Why do people prefer R for data science?
I haven’t used R extensively myself, so I’m curious about its advantages over Python. To me, it seems like a more niche alternative with fewer users and limited machine learning libraries compared to Python. What makes R a better choice for some people, especially when Python seems to offer more versatility?
Statistical libraries and tools,
In the context of choosing R over Python for data science, I’m curious about how the statistical libraries and tools available in R play a role. It seems like R might have specialized resources or unique features that cater specifically to statistical analysis, which could be a significant advantage for certain tasks.
I’m new to this, so I’m wondering: why not adapt these tools for Python or develop new ones instead?
If you just need to get across town and you have both a car and an 18-wheeler at your disposal, would you choose the car (R in this case) for a straightforward trip, or would you go through the hassle of modifying the 18-wheeler (Python) to make it work?
R is a purpose-built tool specifically designed for statistical programming, with a lot of established technology and code tailored for that purpose. Why reinvent the wheel or switch to something that seems more modern when the existing solution is already well-suited for its intended job?
Plus, maintaining a new Python package comes with its own set of challenges, managing dependencies and ongoing upkeep. With R, someone else has already fine-tuned the tool and handles the routine maintenance for you.
If I’m going to take on the task of porting a well-functioning package to Python, I need a significant benefit like increased speed. However, if the time spent on ongoing maintenance is considered, the payoff may not be worth it. Sure, I could write it in C for faster performance, but if issues arise, I have to factor in the time spent fixing them. Some of us have lives beyond work and need that extra time for things like Reddit and binge-watching TV.