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Hello! I have a Power Bi dashboard that I want to display in a warehouse to show performance of workers. I followed this helpful video (https://www.youtube.com/watch?v=xhCqCkf0gMQ) that gave me the exact result I wanted.
The issue is now I have a model that has a live connection to another semantic model. Before the auto scroll effect worked when the data was coming from excel sheets that had exported data from a certain date.
Now with the live connection dashboard it has a page filter that only shows data from the desired range. The issue is arrising when I use the Index column measure
Index = RANKX(all('Labor_data'),'Labor_data'[Associate Num], ,ASC)
it is indexing all of the data back to 2023. It is bypassing the page filter I have set for the desired date. So I changed the function to collect what is being displayed in the table, a measure, and I get the circular dependendacy error.
I was wondering if there is a way to fix this to auto scroll to only display data in the table rather than all of the data in the live connection creating thousands of groups. Or if there is another way to create an auto scroll effect. Thank you!
Solved! Go to Solution.
The circular dependency error is likely caused because the [Performance Measure] referenced within the RANKX function relies on the same table or measure context that is being ranked. This is a common issue, particularly when the ranking is performed in the same visual as the performance measure calculation, creating a recursive dependency.
Index =
RANKX(
ALLSELECTED('Labor_data'[Associate Num]),
CALCULATE(MAX('Labor_data'[On Standard % (No Zeros)])),
,
ASC,
DENSE
)
If you continue to experience this issue, please share the .pbix file or a sample version of it. This will allow us to review the data model and measure dependencies in detail, so we can easily identify the root cause and provide better support.
Regards,
Karpurapu D,
Microsoft Fabric Community Support Team.
Hi,
We have not yet received a response regarding your query. Could you please confirm if your issue has been resolved? If not, kindly provide additional details so we can offer further assistance.
Thank you.
We have not yet heard back from you about whether our response addressed your query. If it did not, please share more details so we can assist you more effectively.
Thank You.
Thank you for reaching out to the Microsoft Fabric Community Forum.
To create an auto-scrolling dashboard in Power BI, you can use the RANKX function together with ALLSELECTED. This ensures that ranking only considers data points visible with current filters, like date slicers, and excludes historical data you don't want included. This method helps support the scrolling effect for your dashboard.
You can use the following DAX measure:
Index =
RANKX(
ALLSELECTED('Labor_data'),
[Performance Measure],
,
ASC,
DENSE
)
Be sure to replace [Performance Measure] with the metric you want to use for ranking, such as MAX('Labor_data'[On Standard % (No Zeros)]).
Regards,
Karpurapu D,
Microsoft Fabric Community Support Team.
I get stuck in a circular dependancy error
The circular dependency error is likely caused because the [Performance Measure] referenced within the RANKX function relies on the same table or measure context that is being ranked. This is a common issue, particularly when the ranking is performed in the same visual as the performance measure calculation, creating a recursive dependency.
Index =
RANKX(
ALLSELECTED('Labor_data'[Associate Num]),
CALCULATE(MAX('Labor_data'[On Standard % (No Zeros)])),
,
ASC,
DENSE
)
If you continue to experience this issue, please share the .pbix file or a sample version of it. This will allow us to review the data model and measure dependencies in detail, so we can easily identify the root cause and provide better support.
Regards,
Karpurapu D,
Microsoft Fabric Community Support Team.
Hi @cameronamiller
I wanted to check if you’ve had a chance to review the information provided. If you have any further questions, please let us know. Has your issue been resolved? If not, please share more details so we can assist you further.
Thank You.
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