R Packages for Data Quality Assessments and Data Monitoring: A Software Scoping Review with Recommendations for Future Developments

Data quality assessments (DQA) are necessary to ensure valid research results. Despite the growing availability of tools of relevance for DQA in the R language, a systematic comparison of their functionalities is missing. Therefore, we review R packages related to data quality (DQ) and assess their...

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Vydáno v:Applied sciences Ročník 12; číslo 9; s. 4238
Hlavní autoři: Mariño, Joany, Kasbohm, Elisa, Struckmann, Stephan, Kapsner, Lorenz A., Schmidt, Carsten O.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2022
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ISSN:2076-3417, 2076-3417
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Abstract Data quality assessments (DQA) are necessary to ensure valid research results. Despite the growing availability of tools of relevance for DQA in the R language, a systematic comparison of their functionalities is missing. Therefore, we review R packages related to data quality (DQ) and assess their scope against a DQ framework for observational health studies. Based on a systematic search, we screened more than 140 R packages related to DQA in the Comprehensive R Archive Network. From these, we selected packages which target at least three of the four DQ dimensions (integrity, completeness, consistency, accuracy) in a reference framework. We evaluated the resulting 27 packages for general features (e.g., usability, metadata handling, output types, descriptive statistics) and the possible assessment’s breadth. To facilitate comparisons, we applied all packages to a publicly available dataset from a cohort study. We found that the packages’ scope varies considerably regarding functionalities and usability. Only three packages follow a DQ concept, and some offer an extensive rule-based issue analysis. However, the reference framework does not include a few implemented functionalities, and it should be broadened accordingly. Improved use of metadata to empower DQA and user-friendliness enhancement, such as GUIs and reports that grade the severity of DQ issues, stand out as the main directions for future developments.
AbstractList Data quality assessments (DQA) are necessary to ensure valid research results. Despite the growing availability of tools of relevance for DQA in the R language, a systematic comparison of their functionalities is missing. Therefore, we review R packages related to data quality (DQ) and assess their scope against a DQ framework for observational health studies. Based on a systematic search, we screened more than 140 R packages related to DQA in the Comprehensive R Archive Network. From these, we selected packages which target at least three of the four DQ dimensions (integrity, completeness, consistency, accuracy) in a reference framework. We evaluated the resulting 27 packages for general features (e.g., usability, metadata handling, output types, descriptive statistics) and the possible assessment’s breadth. To facilitate comparisons, we applied all packages to a publicly available dataset from a cohort study. We found that the packages’ scope varies considerably regarding functionalities and usability. Only three packages follow a DQ concept, and some offer an extensive rule-based issue analysis. However, the reference framework does not include a few implemented functionalities, and it should be broadened accordingly. Improved use of metadata to empower DQA and user-friendliness enhancement, such as GUIs and reports that grade the severity of DQ issues, stand out as the main directions for future developments.
Author Struckmann, Stephan
Mariño, Joany
Kapsner, Lorenz A.
Schmidt, Carsten O.
Kasbohm, Elisa
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  orcidid: 0000-0001-5266-9396
  surname: Schmidt
  fullname: Schmidt, Carsten O.
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crossref_primary_10_1002_wics_70025
crossref_primary_10_1136_bmjoq_2023_002310
crossref_primary_10_1371_journal_pone_0295726
crossref_primary_10_1007_s13222_024_00477_2
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SubjectTerms Automation
Data analysis
data quality
data quality monitoring
data reporting
exploratory data analysis
initial data analysis
Metadata
R project for statistical computing
Search strategies
Software
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Title R Packages for Data Quality Assessments and Data Monitoring: A Software Scoping Review with Recommendations for Future Developments
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Volume 12
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