PYTHON AND R: A SIDE-BY- SIDE EVALUATION FOR ANALYTICS EXCELLENCE
Rekha Raichal · 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