Package 'MOQA' reference manual

Title:	Basic Quality Data Assurance for Epidemiological Research
Description:	With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.
Authors:	Martin Bialke <[email protected]>, Thea Schwaneberg <[email protected]>, Rene Walk <[email protected]>
Maintainer:	Martin Bialke <[email protected]>
License:	AGPL-3
Version:	2.0.0
Built:	2024-11-20 06:33:16 UTC
Source:	CRAN

codelist

Description

internal data variable

Note

internal data variable

Author(s)

The MOSAIC Project, Martin Bialke

footnoteString

Description

internal data variable

Note

internal data variable

Author(s)

The MOSAIC Project, Martin Bialke

label_boxplot

Description

internal label for data variable

Note

internal label for data variable

Author(s)

The MOSAIC Project, Martin Bialke

label_description

Description

internal label for data variable

Note

internal label for data variable

Author(s)

The MOSAIC Project, Martin Bialke

label_normalverteilung

Description

internal label for data variable

Note

internal label for data variable

Author(s)

The MOSAIC Project, Martin Bialke

label_qnormplot

Description

internal label for data variable

Note

internal label for data variable

Author(s)

The MOSAIC Project, Martin Bialke

label_unit

Description

internal label for data variable

Note

internal label for data variable

Author(s)

The MOSAIC Project, Martin Bialke

labelCounts

Description

internal label for data variable

Note

internal label for data variable

Author(s)

The MOSAIC Project, Martin Bialke

labelPercentage

Description

internal label for data variable

Note

internal label for data variable

Author(s)

The MOSAIC Project, Martin Bialke

Basic Quality Data Assurance for Epidemiological Research

Description

With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.

Details

The DESCRIPTION file:

Package:	MOQA
Type:	Package
Title:	Basic Quality Data Assurance for Epidemiological Research
Version:	2.0.0
Date:	2017-06-21
Author:	Martin Bialke <[email protected]>, Thea Schwaneberg <[email protected]>, Rene Walk <[email protected]>
Maintainer:	Martin Bialke <[email protected]>
Description:	With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.
License:	AGPL-3
Depends:	psych, gplots, grid, readr
NeedsCompilation:	no
Repository:	CRAN
Packaged:	2017-06-22 07:51:50 UTC; bialkem
Date/Publication:	2017-06-22 13:23:11 UTC
Config/pak/sysreqs:	libx11-dev

Index of help topics:

MOQA.env                MOQA.env
codelist                codelist
footnoteString          footnoteString
labelCounts             labelCounts
labelPercentage         labelPercentage
label_boxplot           label_boxplot
label_description       label_description
label_normalverteilung
                        label_normalverteilung
label_qnormplot         label_qnormplot
label_unit              label_unit
moqa                    Basic Quality Data Assurance for
                        Epidemiological Research
mosaic.addFootnote      addFootnote
mosaic.beginPlot        beginPlot
mosaic.countValue       countValue
mosaic.createSimplePdfCategorical
                        createSimplePdfCategorical
mosaic.createSimplePdfCategoricalDataframe
                        createSimplePdfCategoricalDataframe
mosaic.createSimplePdfMetric
                        createSimplePdfMetric
mosaic.createSimplePdfMetricDataframe
                        createSimplePdfMetricDataframe
mosaic.finishPlot       finishPlot
mosaic.generateCategoricalPlot
                        generateCategoricalPlot
mosaic.generateMetricPlots
                        generateMetricPlots
mosaic.generateMetricTablePlot
                        generateMetricTablePlot
mosaic.getTimestamp     getTimestamp
mosaic.importToolboxSpssDataFile
                        importToolboxSpssDataFile
mosaic.info             info
mosaic.loadCsvData      loadCsvData
mosaic.preProcessCategoricalData
                        preProcessCategoricalData
mosaic.preProcessMetricData
                        preProcessMetricData
mosaic.setGlobalCodelist
                        setGlobalCodelist
mosaic.setGlobalDescription
                        setGlobalDescription
mosaic.setGlobalMissingTreshold
                        setGlobalMissingTreshold
mosaic.setGlobalUnit    setGlobalUnit
outputPrefix            outputPrefix
qualifiedMissingsTreshold
                        qualifiedMissingsTreshold

