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Mouse tracking and the recording of movement trajectories have become powerful tools to investigate cognitive processes. Dedicated analysis software for this type of data is now readily available to empirical researchers, promising a substantial simplification of the required data processing tasks. However, existing solutions are designed for specific recording software or analysis workflows, thus lacking the flexibility to adapt analyses to individual needs. The R package mousetRajectory addresses this gap. By placing strong emphasis on code clarity and modularity, it facilitates customization for researchers, especially those favoring a modern tidyverse programming style. Here, we provide example analyses that explain the essential preprocessing tools, such as time normalization or resampling, and key functions for computing trajectory markers. These markers include classic spatial metrics such as area under the curve and maximum absolute deviation along with more advanced measures such as sample entropy. In summary, mousetRajectory offers a toolkit for researchers seeking a lightweight and easily adaptable foundation for custom analyses of 2D movement trajectory data.