Abstract: In the era of big data, public statistics courses in universities play a crucial role in cultivating data literacy, developing statistical thinking, and fostering interdisciplinary applications. These courses are essential for training high-quality professionals and equipping them with the skills needed to navigate and contribute to a data-driven world. Integrating programming practice into these courses is a student-centered approach that is essential for effectively enhancing students'skills of data analysis and practice. Given the diverse academic backgrounds of students, as well as their varying levels of programming proficiency and learning needs, the open-source, user-friendly, and powerful statistical programming language R has been selected. This paper explores efficient methods for integrating the R language into the curriculum, maximizing the use of limited class hours to enhance teaching effectiveness and students'practical abilities. It provides actionable teaching reform recommendations and multiple practical integration models, including the utilization of specialized packages and the development of custom demonstration cases.

Key words: Public statistics courses, R language, Practice ability, Data literacy, Programming, Interdisciplinary