VisualEasier is a user-friendly application designed to improve the visualization and interpretation of spatially resolved geochemical data. Developed in R environment with the Shiny package, it provides a dynamic interface that allows users, regardless of programming experience, to generate high-reso-lution elemental maps, apply image filters, and perform clustering analysis. The application supports datasets from varied analytical techniques, such as SEM-EDS, Electron Microprobe, Micro-XRF, and LA-ICP-MS, as long as the input is as comma-separated values with pixel-level positional reference. Users can produce elemental maps with pixel-by-pixel normalization, generate ternary RGB com-positions to explore elemental associations, and apply median or gradient filters to enhance visual features such as grain boundaries or reduce noise. The clustering module uses the k-means algorithm to organize the sample into user-defined compositional groups, returning a set of outputs that includes spatial maps, statistical tables, and boxplots for each cluster. All graphical findings can be impor-ted as high-quality vectorized PDF files. The system’s flexibility allows users to input normalization references, color palettes, and visualization scales. Application on real samples demonstrated the effectiveness of the application in uncovering compositional patterns that are not easily identifiable using conventional static plots. The clustering results provided insights into mineralogical domains and potential zoning within the sample, while the filter tools improved the clarity of textural and chemi-cal boundaries. The combination of interactive features, compatibility with multiple data sources, and customizable outputs makes VisualEasier a versatile tool for geologists, mineralogists, and materials scientists looking to improve data interpretation. The application is particularly valuable in exploratory and research environments where rapid feedback and visual diagnostics play a critical role in decision--making and hypothesis generation.
