Pandas like features
data.pulverizer at gmail.com
Fri Nov 13 21:28:30 UTC 2020
On Thursday, 12 November 2020 at 19:09:48 UTC, bachmeier wrote:
> On Thursday, 5 November 2020 at 22:46:21 UTC, data pulverizer
>> On Thursday, 5 November 2020 at 22:17:12 UTC, data pulverizer
>>> ... I have many years of writing code in R and from my
>>> experience, apart from minor instances I would try to avoid
>>> writing production libraries or code in it.
>> ... avoiding writing production code in R is just my
>> professional advice.
> It really depends (which was one of the points of my earlier
> post about how broad this field is). For someone doing academic
> research or statistical analysis for, say, marketing purposes,
> the interactive code they write is the production code. They're
> not going to write two versions of their code. I know for web
> applications or finance or some other areas where the
> distinction matters.
> But as far as telling people "don't write code in R", that's
> simply a non-starter, and there's no reason to even begin a
> project like this if you're going to tell people to avoid
> existing libraries in either R or Python. They'll just shrug
> when you start talking about performance because for the vast
> majority of what they're doing it's not an issue.
You act as if I'm banning people from writing code in R - I
certainly don't have the power to do that. And yes, it varies
from situation to situation, as I clearly eluded to.
I've done a lot of projects in R. I'm well aware that sometimes
it is unavoidable for the client. What I am saying is given the
choice, you should probably choose a different tool apart from
"some minor instances". I've seen R go spectacularly wrong
because of the type of language it is, it makes assumptions of
that the programmer means which can cause epic bugs, and very
often, it does it silently and it happens all the time. You can
never be sure that *any* piece of R code will work as it should.
It's just the nature of the language. People write it because
it's easy and has "boilerplate", which is fine if you are proof
of concepting or doing research and some other things, but you
use it in mission critical production apps and it may well blow
up in your face, and you might not even know. And that's before
we get to performance, and other things blah, blah, blah.
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