daiquiri: Data Quality Reporting for Temporal Datasets

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

See also

Useful links:

• https://github.com/ropensci/daiquiri

• https://ropensci.github.io/daiquiri/

• Report bugs at https://github.com/ropensci/daiquiri/issues

Author

Maintainer: T. Phuong Quan phuong.quan@ndm.ox.ac.uk (ORCID)

Other contributors:

• Jack Cregan [contributor]

• University of Oxford [copyright holder]

• National Institute for Health Research (NIHR) [funder]

• Brad Cannell [reviewer]