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.
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).