Research and Exploratory Analysis Driven-Time-data Visualization (read-tv) software.

MOTIVATION

Research & Exploratory Analysis Driven Time-data Visualization () is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes.

MATERIALS AND METHODS

is a graphical application, and the main component of a package of the same name. generates and evaluates code to filter and visualize data. Users can view the visualization code from within the application, which facilitates reproducibility. The data input requirements are simple, a table with a time column with no missing values. The input can either be in the form of a file, or an in-memory dataframe- which is effective for rapid visualization during curation.

RESULTS

We used to automatically detect surgical disruption cascades. We found that the most common disruption type during a cascade was training, followed by equipment.

DISCUSSION

fills a need for visualization software of surgical disruptions and other longitudinal data. Every visualization is , the exact source code that executes to create a visualization is available from within the application. is , it can plot any tabular dataset given the simple requirements that there is a numeric, datetime, or datetime string column with no missing values. Finally, the tab-based architecture of is easily , it is relatively simple to add new functionality by implementing a tab in the source code.

CONCLUSION

enables quick identification of patterns through customizable longitudinal plots; faceting; CPA; and user-specified filters. The package is available on GitHub under an MIT license.