attribute description

identifyCaseIssues

A checkFunction for identifying case issues

defaultNumericSummaries

Default summary functions for numeric variables

Check if a variable only contains a single value

defaultDateChecks

Default checks for Date variables

Summarize a variable/dataset

Produce distribution plots using ggplot from ggplot2.

Check if a variable has a class supported by dataMaid

defaultIntegerChecks

Default checks for integer variables

Check if a variable consists of Danish CPR numbers

Example data with zero-inflated variables

messageGenerator

Produce a message for the output of a checkFunction

summaryFunction for minimum and maximum

Check if a variable qualifies as a key

summaryFunction

Create an object of class summaryFunction

Semi-artificial data about the US presidents

summaryFunction for quartiles

Create object of class summaryResult

defaultLogicalSummaries

Default summary functions for logical variables

Set visual arguments for makeDataReport

Set summary arguments for makeDataReport

Create an object of class visualFunction

Produce distribution plots

defaultNumericChecks

Default checks for numeric variables

Create object of class checkResult

identifyWhitespace

A checkFunction for identifying whitespace

identifyOutliersTBStyle

A checkFunction for identifying outliers Turkey Boxstole style

defaultLabelledSummaries

Default summary functions for labelled variables

Set check arguments for makeDataReport

Simplified Rmarkdown rendering

summaryFunction for unique values

identifyOutliers

A checkFunction for identifying outliers

Summary function for original class

defaultLogicalChecks

Default checks for logical variables

A checkFunction

Produce a data codebook

Produce a data report

Small example data to show the features of dataMaid

Extended example data to test the features of dataMaid

allCheckFunctions

Overview of all available checkFunctions

basicVisualCFLB

importFrom stats na.omit

Vector of all variable classes in dataMaid

summaryFunction for central values

allVisualFunctions

Overview of all available visualFunctions

bigPresidentData

Semi-artificial data about the US presidents (extended version)

Semi-artificial data about masterpieces of art

Extract the contents of the attribute classes

defaultDateSummaries

Default summary functions for Date variables

Produce a data cleaning overview document (deprecated version)

allSummaryFunctions

Overview of all available summaryFunctions

Perform checks of potential errors in variable/dataset

Create an object of class checkFunction

Summary function for missing values

defaultIntegerSummaries

Default summary functions for integer variables

defaultLabelledChecks

Default checks for labelled variables

defaultCharacterChecks

Default checks for character variables

A checkFunction for identifying sparsely represented values (loners)