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Description

Data Quality in Epidemiological Research.

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>.

dataquieR

minimal Rversion PipelineStatus Coverage CRAN-Version CRAN-Downloads Project Status: Active – The project has reached a stable, usablestate and is being activelydeveloped. Lifecycle license DOI

The goal of dataquieR is to provide functions for assessing data quality issues in studies, that can be used alone or in a data quality pipeline. dataquieR also implements one generic pipeline producing flexdashboard based HTML5 reports.

See also

https://dataquality.qihs.uni-greifswald.de


Installation

You can install the released version of dataquieR from CRAN with:

install.packages("dataquieR")

The suggested packages can be directly installed by:

install.packages("dataquieR", dependencies = TRUE)

The developer version from GitLab.com can be installed using:

if (!requireNamespace("devtools")) {
  install.packages("devtools")
}
devtools::install_gitlab("libreumg/dataquier")

For examples and additional documentation, please refer to our website.

Suggested packages

dataquieR reports can now use plotly if installed. That means that, in the final report, you can zoom in the figures and get information by hovering on the points, etc. To install plotly type:

install.packages("plotly")

References

Funding – see also here

Metadata

Version

2.1.0

License

Unknown

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