R Packages for Data Quality Assessments and Data Monitoring: A Software Scoping Review with Recommendations for Future Developments  
[PERSON]  
1 Unit Quality in the Health Sciences (QIHS), Department SHIP-KEF, Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (C.O.S.) [PERSON]  
1 Unit Quality in the Health Sciences (QIHS), Department SHIP-KEF, Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (C.O.S.) [PERSON]  
1 Unit Quality in the Health Sciences (QIHS), Department SHIP-KEF, Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (C.O.S.) [PERSON]  
2 Medical Center for Information and Communication Technology, Universitatsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nurnberg (FAU), 91054 Erlangen, Germany; [EMAIL_ADDRESS]  
Footnote 1: These authors contributed equally to this work. [PERSON]  
1 Unit Quality in the Health Sciences (QIHS), Department SHIP-KEF, Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (C.O.S.) Footnote 19: email: {https:Numerous tools have been developed to facilitate and partially automate data inspections and data quality assessments (DQA), ranging from stand-alone software solutions (e.g., [8; 9; 10; 11]) to packages within existing programming environments, such as the R language (e.g., [12; 13; 14; 15; 16; 17; 18]). These developments are of great practical relevance, as they address the phases of data screening, data cleaning, and data preparation, which may even be more time-consuming than the actual statistical modeling itself [19; 20]. Of particular relevance to DQA are developments in the R language [21]. Being a freely available programming environment, R has become one of the most popular statistical data processing environments worldwide. The base distribution of R contains functionalities for many statistical procedures; additional modules or extensions (termed \"packages\") are available for multiple purposes [22], such as exploratory data analysis and DQA. A review by [PERSON] et al. [20] included fifteen R packages that facilitate automated exploratory data analysis. Package features comprised, for example, univariate and multivariate data exploration, whole dataset summaries, checks on data validity, data cleaning options, and data transformation. Nevertheless, their scope varied considerably. While exploratory data analysis overlaps with DQA, they are not identical. The former focuses on data properties and data descriptions in the broadest sense. In contrast, DQA is more concerned with measuring the \"degree to which a set of inherent characteristics of data fulfils requirements\" (ISO 8000 [23]). Thus, to perform DQA, it is not sufficient to rely on a complete data frame with study data. In addition, extensive metadata is needed to characterize further expected and required data properties (e.g., admissible values or ranges), as well as to identify deviations from the requirements [24]. Hence, software evaluation for exploratory data analysis provides only limited guidance on their ability to conduct DQA. Such a review of specific DQA capabilities of R packages is still missing. This paper, therefore, contributes to existing works in several ways. Our primary objective is to provide a structured overview of the features of active R packages for assessing DQ. The focus is on the scope of possible DQA, using a DQ framework for observational studies [6] as a point of reference and example data from a cohort study as a test on real-world data. By doing so, we aim to identify potential enhancements to both the existing packages and the DQ framework. We also target links to DQ concepts, ease of use, metadata handling, and output type. We start by describing our systematic search and defining the eligibility criteria (Section 2.1).