128 GoFiguR: A web-based app for creating and modifying figures

Objectives/Goals: The goal was to create and deploy an intuitive, easy-to-use tool that clinical investigators can use to make or modify high-quality figures for publication. Methods/Study Population: We developed GoFiguR, an interactive application built with RShiny, to help researchers and analysts visualize their data with ease. Users upload data files, choose a plot type (histogram, scatterplot, boxplot, bar plot, or Kaplan-Meier curve), and then specify variables, labels, and font sizes to generate the figure. Alternatively, users can upload a ggplot2 object to modify labels, font sizes, or plot themes in an existing figure. All figures can be exported either as ggplot2 object for additional modification or as one of multiple file formats with user-defined dimensions and resolution. GoFiguR is hosted on a secure, university-maintained website with access limited to university personnel. Instructional step-by-step handouts were developed to assist investigators in the use of the app. Results/Anticipated Results: We plan to integrate GoFiguR into the biostatistics consultative services offered by our Clinical and Translational Science Program. In practice, we anticipate several use cases: investigators and analysts can use GoFiguR to generate mock figures during project planning, analysts can create visualizations following data analysis, and investigators can apply final edits to figures prior to dissemination. Through this collaborative process, investigators are expected to gain a deeper understanding of appropriate figure types for various data visualizations. Discussion/Significance of Impact: Creating publication-quality figures is often a time-intensive process requiring multiple iterations, between the investigator and analyst. GoFiguR enables more efficient development of figures as investigators can directly modify figures to their specification.