Far more severe is the scant documentation online.
Don't take this as an attack on the language or community. I'm a huge fan of Standard ML and it's arguably in a worse state!
I was interested in supporting R in the first place because I knew of its importance (if only vaguely).
If it’s statistical methods, then you’ll need to look outside of R because R documentation isn’t trying to teach statistical methods.
You can try [1] the series of books teaching statistical methods using R.
In my stats degree we learned R methods alongside the statistical methods. R, to us at the undergrad level, is a fancy calculator. Yes it has functions and can do some “programming,” but it’s purpose is to facilitate using statistical methods and writing up reports.
If the audience of your IDE is programmers who want to do data analysis, then I think that’s a different audience than statisticians, who I think are the majority of users of R. R studio is already a decent IDE that statisticians are familiar with, so it might be a hard group to get to switch.
The syntax in R isn’t great. There are multiple ways of sub setting that depend on the data type you're subsetting.
Many of the top stats programs have notes on R or courses designed to teach R that are freely accessible on the internet.
[1] https://www.routledge.com/Chapman--HallCRC-The-R-Series/book...
Here’s a good example: https://dplyr.tidyverse.org
Equally confused about your take on the community. R community is sort of a perfect example of an inclusive community actively trying to include everyone with organizations like “rladies” for women in tech.
Then I went looking for how to interact with JSON. There's no builtin library I guess but rjson seems to be what people use. There's no official documentation I can find on how to install a package but there are many blog posts. The rjson's only official documentation seems to be in PDF and again it's pretty minimal: https://cran.r-project.org/web/packages/rjson/rjson.pdf.
Again, to be fair, anyone looking into Common Lisp or Standard ML or OCaml would probably feel the exact same way about their ecosystems.
Does it cause a problem for existing users? Probably not. Is it the friendliest thing for first-timers to get into? Probably not. Is that a problem? Again probably not?
There are manuals that you can find there.
R Studio (the IDE): https://www.rstudio.com/
R Studio cheat sheets: https://www.rstudio.com/resources/cheatsheets/
There is usually enough information on stack overflow / stack exchange to get through some questions. There is also a stats specific version that can sometimes be helpful. https://stats.stackexchange.com/
You are talking about documentation for user / community contributed packages. There is a minimum amount of standardization that needs to be followed, but yes, I do agree that the documentation could be better.
Hopefully this helps with installing packages: https://jtleek.com/modules/01_DataScientistToolbox/02_09_ins...
Regarding first timers, I think that once they’re aware of RStudio and the content they put out, learning becomes much more friendly and modern.
Still, I definitely agree and would appreciate a modern manual on base R.
1. Documentation is accessible via the interpreter. You can type ?funcname to get documentation or ?libname for the entry point for almost every library, or use the Help tab in RStudio, the most common interpreter. Package documentation is typically hyperlinked text and of a high quality. You can also see syndicated versions of library documentation online in HTML format. Here is for instance, the HTML documentation for the stats library (the built-in library which covers most of the statistical functions you want): https://stat.ethz.ch/R-manual/R-patched/library/stats/html/0... or rdocumentation.org or really any dozens of web syndicated versions. The PDF version you mentioned is linked from CRAN, the package repository, but is by no means the only entry point for documentation.
2. The print function -- actually not a single function, but rather a commonly implemented S3 method -- is easy to understand if you understand how the S3 object system in R works and how function dispatch works. What it does depends on the class of the object and whether an S3 print method has been implemented for the class of the object. This is true in most languages. If you're looking to something closer to a bare metal print function you should consider cat, but in general I don't find print confusing at all.
3. The subset operators available in R are documented. Because everything is a function in R, you can easily see the documentation by typing ?`[` or ?`[[` -- both have the same documentation page, which describes the essential difference between the two subsetting operators. This is tricky to learn at first but given that the two operators do different things, both desireable in different contexts, it's sort of difficult to argue this is an ergonomics issue and not a user error. If you want a more hands on discussion of the differences, you can try http://adv-r.had.co.nz/Subsetting.html
4. Assignment, similarly, is documented. You can check ?`=` if you have some concerns or read the documentation online here: https://stat.ethz.ch/R-manual/R-patched/library/base/html/as... The short version is that although <- is idiomatically preferred by style guides, there are basically no contexts where = would do anything different. You may want to be aware of -> and <<- as other assignment operators. The former allows right hand assignment, which is a fun bit of syntax, and the latter overrides the default assignment scope and forces a global which I personally find distasteful.
One final note: the inner workings of any R function for which the implementation is in R can be inspected. Simply type the name of the function and press enter to see the source code of the function. A lot of low level stuff is implemented in C, so you'll find a stub function that calls internal things, but for almost anything else, this is a good way to learn how things work. Like, run-length encoding is implemented in the rle function so just type rle and press enter and voila, you see the full implementation.
R has a number of core language issues and things that are annoying but the ones you named read like you puttered around for 10 minutes and didn't do the kind of basic homework you need to do to learn a new language. I wouldn't complain about what a bad language Go is because I don't understand the distinction between := and = as assignment operators.