Data Quality Assessment Tools
Check Missing Data Item-wise with Dependency Logic
Check Missing Data by Record (Unit Check)
Check Missing Data by Segments
Perform Concordance Check on Data Based on Defined Rules
Perform Conformance Check on Data Based on Defined Rules
Default Conformance Check Rules (Reference)
Validate Data Against Correctness Rules
Perform Currency Check for Data Frame Columns
Perform plausibility Check for Data Frame Columns
Perform timeliness Check for Data Frame Columns
In the context of data quality assessment, this package provides a number of functions for evaluating data quality across various dimensions, including completeness, plausibility, concordance, conformance, currency, timeliness, and correctness. It has been developed based on two well-known frameworks—Michael G. Kahn (2016) <doi: 10.13063/2327-9214.1244> and Nicole G. Weiskopf (2017) <doi: 10.5334/egems.218>—for data quality assessment. Using this package, users can evaluate the quality of their datasets, provided that corresponding metadata are available.