The competition among julia, python and r for leadership in data science and development
DOI:
https://doi.org/10.32870/recibe.v14i3.422Keywords:
Programming languages, Julia, Python, R, comparison, data science, software development.Abstract
This study compares Julia, Python, and R programming languages in the context of data science and software development. By evaluating performance, libraries, community support, and licensing, through conducting speed tests —including matrix operations, data manipulation, and visualization— it was observed that Julia is the fastest in numerical computations, making it ideal for scientific computing, while Python offers a balanced solution, it is efficient in data processing and graphics, and features a robust ecosystem and a large online community. R stands out in statistical analysis and visualization but is slower in computationally intensive tasks. In the field of web development, Python leads with mature frameworks such as Django and Flask, while the alternatives in R (Shiny) and Julia (Genie) have more limited reach. Additionally, licensing differs: Julia and Python use MIT and PSF licenses, respectively, which are flexible and suitable for commercial projects, whereas R uses the GPL license, which is more restrictive. In conclusion, Python emerges as the most versatile option for general-purpose projects, Julia excels in high-performance computing, and R maintains advantages in statistical analysis. Therefore, the choice of a programming language depends on the context and objectives of the project.References
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