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

Huzefa Khalil
Tue, 15 Feb 2022 16:34:51 -0000

A book I have found useful in this regard is The Workflow of Data
Analysis Using Stata by J. Scott Long. Obviously the book is targeted
towards Stata users but the concepts work just as well for R.


On Tue, Feb 15, 2022 at 11:27 AM akshay kulkarni <akshay_e4 at
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? Or
any online resources for "personal software process"? Perhaps I can
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 at gmail.com>
> Sent: Monday, February 14, 2022 5:23 AM
> To: akshay kulkarni <akshay_e4 at hotmail.com>
> Cc: R help Mailing list <r-help at 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 at
hotmail.com<mailto:akshay_e4 at 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
>
>
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>
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