PYTHON AND R: A SIDE-BY- SIDE EVALUATION FOR ANALYTICS EXCELLENCE - Raichal, Rekha (ShodhKosh: Journal of Visual and Performing Arts, 2024)
• This paper compares Python and R Studio in the domain of data analytics, focusing on their capabilities, libraries, scalability, ease of use, and application suitability. Python, being a general-purpose language, is favored for machine learning and production systems, while R is tailored for statistical analysis and visualization. This study provides insights to help data scientists choose the appropriate tool based on specific requirements. • The investigation focuses on a comparative analysis of Python and R in data analytics, particularly regarding their capabilities, ecosystems, scalability, ease of use, and application suitability. • The analysis aims to determine the strengths and weaknesses of Python and R, helping data professionals choose the appropriate tool for specific requirements in data science and analytics. • The study compared Python and R by evaluating their features, library ecosystems, performance, scalability, learning curves, and suitability for various applications. Sources included peer-reviewed articles, technical blogs, and industry whitepapers. • Python is versatile and excels in data wrangling, machine learning, and production systems. • R specializes in statistical analysis and visualization. • Python has a broader application scope and better scalability for large datasets. • R is preferred for statistical precision and high-quality graphics. • Both languages have strong, complementary ecosystems. • The findings reveal that Python and R are tailored for different purposes. Python is more suitable for general-purpose data manipulation and machine learning, while R is ideal for statistical research and detailed visualization. Combining their strengths can enhance data analytics workflows, emphasizing the importance of mastering both languages for maximum versatility