util_dichotomize

use the metadata attribute <code>RECODE</code> (="recode") to
dichotomize the data

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_dichotomize function

<dl><dt>study_data</dt>
<dd>study data including jump/missing codes as specified in the
code conventions</dd>
<dt>meta_data</dt>
<dd>metadata as specified in the code conventions</dd>
<dt>label_col</dt>
<dd>variable attribute the name of the column in the metadata
with labels of variables</dd></dl>

Arguments

utility function to dichotomize variables — util_dichotomize

<dl>

<dt>study_data</dt>
<dd>study data including jump/missing codes as specified in the
code conventions</dd>


<dt>meta_data</dt>
<dd>metadata as specified in the code conventions</dd>


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

</dl>

utility function to dichotomize variables

util_dichotomize: utility function to dichotomize variables

Description

Usage

Arguments