[mshron]

I highly recommend anybody getting into R to skip the base language (which indeed is ancient and full of gotchas) and go straight for the Tidyverse[1]. You can always go back in and learn how to do things the old way later.

Over the last decade, the R community has largely standardized around tools like dplyr, ggplot, tibble, purrr, and so on that make doing data science work way easier to reason about. Much more ergonomic. At my company we switched from using Python to using R for most analytical data science work because the Tidyverse tools make it so much easier to avoid bugs and weird join issues than you get in a more imperative programming environment.

[1] https://www.tidyverse.org/

[uryga]

i would recommend getting comfortable with doing stuff with base R, then trying tidyverse. Starting with dplyr might get you results quick, but its "special evaluation" actively confuses your understanding of how the base language actually works (speaking from experience with an R course and subsequently helping other confused folks)

Consider this example:

  # base R
  starwars[starwars$height < 200 & starwars$gender == "male", ]
  
  # dplyr
  starwars %>% filter(
    height < 200,
    gender == "male"
  )

(Source: https://tidyeval.tidyverse.org/sec-why-how.html)

Where'd `height` and `gender` come from in the dplyr version? They're just columns in a DF, not variables, and yet they act like variables... Well that's the dplyr magic baby!

dplyr (and other tidystuff) achieves this "niceness" by doing a whole bunch of what amounts to gnarly metaprogramming[1] -- that example was taken from a whole big chapter about "Tidy evalutation", describing how it does all this quote()-ing and eval()-ing under the hood to make the "nicer" version work. it's (arguably) more pleasant to read and write, but much harder to actually understand -- "easy, but not simple", to paraphrase a slightly tired phrase.

---

[1] IIRC it works something like this. the expressions

  height < 200
  gender == "male"

are actually passed to `filter` as unevaluated ASTs (think lisp's `quote`), and then evaluated in a specially constructed environment with added variables like `height` and `gender` corresponding to your dataframe's columns. IIRC this means it can do some cool things like run on an SQL backend (similar to C#'s LINQ), but it's not somthing i'd expose a beginner to.

[canjobear]

My experience is that this weird evaluation order stuff is only confusing for students with a lot of programming experience who already expect nice lexical scope. For those coming in from Excel, the tidyverse conventions are no problem and are in fact easier than all the pedantic quoting you have to do in something like Pandas. It only gets confusing when you want to write new tidyverse functions, and even then, base R isn’t any simpler: the confusing evaluation order is built into R itself at the deepest level.

[buixuanquy]

Wow, now I understand the reasons. As Python guy I'm having trouble to understand how it is possible in R.

[jstx1]

I would recommend the opposite - pick stuff from tidyverse (mostly dplyr and ggplot2) only if you need them. Knowing base R goes a long way on its own.

[melling]

Pipe operator is now in base R so one of the nice features is now standard

|>

https://www.r-bloggers.com/2021/05/new-features-in-r-4-1-0/

[tarsinge]

I found the book R for Data Science (which is free http://r4ds.had.co.nz) to be a very good introduction to R with Tidyverse.

[tpoacher]

I'm on the same page as the other commenter here, except stronger.

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.

[mr_toad]

> ggplot

One day I’ll have a whole week free so I can sit down and learn an entire graphical grammar so that I can remove the egregious amounts of chart-junk in the ggplot defaults.