acc_distributions

Function to identify inadmissible measurements according to hard limits
(multiple variables)

Data quality assessments guided by a
'data quality framework introduced by Schmidt and colleagues, 2021'
<doi:10.1186/s12874-021-01252-7> target the
data quality dimensions integrity, completeness, consistency, and
accuracy. The scope of applicable functions rests on the
availability of extensive metadata which can be provided in
spreadsheet tables. Either standardized (e.g. as 'html5' reports) or
individually tailored reports can be generated. For an introduction
into the specification of corresponding metadata, please refer to the
'package website'
<https://dataquality.ship-med.uni-greifswald.de/Annotation_of_Metadata.html>.

Stephan Struckmann

dataquieR

Data Quality in Epidemiological Research

University Medicine Greifswald 

Elisa Kasbohm

Joany Marino

Adrian Richter

Carsten Oliver Schmidt

German Research Foundation (DFG SCHM 2744/3-1) 

European Union’s Horizon 2020 programme (euCanSHare, grant agreement No. 825903) 

acc_distributions function

<dl><dt>resp_vars</dt>
<dd>variable list the names of the measurement variables</dd>
<dt>label_col</dt>
<dd>variable attribute the name of the column in the metadata
with labels of variables</dd>
<dt>group_vars</dt>
<dd>variable list the name of the observer, device or
reader variable</dd>
<dt>study_data</dt>
<dd><a href="/link/data.frame?package=dataquieR&version=1.0.13" data-mini-rdoc="dataquieR::data.frame">data.frame</a> the data frame that contains the measurements</dd>
<dt>meta_data</dt>
<dd><a href="/link/data.frame?package=dataquieR&version=1.0.13" data-mini-rdoc="dataquieR::data.frame">data.frame</a> the data frame that contains metadata
attributes of study data</dd></dl>

Arguments

<ul>
<li>Select all variables of type float or integer in the study data</li>
<li>Remove missing codes from the study data (if defined in the metadata)</li>
<li>Remove measurements deviating from limits defined in the metadata</li>
<li>Plot histograms<ul>
<li>If group_vars is specified by the user, distributions within
group-wise ecdf are presented.</li>
</ul></li>
</ul>

ALGORITHM OF THIS IMPLEMENTATION:

Function to plot histograms added by empirical cumulative distributions
for subgroups — acc_distributions

<dl>

<dt>resp_vars</dt>
<dd>variable list the names of the measurement variables</dd>


<dt>label_col</dt>
<dd>variable attribute the name of the column in the metadata
with labels of variables</dd>


<dt>group_vars</dt>
<dd>variable list the name of the observer, device or
reader variable</dd>


<dt>study_data</dt>
<dd><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> the data frame that contains the measurements</dd>


<dt>meta_data</dt>
<dd><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> the data frame that contains metadata
attributes of study data</dd>

</dl>

acc_distributions: Function to plot histograms added by empirical cumulative distributions for subgroups

Description

Usage

Value

Arguments

ALGORITHM OF THIS IMPLEMENTATION:

See Also