The underrepresentation and underperformance of low-income, first-generation, gender minoritized, Black, Latine, and Indigenous students in Science, Technology, Engineering, and Mathematics (STEM) occurs for a variety of reasons, including, that students in these groups experience opportunity gaps in STEM classes. A critical approach to disrupting persistent inequities is implementing policies and practices that no longer systematically disadvantage students from minoritized groups. To do this, instructors must use data-informed reflection to interrogate their course outcomes. However, these data can be hard to access, process, and visualize in ways that make patterns of inequities clear. To address this need, we developed an R-Shiny application that allows authenticated users to visualize inequities in student performance. An explorable example can be found here: https://theobaldlab.shinyapps.io/visualizinginequities/. In this essay, we use publicly retrieved data as an illustrative example to detail 1) how individual instructors, groups of instructors, and institutions might use this tool for guided self-reflection and 2) how to adapt the code to accommodate data retrieved from local sources. All of the code is freely available here: https://github.com/TheobaldLab/VisualizingInequities. We hope faculty, administrators, and higher-education policymakers will make visible the opportunity gaps in college courses, with the explicit goal of creating transformative, equitable education through self-reflection, group discussion, and structured support.