PowerQuery/BI: How to group sub-populations of variable size and perform calculations

Question

I have data for planned and actual produced quantities for a number of items (articles) and batches. Please see example data in image and attached table. I wish to calculate:

Note: For a specific item type, the planned quantity may change between batches (depending on when in time the batch was produced, data not included in example), i.e. for the last 3 rows for the item Orange. Different planned quantities within a single item type is to be considered different items.

1. Count number of occurrences/records per item type and planned quantity. Exclude items with <5 occurrences.
2. Overall average yield per article and planned quantity.
3. Max yield per article and planned quantity.
4. Average yield for the bottom 80% of batches as ranked by yield.
5. Average yield for the top 20% of batches as ranked by yield.

Number 1, 2 and 3 I can do myself in Power Query. Number 1 is done in a separate query "Count&Filter_Occurances" which is then merged with the main query.
Number 3 and 4 requires steps that I do not know how to produce, but I would assume what is required is something along the lines of:

1. Sort/rank all records within an article type and planned quantity based on their yield.
2. Group top 20% and bottom 80% of records per each article and planned quantity.
3. Calculate average yield in both groups.

Not sure if doing this in a separate query and merging, or inserting the grouping code directly into the main query is the best approach.

The number of records per item type and quantity can vary greatly, hence I need the top and bottom percentage of records instead of just the top and bottom number of records.

Any help with this would be very highly appreciated! Please see my current M code below.

Main query

let
    Source = Excel.CurrentWorkbook(){[Name="Data"]}[Content],
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Batch No", Int64.Type}, {"Item Name", type text}, {"Item ArtNo", Int64.Type}, {"Plan Qty", type number}, {"Actual Qty", type number}, {"Yield", type number}}),
    #"Merged Queries" = Table.NestedJoin(#"Changed Type", {"Item ArtNo", "Plan Qty"}, #"Count&Filter_Occurances", {"Batch Item No", "Plan Qty"}, "Count_Occurances", JoinKind.Inner),
    #"Expanded Count_Occurances" = Table.ExpandTableColumn(#"Merged Queries", "Count_Occurances", {"Count"}, {"Count"}),
    #"Grouped Rows" = Table.Group(#"Expanded Count_Occurances", {"Batch Item No", "Plan Qty"}, 
        {
            {"Item Name", each Table.First(_, "Item Name")[Item Name]},
            {"Overall Avg Yield%", each List.Average([#"%"]), type nullable number},
            {"Max Yield%", each List.Max([#"%"]), type nullable number},
            /* Change the below to perform the List.Average operation on a subset of the data records.
            {"Bottom80% Yield%", each List.Average([#"%"]), type nullable number},
            {"Top20% Yield%", each List.Average([#"%"]), type nullable number},
            */
            {"Count of occurances", each Table.First(_, "Count")[Count]}
        })
in
    #"Grouped Rows"

Count & Filter query

let
    Source = Excel.CurrentWorkbook(){[Name="Data"]}[Content],
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Batch No", Int64.Type}, {"Item Name", type text}, {"Item ArtNo", Int64.Type}, {"Plan Qty", type number}, {"Actual Qty", type number}, {"Yield", type number}}),
    #"Grouped Rows" = Table.Group(#"Changed Type", {"Item ArtNo", "Plan Qty"}, {{"Count", each Table.RowCount(_), Int64.Type}}),
    #"Filtered Rows" = Table.SelectRows(#"Grouped Rows", each [Count] >= 5)
in
    #"Filtered Rows"

Sample data

Item Name	Item ArtNo	Batch No	Plan Qty	Actual Qty	Yield
Banana	1	1002	10	9.7	0.97
Banana	1	1003	10	10.3	1.03
Banana	1	1004	10	10.1	1.01
Banana	1	1005	10	8.9	0.89
Apple	2	2001	20	24.1	1.21
Apple	2	2002	20	25	1.25
Apple	2	2003	20	17	0.85
Apple	2	2004	20	19.3	0.97
Orange	3	3001	50	50.5	1.01
Orange	3	3002	50	52.2	1.04
Orange	3	3003	50	51	1.02
Orange	3	3004	50	51	1.02
Orange	3	3005	50	51.8	1.04
Orange	3	3006	50	49.9	1.00
Orange	3	3007	70	72	1.03
Orange	3	3008	70	68.9	0.98
Orange	3	3009	70	53.2	0.76
Kiwi	4	4001	80	32	0.40
Kiwi	4	4002	80	53	0.66
Kiwi	4	4003	80	77	0.96
Kiwi	4	4004	80	35	0.44
Kiwi	4	4005	80	48	0.60
Kiwi	4	4006	80	34	0.43
Kiwi	4	4007	80	110	1.38

Edit: Added sample data as table.