The aim of the MOQA R-Package is to provide a basic assessment of data quality and to generate a set of informative graphs. Especially, there should be no demand for the potential researcher to master R. This R-package enables researchers to generate reports for various kinds of metric and categorical data. Additionally, general reports for multivariate input data and, if needed, detailed results for single-variable data can be produced.

CSV-files as well as dataframes can be used as input format to create a report. The results are instantly saved in an automatically generated PDF-file. For each study variable within the data input file a separate PDF-file with standard or, if applicable, customized plots and tables is produced. These standard reports enable the user to monitor and report the data integrity and completeness. However, for more specific reports the knowledge of metadata is necessary, including definition of units, variables, descriptions, code lists and categories of qualified missings.

Version 1.2 ———– ADDED Support for metric and categorical dataframes BUGFIX Aborted report generation in case of non-existent missings in datacolumn

Version 2.0 ———– RENAME Official Renaming of former package-name mosaicQA to MOQA ADDED new function importToolboxSpssDataFile

Author(s)

Martin Bialke <[email protected]>, Thea Schwaneberg <[email protected]>, Rene Walk <[email protected]>

Maintainer: Martin Bialke <[email protected]>

Examples


## Example 1: Generate pdf with graphs for a single metric data column, e.g. data of body height

# load MOQA package
library('MOQA')

# specify the csv import file with metric data, use one column per variable
metric_datafile='c:/mosaic/metric_single_var.csv'

#specify output folder
outputFolder='c:/mosaic/outputs/'

#set missing threshold, optional, default is 99900
mosaic.setGlobalMissingTreshold(99900)

#set variable unit, optional
mosaic.setGlobalUnit('(cm)')

#set variable description, optional, if not uses the name of the variable is displayed in
#table heading
mosaic.setGlobalDescription('Height')

#create PDF-report,
#uncomment to start report-generation
#mosaic.createSimplePdfmetric(metric_datafile, outputFolder)



## Example 2: Generate pdf with graphs for a single categorical data column

# load MOQA package
library('MOQA')

# specify the import file with Categorical data
# first row has to contain variable names without special characters
Categorical_datafile='c:/mosaic/cat_single_var_en.csv'

#specify output folder
outputFolder='c:/mosaic/outputs/'

#set treshold to detect missings, default is 99900 (adjust this line to change this global value,
#but be careful)
mosaic.setGlobalMissingTreshold(99900)

#set description of var
mosaic.setGlobalCodelist(c('1=yes','2=no','99996=not specified','99997=not acquired'))

# create simple pdf file foreach variable column in Categorical data file,
# uncomment to start report-generation
# mosaic.createSimplePdfCategorical(Categorical_datafile,outputFolder)




## Example 3: Generate pdf with graphs for a multiple metric data columns, generates one pdf for
# each column using the variable name for table headings

# load MOQA package
library('MOQA')

# specify the import file with metric data
# use one column per variable, first row should contain variable name, following rows should
# contain data, csv Files with multiple rows are supported, decimal values should be formated
# for example : 25.4
metric_datafile='c:/mosaic/metric_multi_var.csv'

#specify output folder
outputFolder="c:/mosaic/outputs/"

# set treshold to detect missings, default is 99900 (adjust this line to change this global value
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# create PDF-Files for vars,
# uncomment to start report-generation
#mosaic.createSimplePdfmetric(metric_datafile, outputFolder)




## Example 4: Generate pdf with graphs for a multiple metric dataframe, generates one pdf for
# each column using the variable name for table headings

# load MOQA package
library('MOQA')

# specify the metric dataframe with 1-n columns, here sample data is generated
metric_data=data.frame(matrix(rnorm(20), nrow=10))

