While automated quality control is a key component of modern data processing workflows, visual review by a trained eye can further ensure data quality. In particular, graphical representations allow analysts to quickly review data in context. Such review has relied on specialized, often costly software or inefficient processing with spreadsheet software. The interactive graphing capabilities provided by the R Shiny package presents an opportunity to explore and interact with data in practical and user-friendly ways that are relatively simple to implement and flexible to the needs of particular datasets.nnUsing Shiny, we developed data quality control dashboards to facilitate analyst review of ambient air quality and meteorological datasets such as criteria pollutant concentrations, wind profiles, and weather forecasts. These dashboards allow analysts to systematically visualize and validate data using a straightforward user interface, and interactively mark data points that are suspect or invalid. The dashboards log all validation activities, display multiple plots to allow comparison among parameters or data sources, and support collaboration among multiple analysts when deployed to Shiny Server. In this presentation, we will provide an overview of these dashboards, highlighting useful features and the functions used we used to create them.
