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

[akshay kulkarni]

Dear Bert,
                  I don't know what made you to state that this dialogue to be taken off the list, but if there is something wrong in my wordings or tone or the way of argument, I highly welcome criticism. Anyway thanks for your reply. I will make sure that I do a thorough research before posting here next time.

Thanks  again,
Yours sincerely
AKSHAY M KULKARNI
________________________________
From: Bert Gunter <bgunter.4567 using gmail.com>
Sent: Tuesday, February 15, 2022 10:06 PM
To: akshay kulkarni <akshay_e4 using hotmail.com>
Cc: Richard O'Keefe <raoknz using gmail.com>; R help Mailing list <r-help using r-project.org>
Subject: Re: [R] SDLC methodology for R and Data science......

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<mailto: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? 
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<mailto:raoknz using gmail.com>>
Sent: Monday, February 14, 2022 5:23 AM
To: akshay kulkarni <akshay_e4 using hotmail.com<mailto:akshay_e4 using hotmail.com>>
Cc: R help Mailing list <r-help using r-project.org<mailto: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><mailto: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]]

______________________________________________
R-help using r-project.org<mailto:R-help using r-project.org><mailto:R-help using r-project.org<mailto: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<mailto: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]]