Set Analysis Issue : Weekly "Newcomers" count returning only one row in Table/Chart

[Thibo74]

Hello everyone, 

I'm struggling with a Set Analysis formula to calculate the weekly volume of "Newcomers" in a historical tracking application similar to Jira.

Context : 

I have a snapshot-based data model where each "issue" (identified by issue_id) is recorded every Monday (DD/MM/YYYY) . I want to create a table (or bar chart) showing the volume of "Newcomers" for each week.

Logic : 

A issue_id is considered a "Newcomer" on Week W if : 

1. It exists in the snapshot of Week W (WeekDate field)
2. It did not exist in the snapshot of Week W-1 (PreviousWeekDate field)

The problem  : 

When I use the following formula in a table with WeekDate as a dimension, I only get one single row (usually the last week) instead of the full history : 

Count({$
 <WeekDate = P(WeekDate),
 issue_id = E({$ <WeekDate = P(PreviousWeekDate)> })
 >} DISTINCT issue_id)

It seems that P(PreviousWeekDate) is calculated globally for the entire object, ignoring the row-level context of my dimension. 

How can I force the Set Analysis to evaluate the Previous Week comparison for every single row of my table (a row is a week) ?

Result expected:

WeekDate	Count of NewComer
23/02/2026	12
02/03/2026	8
09/03/2026	15
...	...

Thanks for your help!

[BIAKS]

Can you try this:

Count(
  Aggr(
    IF(
      Count(Issue_ID) + Sum({1} 0) > 0 
      AND 
      RangeSum(Above(Count(Issue_ID)), 0) = 0,
      1
    ),
    WeekDate, Issue_ID
  )
)

[marcus_sommer]

It's not an issue with the set analysis which is quite the same as a selection which is in general global and working against the column-level of the data-set.

If a row-level comparing is wanted it must be applied within any conditional-functions - most common are if-loops. In your scenario it won't work against a period-information as object-dimension because the data of the current week doesn't belong to the previous one and revers. It's not a matter of any condition else it relates to the relationship of the data.

A quite similar topic is discussed here: new customers trend over time - Qlik Community - 2544952 

[Daniel_Castella]

Hi @Thibo74 

These transformations are better to be applied in the script. For example, if your input data are two columns issue_id and WeekDate, you can apply this transformation in the script:

MAP:
Mapping
LOAD
Date(WeekDate+7,'DD/MM/YYYY')&issue_id as PastWeek,
'0' as Flag
FROM [lib://DataFiles/Test week dates.xlsx]
(ooxml, embedded labels, table is Sheet1);

A:
LOAD
WeekDate,
issue_id ,
ApplyMap('MAP',Date(WeekDate,'DD/MM/YYYY')&issue_id,1) as Flag
FROM [lib://DataFiles/Test week dates.xlsx]
(ooxml, embedded labels, table is Sheet1);

In this way, you create a flag field that will be 1 for the Newcomers and 0 for the repeated ones.

Then in front end, you just need to apply this formula:

count({<Flag={'1'}>} issue_id)

You can find my data example attached.

Kind Regards

Daniel

[Thibo74]

@Daniel_Castella : I can't calculate the number of incoming issues in a script because the scope of incoming issues can vary depending on the filters (for example, I want to see issues with priority P0). If an issue was P1 last week and then becomes P0 this week, then it's considered an incoming issue. I need everything to be done dynamically.

@marcus_sommer : 

I'll explore another way to bypass the row-level context limitations in the table:

1. I will create a flag in the script (Flag_Previous_Week)

2. I will load the data from Week W-1  and force WeekDate to align with Week W

3. This will allow every row in my table to 'see' both the curent state and the previous state of Issue_Id simultaneously 

By doing this, I think use Set Analysis like that : 

Count({$
<Flag_Current_Week = {1}, 
Issue_Id = E({
   $<Flag_Previous_Week={1}>
})
>}
Issue_Id)

 If I filtering on a specific dimension like Priority I think I will work...

I will update this thread if it works as expected.

[Daniel_Castella]

Hi @Thibo74 

Well, if the priority needs to be considered is as easy as introducing it in the mapping and the applymap key like:

Date(WeekDate+7,'DD/MM/YYYY')&issue_id &Priority as PastWeek,

I think it can also be done with a self join if a big number of filters are needed. I could try to refine the code if you provide us with a data sample, to better replicate your issue in our end.

Kind Regards

Daniel 

[Thibo74]

Hello, 

Hi all,

Following my previous messages, I wanted to share the solution I’ve implemented to track weekly Inflow and Outflow.

To compare a ticket's state between Week N and Week N−1 on a single time axis, I performed a self-concatenate in the script. I projected the previous week's data onto the current week's date using two flags.

Initial Data:

Issue_ID	WeekDate	Status	Title	priority
1000	09/03/2026	Status3	Test Issue 1	P0
1001	09/03/2026	Status2	Test Issue 2	P1
1002	09/03/2026	Status1	Test Issue 3	P1
1000	02/03/2026	Status2	Test Issue 1	P0
1001	02/03/2026	Status2	Test Issue 2	P1

Realigned Data (Current vs. Previous flags):

Issue_ID	WeekDate	Status	Title	priority	Flag_Current_Week	Flag_Previous_Week
1000	09/03/2026	Status3	Test Issue 1	P0	1	0
1001	09/03/2026	Status2	Test Issue 2	P1	1	0
1002	09/03/2026	Status1	Test Issue 3	P1	1	0
1000	09/03/2026	Status2	Test Issue 1	P0	0	1
1001	09/03/2026	Status2	Test Issue 2	P1	0	1
1000	02/03/2026	Status2	Test Issue 1	P0	1	0
1001	02/03/2026	Status2	Test Issue 2	P1	1	0
1000	02/03/2026	Status1	Test Issue 1	P0	0	1
1001	02/03/2026	Status1	Test Issue 2	P1	0	1

By aligning both records on the same WeekDate, I can now identify an Inflow (a new ticket) by checking if the ID exists in the current snapshot but has a sum of 0 in the previous one:

Count({<Flag_Current_Week={1}>} DISTINCT 
    If(Aggr(Sum(Flag_Previous_Week), WeekDate, Issue_ID) = 0, Issue_ID)
)

In my case, if I filter only issues with Status2, I have 2 inflow at 02/03/2026 and 1 outflow at 09/03/2026

I don't understand why E() didn't work something like that : 

Count({$
<Flag_Current_Week = {1}, 
Issue_Id = E({
   $<Flag_Previous_Week={1}>
})
>}
Issue_Id)