Why do we use R rather than Excel?

Attempting to solve a complex analytical problems in Excel is similar to a doctor trying to solve medical problems by reference only to atoms.

Instead doctors use a variety of abstractions: organs, cells, enzyme, etc. to understand and explain a problem.

By using a programming language, we can develop appropriate abstractions to solve our problem in a way which keeps a lid on complexity.

I think this concept does make sense to an advanced Excel user, and can help explain the situations in which they may reach for a different tool. Having made some very complex Excel spreadsheets in the past, I think was aware it can become very difficult to develop them or generalise them further, even before I became a programmer.

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 and 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.

Avoid tidyverse like the plague, except when you can't, or when you don't actually care about the sanity of your code and are happy copy/pasting pre-prescribed snippets without needing to understand let alone modify them.