I've used R for 19 years and do not have any other programming language ability. I am curious: What is frustrating to you about R relative to other languages?
- It has a bunch of different types of classes, and they all behave differently. Debugging isn't awful, but it's harder than it should be. Also, the documentation isn't clear about which classes to use.
- A lot of the workhorse functions suffer from parameter glut. Despite having different kinds of classes, almost all functions expect plain vectors. Packages like survival show how objects make it easier to read code, reuse data, and validate data. Without the base packages doing it more, everyone's chosen their own systems. The community's been gravitating to organizing "objects" as rows in tables (i.e. tidy).
- The way a function uses an argument might surprisingly change based on other arguments given (e.g., `binom.test`). And then the documentation won't have examples for the different use cases.
- Most users don't have the time or desire to become better R programmers. They have other work to do. For my own work, I write packages with custom classes, functions, and template documents. For collaboration, I keep things very plain and rarely go beyond dplyr; very often, the script goes between two steps executed in a GUI software.
To me, it's the tooling around it.
Everything is done in R-studio and it's focus is to generate statistical documents.
The result is a sub optimum solution.
It lacks good tooling around installing and running R programs.
R programs don't import, they include. It doesn't make it more readable.
R-studio is very Emacs like in the sense that it just lacks a decent editor.
Due to R-studio being the default, there's not much support for other editors.
I use Vim with the R command line wrapped around Makefiles. I don't even have RStudio installed. Works great.
I can even pop up an interactive R command prompt session and do whatever I want in it, even quick ggplot2 graphs. Help shows up just as you would expect, and plots pop up in new windows. RStudio is much less advanced than people think it is, it's really just managing R's windows for you and doing generic IDE work. R is doing all the heavy lifting.
If you're wanting a more "import" like thing you might want to look into making R packages instead of scripts. You don't have to submit them to CRAN, and you can execute them pretty simply on the R command line as well.
That being said, it's really not an OOP or software development tool. It's definitely geared toward data science, but you can automate that very well for generating graphs automatically and reporting for whatever reason needed.
I use Linux, but it should be pretty straightforward on Mac as well. Just type "R" in the command prompt and check the manpage for R itself ("man R").
I'd also look into the "knitr" package, which is what all of the Rmarkdown is based around. So for instance, most of my Makefiles are based around a simple command like:
R -e "library(knitr); knit2html('index.Rmd')"
Then I just code using VIM on index.Rmd. You can probably set this up however you like with the R command line.
For interactive it literally is just typing "R" in the command prompt. For help, things like "?ggplot", "??knitr", or whatever, so you can open multiple interactive sessions like you were using IPython or something. When you print a plot, it just pops up in a new window.
You can also use "R" to just execute R raw if you are trying to do it without Rmarkdown. I just prefer the HTML output. Pretty sure all the RStudio RMarkdown stuff just calls knitr as well.
The output looks the same as anything on RPubs (and there is a way to publish to RPubs, I used to have to do that at one point), random one from the first page:
Emacs has had excellent R support since much before R-studio came around. In fact to me R-studio has always felt like a stand-alone implementation of ESS (minus Emacs).
FWIW, I do prefer R to all other "notebook" languages. It has everything I need for answering questions about data, working with files, text, and visualization. Libraries that do everything I could imagine are easily available.
For me it's a pragmatic workhorse tool that I use and aside from frequently getting frustrated with the task at hand, it has never failed me in the end.
I think R is much like a handheld power tool. I have no interest in diving deep into the workings of the tool because when I need to use a drill, for instance, I just need to drill holes and anything else is an annoying distraction (I realize that sounds bad!).
I've also worked with Mathematica and JMP in the past. They're very capable, but not as good at general-purpose data-wrangling as R is today (given Rstudio, knitr, shiny, all the specialized libraries, and most especially the tidyverse).
In decades I have not encountered any development environment where the obscurity of error message presentation can touch R. If one uses other languages or build environments the error messages can frequently be used to diagnose the issue.
In R they default to just vomiting some internal exception often with no context, and I can count the times I have encountered helpful or even seemingly deliberately constructed error messaging in single-place base five. Even kernel development is in some sense better because at least there you are in context and the layers are traversable.
Seemingly one of the primary skills of an R programmer is serving as an informal database of “what the fuck does this mean?” when the issue is something trivially detectable like passing a list vs a vector.
