As the volume of data and technical complexity of large-scale analysis increases, many domain experts desire a computational powerful but still familiar analysis interface to fully participate in the analysis workflow by just focusing on individual datasets, leaving the large-scale computation to the system. Towards this goal, we discuss Divide-and-Conquer strategies that can help domain experts perform large-scale simulations by scaling up their analysis code written in R, the most popuar data science and interactive analysis language. We then proceed to implementing the Divide-and-Conquer strategies as a dental imaging analysis framework, VisRden, that uses R as the analysis language, allowing advanced users to provide custom R scripts and variables to be fully embedded into the large-scale analysis workflow in R. VisRden can divide large-scale image processing tasks and conquer 3D reconstruction tasks with SGE (Sun Grid Engine) array jobs and R. Image-based operations and result aggregations are scheduled as array jobs in a parallel means to accelerate the knowledge discovery process. All these combine to provide a new analytics workflow for performing similar large-scale analysis loops where expert users only need to focus on the Divide-and-Conquer tasks with the domain knowledge.