Moderated multiple regression (MMR) has become a fundamental tool for applied researchers, since many effects are expected to vary based on other variables. However, the inherent complexity of MMR creates formidable challenges for adequately performing power analysis on interaction effects to ensure reliable and replicable research results. Prior literature indicates interaction effects are frequently underpowered, and that researchers should attend to the power implications of subtle suppression/enhancement effects, measurement error, and range restriction, not to mention the prevalence of small effect sizes. Despite existing tools and guidance related to MMR power analysis, we have not seen a practical framework for guiding applied researchers and practitioners through this challenging process. In response, we developed an incremental model-building framework that allows for a systematic step-by-step approach to MMR power analysis in R with the "InteractionPoweR" and "simpr" packages using an R[superscript 2] change approach. Using the proposed approach, researchers ground their analysis in prior empirical research, and build sequentially more sophisticated power analyses to illuminate the intricacies of their MMR while managing cognitive complexity. We demonstrate the framework through an applied example, with full R code provided, as a resource to support applied researchers and practitioners in their study planning and decision making and to improve the empirical knowledge base. This tutorial is expected to substantially improve practices of conducting power analysis needed to test interaction effects in educational and psychological studies, as well as inspire software development to address current practical challenges in performing power analysis.