#specify output folder
outputFolder="c:/mosaic/outputs/"

# set treshold to detect missings, default is 99900 (adjust this line to change this global value
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# create PDF-Files for vars,
# uncomment to start report-generation
#mosaic.createSimplePdfMetricDataframe(metric_data, outputFolder)



## Example 5: Import data from SPSS Export file generated by Toolbox for Research
# and generate report for specific variable

# load MOQA package
library('MOQA')

# specify import dat-file
importfile="c:/mosaic/import/all_in_one.dat"

# specify output folder
outputFolder="c:/mosaic/outputs/"

# import data
#importdata=mosaic.importToolboxSpssDataFile(importfile)

# generate report for a specifc variable e.e. patient.age
# pass data as dataframe to use already given column name for a more descriptive output
#mosaic.createSimplePdfMetricDataframe(as.data.frame(importdata$ve_temperature_ear),outputFolder)

## Example 1: Generate pdf with graphs for a single metric data column, e.g. data of body height

# load MOQA package
library('MOQA')

# specify the csv import file with metric data, use one column per variable
metric_datafile='c:/mosaic/metric_single_var.csv'

#specify output folder
outputFolder='c:/mosaic/outputs/'

#set missing threshold, optional, default is 99900
mosaic.setGlobalMissingTreshold(99900)

#set variable unit, optional
mosaic.setGlobalUnit('(cm)')

#set variable description, optional, if not uses the name of the variable is displayed in
#table heading
mosaic.setGlobalDescription('Height')

#create PDF-report,
#uncomment to start report-generation
#mosaic.createSimplePdfmetric(metric_datafile, outputFolder)



## Example 2: Generate pdf with graphs for a single categorical data column

# load MOQA package
library('MOQA')

# specify the import file with Categorical data
# first row has to contain variable names without special characters
Categorical_datafile='c:/mosaic/cat_single_var_en.csv'

#specify output folder
outputFolder='c:/mosaic/outputs/'

#set treshold to detect missings, default is 99900 (adjust this line to change this global value,
#but be careful)
mosaic.setGlobalMissingTreshold(99900)

#set description of var
mosaic.setGlobalCodelist(c('1=yes','2=no','99996=not specified','99997=not acquired'))

# create simple pdf file foreach variable column in Categorical data file,
# uncomment to start report-generation
# mosaic.createSimplePdfCategorical(Categorical_datafile,outputFolder)




## Example 3: Generate pdf with graphs for a multiple metric data columns, generates one pdf for
# each column using the variable name for table headings

# load MOQA package
library('MOQA')

# specify the import file with metric data
# use one column per variable, first row should contain variable name, following rows should
# contain data, csv Files with multiple rows are supported, decimal values should be formated
# for example : 25.4
metric_datafile='c:/mosaic/metric_multi_var.csv'

#specify output folder
outputFolder="c:/mosaic/outputs/"

# set treshold to detect missings, default is 99900 (adjust this line to change this global value
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# create PDF-Files for vars,
# uncomment to start report-generation
#mosaic.createSimplePdfmetric(metric_datafile, outputFolder)




## Example 4: Generate pdf with graphs for a multiple metric dataframe, generates one pdf for
# each column using the variable name for table headings

# load MOQA package
library('MOQA')

# specify the metric dataframe with 1-n columns, here sample data is generated
metric_data=data.frame(matrix(rnorm(20), nrow=10))

#specify output folder
outputFolder="c:/mosaic/outputs/"

# set treshold to detect missings, default is 99900 (adjust this line to change this global value
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# create PDF-Files for vars,
# uncomment to start report-generation
#mosaic.createSimplePdfMetricDataframe(metric_data, outputFolder)



## Example 5: Import data from SPSS Export file generated by Toolbox for Research
# and generate report for specific variable

# load MOQA package
library('MOQA')

# specify import dat-file
importfile="c:/mosaic/import/all_in_one.dat"

# specify output folder
outputFolder="c:/mosaic/outputs/"

# import data
#importdata=mosaic.importToolboxSpssDataFile(importfile)

# generate report for a specifc variable e.e. patient.age
# pass data as dataframe to use already given column name for a more descriptive output
#mosaic.createSimplePdfMetricDataframe(as.data.frame(importdata$ve_temperature_ear),outputFolder)

MOQA.env

Description

local environment to handle MOQA-internal variables

Note

local environment

Author(s)

The MOSAIC Project, Martin Bialke

addFootnote

Description

Add a Footnote to plot using footnotestring and current timestamp.

