Refactor convert_input to Perform tasks via helper function

[Sweetdevil144]

Description

Following changes were performed :

• Shift functions to check for missing files
• Return from convert_input via a helper function
• Refactor extra variables in run.meta.analysis
• Update corresponding test files and add tests to ensure do_conversions isn't affected by current applied changes

Motivation and Context

The main motive for these changes is to simplify convert_input by trying to break some of its components and branch it to other functions.

This PR may fix a task within #3307

Review Time Estimate

• Immediately
• Within one week
• When possible

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

• My change requires a change to the documentation.
• My name is in the list of CITATION.cff
• I have updated the CHANGELOG.md.
• I have updated the documentation accordingly.
• I have read the CONTRIBUTING document.
• I have added tests to cover my changes.
• All new and existing tests passed.

[Sweetdevil144]

Is their any other way to unwrap these variables in a single context line. For example, inheriting the above list and utilising the list$var syntax in-place. A lot of variable operations(assignment and reassignment) would consume extra memory in the heap. Should we also implement a Garbage Collection (gc()) or is it automatically applied in R ?

[infotroph]

Rather than try to unwrap them in one line I'd consider whether they need to be assigned separate names at all -- e.g. can all places below that refer to result_sizes refer to checked.missing.files$result_sizes instead? If that's what you're suggesting by "utilising the list$var syntax in-place", then yes, I like that approach.

That said these objects are all very small and the runtime/memory overhead from reassignment will be negligible and R's reference-counting garbage collection will take care of the cleanup for us. Happily though, the same optimization gives (what I think is) a substantial improvement in code clarity, so I endorse it on those grounds.

[harshagr70]

add_entries_result <- update_ensemble_writes( result, con, start_date, end_date, overwrite, insert.new.file, input.args, machine, mimetype, formatname )
return(add_entries_result)

The return values assigned here to a variable likely is a bug , this patch might fix this up .