The retention of college students is an important problem that may be analyzed by computing techniques, such as data mining, to identify students who may be at risk of dropping out. The importance of the problem has grown due to institutions’ requirement of meeting legislative retention mandates, face budget shortfalls due to decreased tuition or state-based revenue, and fall short of producing enough graduates in fields of need, such as computing. While data mining techniques were applied with some success, this article aims to show how R can be used to develop a hybrid methodology to enable rules to be created for the minority class with coverage and accuracy range which were not available as per existing literature. A multiple stage decision methodology (MSDM) used data mining techniques for extracting rules from an institution’s student data set to enable administrators to identify at risk students. The data mining techniques included partial decisions trees, K-means clustering, and Apriori association mining to be implemented in R. MSDM was able to identify students with up to 89% accuracy on student datasets, where the number of at risk students was fewer than the retained students that made the at risk model difficult to build. The motivation for using R was twofold. First, to generate rules for minority class, and second, use R to make it reproducible.