[R] SDLC methodology for R and Data science......

[akshay kulkarni]

dear ERic,
                 Thanks for your reply. As mentioned, I will post my issue in the R devel list.

Thanking you,
Yours sinecerly,
AKSHAY M KULKARNI
________________________________
From: Eric Berger <ericjberger using gmail.com>
Sent: Tuesday, February 15, 2022 10:26 PM
To: Bert Gunter <bgunter.4567 using gmail.com>
Cc: akshay kulkarni <akshay_e4 using hotmail.com>; R help Mailing list <r-help using r-project.org>
Subject: Re: [R] SDLC methodology for R and Data science......

Bert Gunter writes:
>> 1. This dialogue should be taken offlist imo.

Akshay, I think you asked a great question and I was looking forward
to seeing the answers.
After reading Bert's comment I checked the posting guide for this list
and I see that a better fit for your question would be the r-devel
list.

https://stat.ethz.ch/mailman/listinfo/r-devel

Best,
Eric



On Tue, Feb 15, 2022 at 6:37 PM Bert Gunter <bgunter.4567 using gmail.com> wrote:
>
> 1. This dialogue should be taken offlist imo.
>
> 2. And really, make some effort of your own before posting: An internet
> search on "Watts Humphrey Software Development" immediately brought up what
> appeared to be answers to at least some of your queries.
>
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along and
> sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
>
> On Tue, Feb 15, 2022 at 8:27 AM akshay kulkarni <akshay_e4 using hotmail.com>
> wrote:
>
> > Dear richard,
> >                       I am very grateful for your informative reply.
> >
> > THe fact is, I am doing a project, which is not less complex,(if not more)
> > than those of Microsoft or Accenture or Google , but I am doing it all by
> > myself. Can you please let me the full title of the book by Watts Humphrey?

get
> > some tips on how to go about my project ( I've mostly taken into account
> > standard methods of the state of the art, I am looking for something
> > "whizzy" than aids development by one person).
> >
> > Thanks again,
> > Yours sinecerly,
> > AKSHAY M KULKARNI
> > ________________________________
> > From: Richard O'Keefe <raoknz using gmail.com>
> > Sent: Monday, February 14, 2022 5:23 AM
> > To: akshay kulkarni <akshay_e4 using hotmail.com>
> > Cc: R help Mailing list <r-help using r-project.org>
> > Subject: Re: [R] SDLC methodology for R and Data science......
> >
> > There are at least two ways to use R.
> > If you have devised a statistical/data science technique
> > and are writing a package to be used by other people,
> > that is normal software development that happens to be
> > using R and the R tool.  Lots of attention to documentation
> > and tests.  Test-Driven Development is one approach.
> >
> > Many R users aren't developing code for other people.
> > They are trying to make sense of some kind of data.
> > This is what used to be called "exploratory programming".
> > And heavyweight development processes aren't really
> > appropriate for this kind of work.  In traditional terms,
> > when you are doing exploratory programming, you spend
> > most of your time in the requirements phase.
> >
> > Perhaps the most important thing here is to keep a log
> > of what you are doing and record things that didn't work,
> > why they didn't work, and what you learned from it.
> > When something DOES give you some insight, you want to
> > be able to do it again.
> >
> > The tricky thing is scaling from exploration to development.
> > After playing around with one data set, you might want to
> > provide a script that other people can use to process
> > similar data sets the same way.
> > Use a light weight process, but make sure you have plenty
> > of tests, and adequate documentation.
> >
> > Watts Humphrey developed something he called the "Personal
> > Software Process" and wrote a book about it.  I don't like
> > his examples for several reasons, but the point about
> > watching what you do and measuring it so you can improve is
> > well made.
> >
> >
> >
> > On Mon, 14 Feb 2022 at 05:33, akshay kulkarni <akshay_e4 using hotmail.com
> > <mailto:akshay_e4 using hotmail.com>> wrote:
> > dear members,
> >                          I am Stock trader and using R for research.
> >
> > Until now I was coding very haphazardly, but recently I stumbled upon the
> > Software Development Life Cycle (SDLC), which introduced me to principled
> > software design. I am college dropout and don't have in depth knowledge in
> > Software Engineering principles. However, now, I want to go in a structured
> > manner.
> >
> > I googled for a SDLC method (like XP, AGILE and WATERFALL) that suits the
> > R programming language and specifically for data science, but was bootless.
> > Do you people have any idea on which software engineering methodology to
> > use in R and data science, so that I can code efficiently and in a
> > structured manner? The point to note, with regards to R, is that
> > statistical ANALYSIS sometimes takes very little code as compared to other
> > programming languages. Any SDLC method for these types of analysis,
> > besides, rigorous scripting with R?
> >
> > Thanking you,
> > Yours sincerely,
> > AKSHAY M KULKARNI
> >
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
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> > UNSUBSCRIBE and more, see
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> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

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