This chapter provides a comparative and integrative overview of R and Python as essential tools for modeling, simulating, and analyzing complex systems and 6G communication networks. It examines the distinct strengths and limitations of each language across various dimensions, including statistical modeling, machine learning, network analysis, and deployment scalability. Emphasis is placed on hybrid workflows that integrate both environments, facilitated by packages, to support advanced research in digital twins, network slicing, federated learning, and edge intelligence. Through pseudocode examples and interoperability strategies, the chapter offers actionable insights for leveraging the complementary capabilities of R and Python in interdisciplinary and future infrastructures.