Usage

mosaic.addFootnote()
mosaic.addFootnote()

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

beginPlot

Description

begin plotting the configured graphs for loaded data and generate the output PDF-File.

Usage

mosaic.beginPlot(varname,outputfolder)
mosaic.beginPlot(varname,outputfolder)

Arguments

`varname`	name of the studyitem or csv column loaded to plot graphs for.
`outputfolder`	name of the output folder

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

countValue

Description

Count occurrence of search value in data column

Usage

mosaic.countValue(searchvalue, data_column)
mosaic.countValue(searchvalue, data_column)

Arguments

`searchvalue`	value to search for
`data_column`	name of study item or data column to search in

Details

useful to find qualified missings in data column

Value

count of occurences of specified value in specified data column

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

createSimplePdfCategorical

Description

Create simple PDF-file for categorical data

Usage

mosaic.createSimplePdfCategorical(inputfile, outputfolder)
mosaic.createSimplePdfCategorical(inputfile, outputfolder)

Arguments

`inputfile`	path to input csv-file
`outputfolder`	path to output folder

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

Examples


# load MOQA package
library('MOQA')

# specify the import file with categorial data
# first row has to contain variable names without special characters
categorial_datafile='c:/mosaic/cat_single_var_en.csv'

# specify output folder
outputFolder='c:/mosaic/outputs/'

# set treshold to detect missings, default is 99900 (adjust this line to change this global value, 
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# set description of var
mosaic.setGlobalCodelist(c('1=yes','2=no','99996=not specified','99997=not acquired'))

# create simple pdf file foreach variable column in categorial data file, uncomment to start 
# report-generation
# mosaic.createSimplePdfCategorical(categorial_datafile,outputFolder)
# load MOQA package
library('MOQA')

# specify the import file with categorial data
# first row has to contain variable names without special characters
categorial_datafile='c:/mosaic/cat_single_var_en.csv'

# specify output folder
outputFolder='c:/mosaic/outputs/'

# set treshold to detect missings, default is 99900 (adjust this line to change this global value, 
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# set description of var
mosaic.setGlobalCodelist(c('1=yes','2=no','99996=not specified','99997=not acquired'))

# create simple pdf file foreach variable column in categorial data file, uncomment to start 
# report-generation
# mosaic.createSimplePdfCategorical(categorial_datafile,outputFolder)

createSimplePdfCategoricalDataframe

Description

Create simple PDF-file for categorical data

Usage

mosaic.createSimplePdfCategoricalDataframe(df, outputfolder)
mosaic.createSimplePdfCategoricalDataframe(df, outputfolder)

Arguments

`df`	dataframe
`outputfolder`	path to output folder

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

createSimplePdfMetric

Description

Create simple PDF-file for metric data

Usage

mosaic.createSimplePdfMetric(inputfile, outputfolder)
mosaic.createSimplePdfMetric(inputfile, outputfolder)

Arguments

`inputfile`	path to input csv file
`outputfolder`	path to output folder

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

Examples

# load MOQA package
library('MOQA')

# specify the csv import file with metric data, use one column per variable
metric_datafile='c:/mosaic/metric_single_var.csv'

#specify output folder
outputFolder='c:/mosaic/output/'

#set missing threshold, optional, default is 99900
mosaic.setGlobalMissingTreshold(99900)

#set variable unit, optional
mosaic.setGlobalUnit('(cm)')

