util_observations_in_subgroups

This function uses <code>!is.na</code> to count the number of non-missing observations in subgroups of
the data (list) and in a set of user defined response variables. In some applications it is
required that the number of observations per e.g. factor level is higher than a user-defined
minimum number.

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.qihs.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, SCHM 2744/9-1, SCHM 2744/3-4) 

National Research Data Infrastructure for Personal Health Data: (NFDI 13/1) 

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

util_observations_in_subgroups function

<dl><dt>x</dt>
<dd>data frame</dd>
<dt>rvs</dt>
<dd>variable names</dd></dl>

Arguments

Utility function observations in subgroups — util_observations_in_subgroups

<dl>

<dt>x</dt>
<dd>data frame</dd>


<dt>rvs</dt>
<dd>variable names</dd>

</dl>

Utility function observations in subgroups

util_observations_in_subgroups: Utility function observations in subgroups

Description

Usage

Value

Arguments