Research on data use and school Early Warning Systems (EWS) notes a central practice of researchers and practitioners is to search for patterns in student data to predict outcomes so schools can support success when students experience challenges. Yet, the domain lacks a means to visualize the rich longitudinal data that schools collect. Here, we use visual data analytic hierarchical cluster analysis (HCA) heatmaps to pattern and visualize entire longitudinal grading histories of a national sample of n=14,290 students from grade 9 to college in every enrolled subject and year, visualizing 6,728,920 individual datapoints. We provide both the open access code in R and an open-access online tool allowing anyone to upload their data and create a HCA heatmap, providing support for visual data analytic and data science practice for both education researchers and schooling organizations.