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.
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.
# 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")
R -d gdb
options(error=recover)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.
--
[1] https://wiki.python.org/moin/IntegratingPythonWithOtherLangu... [2] https://www.burns-stat.com/pages/Present/infernoishR_annotat...
But R also makes a lot of the tasks you do in data science far easier than it would be in a 'real language'.