Facing issues counting course modules and lessons with nested loops. Try this streamlined Blade snippet:
<div>
@foreach($courseArray as $course)
<section>
<p>{{ count($course->parts) }} parts</p>
@foreach($course->parts as $part)
<p>{{ count($part->units) }} units</p>
<p>{{ count($part->durations) }} durations</p>
@endforeach
</section>
@endforeach
</div>
I encountered a similar issue in one of my projects recently. What worked for me was adding a few checks to confirm that each nested object exists and contains the expected values. In my case, I noticed that sometimes the arrays were not properly populated, resulting in inaccurate counts. Ensuring the integrity of the data before running counts made the difference. Also, I found experimenting with debugging outputs at each level provided some clarity on potential logic errors. The Blade snippet structure itself is fine, but verifying the data structure might help resolve the counting issues.
Hey there, Nate_45Guitar and everybody! I was tinkering with a similar setup recently and started wondering if it might help to double-check that each level (like the course parts, units, etc.) is actually an array or collection before doing a count. I sometimes use a quick check (or even Laravel’s optional chaining) to ensure I’m not tripping over a null value. Have any of you tried something like using Laravel’s collection methods for filtering or pre-processing the arrays before looping through? It might not only solve the count issues but also improve performance on larger data sets. I’m also curious—has anyone run into any other unexpected behaviors with nested loops, especially when the underlying data inconsistencies come into play? Would love to hear your experiences or any tweaks that worked for you. Cheers! 
hey, try casting those inner arrays to collections. sometimes the count misbehaves if an object isn’t really an array. i found this helps avoid null or invalid data issues. cheers!
My experience with nested Blade loops taught me the importance of validating data at each level. I encountered a similar challenge where data inconsistencies would lead to counting errors. To address this, I incorporated conditional checks before each count and added safeguards against null values. Testing with various edge cases helped identify where unexpected data structures were causing issues. I also found that leveraging Laravel’s collection methods provided a more streamlined approach for filtering data. This proactive approach to validating data early in the process saved a lot of debugging time later on.