Automated Data Quality Assessments for Observational Studies with dataquieR
Channel: R Consortium
Date: 2023-08-03

Elisa Kasbohm, Biomathematician at University Medicine Greifswald & Joany Mariño, Biomathematician at University Medicine Greifswald

The quality of medical research data can be affected by various sources, ranging from erroneous measurement devices to selective drop-out of participants. For this reason, data quality must be routinely assessed, which requires efficient and comprehensive tools. dataquieR enables to assessment of the quality of research data collections, especially for observational studies. The scope of checks is based on the data quality framework by Schmidt et al. (2021) and depends on the available metadata. The metadata describes properties of single variables (e.g., range of numeric values, codes for missing values), combinations of variables (e.g., contradictions, multivariate outliers) or information about examinations and the entire study (e.g., number of participants). This information can be simply provided in spreadsheets. Extensive data quality reports can then be generated with a single command call and viewed interactively in a browser. Each report output is reproducible, as it is possible to extract code snippets from the report to re-run specific checks. dataquieR can be installed from CRAN and more information is provided on our website (https://dataquality.ship-med.uni-greifswald.de).

PDF: https://static.sched.com/hosted_files/rmed2023a/49/R-Med-dataquieR.pdf 

Main Sections

0:00 Introduction
1:11 DataquieR: A quick intro
2:50 DataquieR: Metadata setup
6:38 DataquieR: Examples for data quality checks (I)
8:46 DataquieR: Examples for data quality checks (II)
9:45 DataquieR: Examples for data quality checks (III)
10:30 DataquieR: Examples for data quality checks (IV)
11:31 DataquieR: Examples for data quality checks (V)
13:22 DataquieR: Creating reports 
14:11 Thank you and Q&A

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