#set variable description, optional
mosaic.setGlobalDescription('Height')

#create PDF-report, uncomment to start report-generation
#mosaic.createSimplePdfMetric(metric_datafile, outputFolder)
# load MOQA package
library('MOQA')

# specify the csv import file with metric data, use one column per variable
metric_datafile='c:/mosaic/metric_single_var.csv'

#specify output folder
outputFolder='c:/mosaic/output/'

#set missing threshold, optional, default is 99900
mosaic.setGlobalMissingTreshold(99900)

#set variable unit, optional
mosaic.setGlobalUnit('(cm)')

#set variable description, optional
mosaic.setGlobalDescription('Height')

#create PDF-report, uncomment to start report-generation
#mosaic.createSimplePdfMetric(metric_datafile, outputFolder)

createSimplePdfMetricDataframe

Description

Create simple PDF-file for metric data

Usage

mosaic.createSimplePdfMetricDataframe(df, outputfolder)
mosaic.createSimplePdfMetricDataframe(df, outputfolder)

Arguments

`df`	path to input csv file
`outputfolder`	path to output folder

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

Examples

# load MOQA package
library('MOQA')

# specify the metric dataframe with 1-n columns, here sample data is generated
metric_data=data.frame(matrix(rnorm(20), nrow=10))

#specify output folder
outputFolder="c:/mosaic/outputs/"

# set treshold to detect missings, default is 99900 (adjust this line to change this global value
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# create PDF-Files for vars, 
# uncomment to start report-generation
#mosaic.createSimplePdfMetricDataframe(metric_data, outputFolder)
# load MOQA package
library('MOQA')

# specify the metric dataframe with 1-n columns, here sample data is generated
metric_data=data.frame(matrix(rnorm(20), nrow=10))

#specify output folder
outputFolder="c:/mosaic/outputs/"

# set treshold to detect missings, default is 99900 (adjust this line to change this global value
# but be careful)
mosaic.setGlobalMissingTreshold(99900)

# create PDF-Files for vars, 
# uncomment to start report-generation
#mosaic.createSimplePdfMetricDataframe(metric_data, outputFolder)

finishPlot

Description

Finish plotting, close PDF-file

Usage

mosaic.finishPlot()
mosaic.finishPlot()

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

generateCategoricalPlot

Description

Generate Statistics and Create plots for categorical data

Usage

mosaic.generateCategoricalPlot(dataframe, varname)
mosaic.generateCategoricalPlot(dataframe, varname)

Arguments

`dataframe`	data table with one or more columns (first row should contain column names/study item names/variable names)
`varname`	selected column/study item/variable to plot graph for

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

generateMetricPlots

Description

calculate statistics and generate graphs for metric data

Usage

mosaic.generateMetricPlots(data_snippet, var_name)
mosaic.generateMetricPlots(data_snippet, var_name)

Arguments

`data_snippet`	data table with one or more columns (first row should contain column names/study item names/variable names)
`var_name`	selected column/study item/variable to plot graph for

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

generateMetricTablePlot

Description

Generate missing-ratio table for metric data (data, num of columns, column index, varname)

Usage

mosaic.generateMetricTablePlot(data, num_of_columns, index, varname)
mosaic.generateMetricTablePlot(data, num_of_columns, index, varname)

Arguments

`data`	preprocessed data frame including 'valid value markers'
`num_of_columns`	absolute number of to be processed data columns
`index`	current column to be processed
`varname`	current name of variable to be used in table heading

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

getTimestamp

Description

get a current timestamp formatted as %Y_%m_%d_%H%M%S

Usage

mosaic.getTimestamp()
mosaic.getTimestamp()

Value

timestamp, e.g. '2016_09_09_143458'

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

importToolboxSpssDataFile

Description

load dat-file from 'toolbox for resarch' spss export with tab-separator with n columns to dataframe

Usage

mosaic.importToolboxSpssDataFile(filename)
mosaic.importToolboxSpssDataFile(filename)

