R seems like a complete mess after Python. (The comparison is not fair, but still...). Now I know why: https://t.co/DHNVNoxImy— Somi András (@somiandras) July 31, 2017
I rarely blurt out generalising complaints on Twitter or anywhere else, but stumbling through the R course on Udacity (called Exploratory Data Analysis, but it’s really about R itself, the content otherwise is very simple) I couldn’t hold it back. I had to Google ‘R inconsistencies’ to get some hints whether it’s just me or there are others who feel the same way, too. That’s how I found that article.
I just cannot embrace R (yet), especially not after I fell in love with Python many months (maybe even more than a year) ago. R doesn’t look ‘neat’ many times, I don’t feel the logic in the naming conventions (or the lack thereof) and the usage of many special characters, the data types seem to differ from usual data types ’just because’ and it seems there are several competing syntactical structures randomly dropped into the languge. But it’s really just my impression on the ‘look and feel’.
Of course, it’s not fair to compare R, a very statistics focused language or tool to a general purpose programming language, Python, which kind of just happens to have strong support for similar operations through its libraries. And of course, I also read several pieces on the long list of inconsistencies and design faults of Python, too (though this list about R feels longer, but it’s just my bias).
I think I know how it feels to look up on a steep learning curve, and it’s not how I feel currently. Maybe sometime in the future I’m going to be enlightened, but for now I’m just full of self-pitty for having to struggle with R on simple problems I would solve with Python in no time.
Cover photo by Steinar Engeland on Unsplash