Although there are established graphics that accompany the most commons functional data analyses, generating these graphics can be cumbersome and time consuming. Often, the barriers to visualization inhibit useful exploratory data analyses and prevent the development of intuition for a method and its application to a particular dataset. The refund.shiny R package was developed to address these issues for several of the most common methods for functional data. After conducting an analysis, the plot_shiny() function is used to generate an interactive visualization environment that contains several distinct graphics, many of which are updated in response to user input. These visualizations reduce the burden of exploratory analyses and can serve as a useful tool for the communication of results to non-statisticians.