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

[Richard O'Keefe]

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> 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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