this post was submitted on 13 Jul 2023
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As an experienced programmer, I can say there's nothing inherently wrong with hardcoding data. If you're not presented with clear requirements up front (i.e. all the different use cases your code should handle) then it makes more sense to get something working and refactor as you go. In your case you said the data was subject to change, which doesn't necessarily mean you could abstract it out from the start (unless you were told exactly how it would change). In general, however, software development is all about iteratively refining your assumptions about how things should work while simultaneously juggling the changing demands of stakeholders. One of the most useful rules of thumb is the DRY approach (don't repeat yourself). This is complemented by WET (write exactly twice). Together this means that if you find yourself repeating logic three times, that's enough for you to refactor into a single, generalized function. Repeating twice is generally fine because your third use case might be sufficiently different that a premature refactor is a waste of effort. As you gain more experience programming, you'll find it's less about technical proficiency and more about working efficiently, creating fewer headaches, etc.
This is the approach I generally take and I always think it’s poor as I watch the SE’s come over and just write whole components in one go that work seamlessly whereas I will tend to need to figure it out iteratively and thus my code can look like a mess but I will go back and refactor once I have something that works.
I guess it comes with experience as the app I am building is following the contentions of other apps this company has made so although it’s the first time for me, the SE might have made ten very similar apps using the same custom hooks etc.
There's a difference between iterating on your code patterns until you get something working and iterating on the solution as requirements evolve. You're only two months in so you still have loads to learn, but eventually you'll come to know all the "best practices" so that when you're presented with a problem, you can immediately know what the ideal solution should look like. The caveat there is that the ideal solution is subject to change as the situation changes.