How can I "think OOP" when using R?

Question

I am a statistics graduate student who works a lot with R. I am familiar with OOP in other programming contexts. I even see its use in various statistical packages that define new classes for storing data.

At this stage in my graduate career, I am usually coding some algorithm for some class assignment--something that takes in raw data and gives some kind of output. I would like to make it easier to reuse code, and establish good coding habits, especially before I move on to more involved research. Please offer some advice on how to "think OOP" when doing statistical programming in R.

Answer 1

I would argue that you shouldn't. Try to think about R in terms of a workflow. There's some useful workflow suggestions on this page:

Workflow for statistical analysis and report writing

Another important consideration is line-by-line analysis vs. reproducible research. There's a good discussion here:

writing functions vs. line-by-line interpretation in an R workflow