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JaSp
New Member

Grouping of Multiple Columns in PowerBI-Service

Hi all,

 

I am unfortunately stuck at a visualisation issue within the PowerBI Online App. 

 

The data I'd like to analyse/visualise is structured as follows:

 

Title | LocationA | LocationB | LocationC | Publisher1 | Publisher2

 

Title contains distinct datapoints whereas Location and Publisher contain repeating datapoints (there is one type of type listed in the TypeA column. Here's an example:

 

 

 

The issue is that columns B, C, D and E, F all represent datapoints related to the same category (Publish-Location for B, C, D and Publisher for E, F). However, if I want to create a visualization showing where and by whom my ads have been published (e.g. a bar graph with 3 bars for the publishing locations and a graphical sub-division per bar for each publisher) .

 

Is there any way to group those colums or tell PowerBI that they contain data from the same category (Location and Publisher)?

 

Thanks for your help!

 

Jan

 

 

2 REPLIES 2
v-danhe-msft
Employee
Employee

Hi @JaSp,

Could you please tell me if your problem has been solved? If it is, could you please mark the helpful replies as Answered?

 

Regards

Daniel He

Community Support Team _ Daniel He
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
v-danhe-msft
Employee
Employee

Hi @JaSp,

 

Based on my test, you could refer to below steps:

Sample data:

1.PNG

Unpivot all the columns except [Title] in query editor:

1.PNG

Apply it and create a calculated column in data view:

Group = IF(Table1[Attribute]="Facebook"||[Attribute]="Youtube"||[Attribute]="Linkedin","Group1","Group2")

1.PNG

Now you could create a bar chart and use the Group as legend and publish to service:

1.PNG

You could also download the pbix file to have a view.

 

Regards,

Daniel He

 

 

Community Support Team _ Daniel He
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

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