dataquieR

The goal of dataquieR is to provide functions for assessing data quality issues across four dimensions: data integrity (e.g., data type errors or duplicates), completeness (e.g., missing values), consistency (e.g., range violations or contradictions), and accuracy (e.g., time trends or examiner effects). It can be applied to any tabular data, including population-based cohort studies, registries, and electronic health record (EHR) data. It can be used alone or in a data quality pipeline. dataquieR also implements one generic pipeline producing htmltools-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.

dataquieR usage questionnaire

To help us improve dataquieR, we invite you to provide your feedback by completing this short survey (English or German version).

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")

To install all suggested packages, run:

prep_check_for_dataquieR_updates()

This command can also check for new beta releases of dataquieR from our own server, so not from CRAN:

prep_check_for_dataquieR_updates(beta = TRUE)

Hint If you are running dataquieR in an un-trusted setting, namely, inside a server application, please consider disabling the import of R-serialization files to prevent users from importing RData (or RDS or even R) files, that trigger code execution on your machine, see, e.g., Ivan Krylov’s blog for the reason:

# prevent rio from reading potentially code-containing files 
options(rio.import.trust = FALSE)

If you do so, the example data won’t be loaded any more.

If you are using a version >= 2.0.0 of rio, this will be the default, so for running our examples, then, you’ll have to trust our files by using e.g. withr::with_options(list(rio.import.trust = FALSE), prep_get_data_frame("study_data")) for loading our example study data into the data-frame cache, initially and trusting our files loaded from

• https://dataquality.qihs.uni-greifswald.de/extdata/study_data.RData
• https://dataquality.qihs.uni-greifswald.de/extdata/meta_data.RData
• https://dataquality.qihs.uni-greifswald.de/extdata/ship_meta.RDS
• https://dataquality.qihs.uni-greifswald.de/extdata/ship_subset1.RDS
• https://dataquality.qihs.uni-greifswald.de/extdata/ship_subset2.RDS
• https://dataquality.qihs.uni-greifswald.de/extdata/ship_subset3.RDS
• https://dataquality.qihs.uni-greifswald.de/extdata/ship.RDS

References

• Software Paper
• Software Paper
• Data Quality Concept Paper
• Data Quality Concept and Software Web Site

Funding – see also here

• German Research Foundation (https://www.dfg.de/) (DFG: SCHM 2744/3–1 – initial concept and dataquieR development, SCHM 2744/9-1 – NFDI Task Force COVID-19 use case application; SCHM 2744/3-4 – concept extensions, ongoing)

• European Union’s Horizon 2020 research and innovation program: euCanSHare, grant agreement No. 825903 – dataquieR refinements and implementations in the Square2 web application.

• National Research Data Infrastructure for Personal Health Data: NFDI 13/1 – extension based on revised metadata concept, ongoing.

• German National Cohort (NAKO Gesundheitsstudie) NAKO (https://nako.de/): BMBF (https://www.bmbf.de/): 01ER1301A and 01ER1801A