I use R on a daily level. A significant portion of our climate change adaptation research code and decision planning systems (which are used in various utilities around the world to support decision makers) are built using R.
I understand what the author is stating, but I just feel like this is from inexperience with R and ignoring the vast amount of packages available within it that are specifically targeted at data science. There are some valid issues and criticisms of R, but I think this article only focuses on the application of R in a single context. A significant portion of data science is about cleaning the data and an entire suite of packages known as tidyverse solves these problems (for me anyways) while also being very simple and easy to understand. I mean tidyverse supports piping, which is exactly what this article is saying to use.
Obviously your mileage may vary, but this post irks me the wrong way.
The tidyverse is excellent, and has its own stylistic choices that are arguably quite good. A lot of other random packages also have their own stylistic choices and they are not good. So I suppose I would say that R makes it really easy to write data pipelines well, but also really hard to write them poorly; and doesn’t make it very obvious on what the better choices are.
If only Hadley hadn't picked snake-case, which doesn't work so well out of the box with ESS. Yah, I know there is a way of fixing this with a setq in ESS, but I shouldn't have to.
Always weird to meet Emacs people who don't like having to customise Emacs. That said, ESS has some weird defaults, not sure I'd lay them at R's door tbh.
ESS defaults predated Hadley's contributions by a decade, making his seemingly unique adoption of snake case extremely unsociable. I actually use _ to <- a lot. It's enough of a pain in the ass, I basically stopped using his packages. This was annoying, but ultimately the Hadleyverse became a bridge too far for the kind of bread and butter data science I do, so it turned out to be helpful to me personally.
I agree. But I'd also argue that it's based on how you're using the tools you're given (or what others have given you).
Pretty much the argument of using apply vs for loops. Most of the time, apply is going to be significantly more computationally efficient. However, some people think of the problem in different context than others, and if it works for them then it works.
I understand what the author is stating, but I just feel like this is from inexperience with R and ignoring the vast amount of packages available within it that are specifically targeted at data science. There are some valid issues and criticisms of R, but I think this article only focuses on the application of R in a single context. A significant portion of data science is about cleaning the data and an entire suite of packages known as tidyverse solves these problems (for me anyways) while also being very simple and easy to understand. I mean tidyverse supports piping, which is exactly what this article is saying to use.
Obviously your mileage may vary, but this post irks me the wrong way.