R is a scripting language, most of the underlying infrastructure is written in F...

glofish · on Dec 14, 2019

that "easy" has a huge price - the language is choke-full of unexpected behaviors, inconsistencies and gotchas - all in the name of making it "easy"

ekianjo · on Dec 15, 2019

Care to share any language that does things better without a steep learning curve? R is popular for very, very good reasons. You can pick it up and be productive with it in no time, even with the "gotchas"...

proverbialbunny · on Dec 15, 2019

Raku has less of a learning curve than R, does things better, and is quicker and easier to pick up and be productive in, with barely any gotchas if any at all.

Though, to be fair, I like R. It has good plotting libraries, and as much as it gets bashed I like RStudio too. In comparison, Raku's ecosystem is brand new.

epistasis · on Dec 15, 2019

R is lisp variant with a ton of syntactic sugar and vectorization of the basic data types. Calling it a scripting language would indicate that you haven't liked at it much as a language.

Sure, most of the matrix operations and tight loops are implemented in FORTRAN or C++, but that's for performance reasons. The same would happen with Python, and I don't think it would be fair to call Python a scripting language either; it also has a ton of Lisp like qualities.

sargram01 · on Dec 14, 2019

I find I spend most of my time debugging R, which is astoundingly difficult since it doesn’t report line numbers on errors. Most of my code is in C++, so that helps, albeit it’s still overly complicated to start up R in gdb. Amazingly Julia isn’t much better when it comes to error reporting either.

kgwgk · on Dec 14, 2019

  # cat /tmp/test.R
  x <- 1:10
  y <- 1:20
  plot(x, y)

  # R --quiet
  > source("/tmp/test.R")
  Error in xy.coords(x, y, xlabel, ylabel, log) :
    'x' and 'y' lengths differ
  > traceback()
  8: stop("'x' and 'y' lengths differ")
  7: xy.coords(x, y, xlabel, ylabel, log)
  6: plot.default(x, y)
  5: plot(x, y) at test.R#3
  4: eval(ei, envir)
  3: eval(ei, envir)
  2: withVisible(eval(ei, envir))
  1: source("/tmp/test.R")

If you mean the lines in the packages you used, I think you'll see the lines if you build them with keep.source=TRUE

CreRecombinase · on Dec 14, 2019

It's so crazy easy to start R in gdb:

  R -d gdb

that's it!

ksevastyanenko · on Dec 14, 2019

You might find this package useful https://github.com/robertzk/bettertrace

Zelazny7 · on Dec 14, 2019

  options(error=recover)

LegitShady · on Dec 15, 2019

All the design brief said was get started anything beyond that is a change request

Cosi1125 · on Dec 15, 2019

What are you talking about? Of course it was written in lower-level, compiled languages. Is it any different than Python? Javascript? Perl?

As for the "easy iterop with other languages": [1]

So, how is R different from Python?

Also, it's not true that R wasn't designed for writing software. Even a critical "pamphlet" by Pat Burns [2] states otherwise. For writing statistical software, yes.

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[1] https://wiki.python.org/moin/IntegratingPythonWithOtherLangu... [2] https://www.burns-stat.com/pages/Present/infernoishR_annotat...