Arguments

filename

filename or a complete path to a dat-file

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

info

Description

MOSAIC Information

Usage

mosaic.info()
mosaic.info()

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

loadCsvData

Description

Load data from csv-file is one or more columns. first row should contain the name of the study item, e.g. 'height'

Usage

mosaic.loadCsvData(filename)
mosaic.loadCsvData(filename)

Arguments

filename

filename or a complete path to a file

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

preProcessCategoricalData

Description

Identify unique values in data column, get absolute, percentage and cumulative statistics

Usage

mosaic.preProcessCategoricalData(data)
mosaic.preProcessCategoricalData(data)

Arguments

data

data frame to be processed containing categorical data

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

preProcessMetricData

Description

Pre-process metric data to allow missing-ratio table

Usage

mosaic.preProcessMetricData(data)
mosaic.preProcessMetricData(data)

Arguments

data

data frame to be preprocessed containing metric data

Note

Function call type: internal

Author(s)

The MOSAIC Project, Martin Bialke

setGlobalCodelist

Description

set and parse a global code list for categorical data to be used in categorical plot descriptions

Usage

mosaic.setGlobalCodelist(coding)
mosaic.setGlobalCodelist(coding)

Arguments

coding

list of code and value pairs, see example for details

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

Examples

mosaic.setGlobalCodelist(c('1=yes','2=no', '99996=no information'))
mosaic.setGlobalCodelist(c('1=yes','2=no', '99996=no information'))

setGlobalDescription

Description

Set Global Description for variable User (description) data. especially useful when plotting graphs for a selected data column

Usage

mosaic.setGlobalDescription(value)
mosaic.setGlobalDescription(value)

Arguments

value

string value to be used as study item description, e.g. 'waist circumference'

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

Examples

mosaic.setGlobalDescription('waist circumference')
mosaic.setGlobalDescription('waist circumference')

setGlobalMissingTreshold

Description

Set Global Threshold for Missings , e.g. 99000

Usage

mosaic.setGlobalMissingTreshold(value)
mosaic.setGlobalMissingTreshold(value)

Arguments

value

threshold to separate missings from valid values

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

Examples

mosaic.setGlobalMissingTreshold(99000)
mosaic.setGlobalMissingTreshold(99000)

setGlobalUnit

Description

Set Global Unit Label to be used User in graphs, e.g. '(cm)'

Usage

mosaic.setGlobalUnit(value)
mosaic.setGlobalUnit(value)

Arguments

value

unit string to be used in graphs

Note

Function call type: user

Author(s)

The MOSAIC Project, Martin Bialke

Examples

mosaic.setGlobalUnit('(cm)')
mosaic.setGlobalUnit('(cm)')

outputPrefix

Description

internal data variable

Note

internal data variable

Author(s)

The MOSAIC Project, Martin Bialke

qualifiedMissingsTreshold

Description

internal data variable

Note

internal data variable

Author(s)

The MOSAIC Project, Martin Bialke

Package 'MOQA'

Help Index

codelist

Description

Note

Author(s)

footnoteString

Description

Note

Author(s)

label_boxplot

Description

Note

Author(s)

label_description

Description

Note

Author(s)

label_normalverteilung

Description

Note

Author(s)

label_qnormplot

Description

Note

Author(s)

label_unit

Description

Note

Author(s)

labelCounts

Description

Note

Author(s)

labelPercentage

Description

Note

Author(s)

Basic Quality Data Assurance for Epidemiological Research

Description

Details

Author(s)

See Also

Examples

MOQA.env

Description

Note

Author(s)

addFootnote

Description

Usage

Note

Author(s)

beginPlot

Description

Usage

Arguments

Note

Author(s)

countValue

Description

Usage

Arguments

Details

Value

Note

Author(s)

createSimplePdfCategorical

Description

Usage

Arguments

Note

Author(s)

Examples

createSimplePdfCategoricalDataframe

Description

Usage

Arguments

Note

Author(s)