I agree that researchers usually don’t need to worry about software engineering because their code is often simple, used only for one paper, and intended for personal use. But for large-scale research involving multiple researchers, users, and datasets, software engineering mindset can be beneficial in the long term.
It can be frustrating if those post-docs underestimate the time cost and engineering complexity of software development. They would complain why it takes so long for your friend to "just" change a button in some app.
As an R developer, I can deeply relate to this. People that use R as the mother language are probably from academia. They can be very good at data analysis and scripting, but usually have no idea of software development. They have few knowledge about versioning, modularization, documentation, and testing, which can lead to issues when they develop production-level R packages and Shiny applications.
On the flip side, R developers with strong software development skills can be incredibly valuable in academic settings.
In creating a music score, you literally "add" components to the score. So it's natural to implement this operation this way. From the OOP perspective, it's just method overloading. Not uncommon in R development.