MOQA {MOQA}

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 <[e-mail]>, Thea Schwaneberg <[e-mail]>, Rene Walk <[e-mail]>
Maintainer:	Martin Bialke <[e-mail]>
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

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 <[e-mail]>, Thea Schwaneberg <[e-mail]>, Rene Walk <[e-mail]>

Maintainer: Martin Bialke <[e-mail]>

See Also

mosaic-greifswald.de

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)