Cumulative counts over a period of a time; table output great, can't translate to graph due to reference reqs

Hello. I’m working with dynamic production datasets from our MRP. I’m pulling most of the data for the visuals from a ‘fact_material_transactions’ table. My visuals thus far focus on:
work week integer calculated field at the dataset level, dependent on transaction_date [datetime],
serial_number [string],
part_number [string],
transaction_type_name [string]

Set Up:
My graphs over time (work week integer) are clustered bar combo charts showing planned (line) vs actual (bars); x-axis is work week integer, bars are serial_number (count distinct), group color is part_number, line is planned (Min) which is fed by a joined excel doc with static data. Picture of an example:

I’d like to show cumulative bar combo charts next to these charts I introduced to you above. I’ve narrowed in on combinations of maxOver, minOver, runningSum, and windowSum. I can get the values to show properly in a table using

maxOver(runningSum(count({part_number})/count({part_number}),[{Work Week Integer} ASC],[{part_number}]),[{Work Week Integer}])

But that doesn’t translate to a bar chart visual due to the references needed. I’ve tried the following, as well, but can’t seem to get it:

windowSum(distinct_count({serial_number}),[{Work Week Integer} ASC],39,27,[{Work Week Integer}])

runningSum(distinct_count({serial_number}),[{Work Week Integer} ASC],[{Work Week Integer}])

Brief: Focusing on Q3, I want WW27 to show a bar with the value 78, then WW28 would show 200 (WW27 + WW28, 78 + 122), then WW29 would show 318, etc. I am filtered for a singular part number and there is one more filter that is supposed to take the first completion based on transaction_type_name at the dataset level:


Sadly the formula doesn’t seem to consistently work, but that’s the least of my concerns right now. Can anyone assist? Also, I’d love if there was a resource that dumbed down some of these explanations on Amazon’s documentation pages. I understand how LACs work conceptually, but when you start bringing in PRE_FILTER, PRE_AGG, POST_AGG_FILTER it’s not intuitive as to whether these levels are addressed at the dataset level or the visual level. Maybe I’m just dense. Thanks for the assistance.

Got it. Now I just have to figure out how to mark this as solved, or delete.