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Problem is that we are coming from completely different perspectives. When you say "programmer", you are likely referring to someone from a CS background, likely with software engineering experience, who has spent their lives working in C++, Java, Python, etc. By that definition, I would be a non-programmer, as I come from a statistics background, and though I have lots of experience in C++ and Python, most of my experience and work is in R. But that is by choice. If I'm trying to create an application or build a website, I wouldn't use R. But when it comes to ingesting data, transforming and cleaning data, and modeling data, R is second to none. Yes, its syntax looks ugly and bizarre if you are used to object-oriented programming, software development, etc. In the context of working with data, I have never found anything in R to be even remotely confusing or strange. On the contrary, the next best option to R would almost certainly be Python, and the gulf between the two is massive in my opinion. Python is a great general purpose programming language, but its data analysis capabilities, using packages like Pandas and sci-kit learn, feel like poorly designed, bolted on, and unwieldy. R is better for virtually every aspect of data analysis than Python. So it isn't that R is poorly designed. Conversely, its very well designed, for its purpose as a data analysis-focused programming language. It only seems to be poorly designed to "programmers" because programmers work on problems that R isn't meant for. But that is like complaining that a screwdriver looks poorly designed for hammering nails. |