I would add to what Jeff replied. Many and perhaps most or even all
languages that have room for evolution, including Python, can end up getting
more and more complex with multiple ways to do things but it generally is
possible to write many useful programs in the core language.
I often wonder what would happen if someone took a language that was decades
old, and examined a recent version and used the results to create a new
streamlined language in which many choices are simply removed and some newer
ones are used instead. Consider the endless number of ways you can now do
formatted printing in python including various versions of strings. In R,
some of the ideas have been made available in the glue package in the
tidyverse which many people do not know about and others use instead of much
of what is available in basic R.
I think having choices is great for programmers but as noted, makes it
harder when hiring people to see if they fit. But, IMNSHO, any programmer
you hire that is not able to rapidly get on board and read manual pages or
sections of books showing how to use features, may not be the best hire. I
know I have been hired in situations where my experience was of different
operating systems, programming languages and editors/environments and
switching was not hard because I had a flexible background. Over years, we
kept shifting and I kept up while some others who knew ONE THING were often
struggling.
The reality is that R was written so long ago that it would rapidly have
been less and less attractive to some programmers if it stood still. Some of
the concerns mentioned are reasonable and some have solutions such as taking
a snapshot of what versions of things you allow to be used that form a
stable environment and then not updating anything. A new machine would
download just the copies needed, as long as the version remained archived.
But is R as bad as Python which split in ways that made many 2.x programs
incompatible with 3.x and yet some people continue to use the old version,
which is a bit souped up to emulate, rather than changing the code to be
compatible? Nobody forces you to use dplyr and frankly, it has similar
issues as the tidyverse once built has been changed often enough so my older
programs often tell me functionality has been, or will soon be, made
obsolete and the newer stuff may be much more powerful and yet a pain to use
for simple things as they allow ever more abstractions.
I will say that it may happen to R too and a new language named P may be
offered alongside R that will become more difficult within a year.
But had this happened, R would not have things like a built-in pipe that
some find useful or even essential.
-----Original Message-----
From: R-help <r-help-boun...@r-project.org> On Behalf Of Small Investor via
R-help
Sent: Friday, June 13, 2025 7:50 AM
To: r-help@r-project.org
Subject: [R] Some general comments
Dear R community,
I have been using R for over 15 years. I want to raise an issue which has
been haunting me for some time now: It feels as if R is falling apart. I try
to justify this feeling by providing three discussion points:
1. Version compatibility issues seem to be on the rise. Very often, you get
the message that package x was built on R version y (and thus, won't work in
your version of R). When you update to the latest version of R, you realize
that not all packages are available for that version. It seems that for each
version, only a (non-predictable) subset of packages is available.
2. The overhead of installing new packages seems to be on the rise. It seems
that the packages depend on more and more other packages. The more packages
you have in the 'foundations' of package x, the more likely it is that one
of these causes an error and the whole stack fails. Installing used to be
easy back in the day: You got a 20 lines' output. Now you get endless
prints. I may be mistaken but some packages seem to require admin rights on
your computer which you don't often have on your work PC.
3. R seems to be developing into different dialects. You have dplyr and
tidyr, some people prefer data frames, some prefer tibbles. Some people use
pipes, some use traditional syntax. Some prefer object-oriented programs,
some prefer procedural scripts. If you put in a job announcement that
somebody has to know R, it doesn't mean the same thing for different people.
If you compare the use experience of R in 2025 to that of Matlab, the
difference is striking: Matlab is concise and clear, R is more and more
about endless prints. Of course, Matlab is a commerical product, but R used
to be the same way. I don't know if many other people feel the same way, but
I think I am shifting away from R.
yours best,a data analyst dude
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