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jkrewpbi's avatar
jkrewpbi
Frequent Visitor
10 years ago

Generate calculated table containing rows where count of a value is greater 0 for each user

I know this is easy, but I can't see to find the right combination of steps to do what I'm trying to do.   I currently have TABLE_ALL that contains three columns (for example sake):  user(int) cont...
CahabaData's avatar
CahabaData
Icon for Memorable Member rankMemorable Member
10 years ago

To clarify: you state

confirmed(True or False) 

Then: "a user might have 0, 1, 2, or 3 confirmed contact methods"

 

so then it would be a record count? (since the field is true/false) meaning that if 0 contacts you want to count False and only false?

 

 

 

 

jkrewpbi's avatar
jkrewpbi
Frequent Visitor
10 years ago

For your question "so then it would be a record count? (since the field is true/false) meaning that if 0 contacts you want to count False and only false?".

 

Almost - I need the list of users that have False for EVERY contact_method.

 

Another way to look at it (I've added the campaign column in this example - just for context since this table is tied to others via the campaign:

 

TABLE_A:

campaign  user_id       contact_method   confirmed

X              123            email                  False

X              123            text                    False

X              321            email                  True

X              321            text                    False

 

So for my calculated table, I'm looking to get the following result:

 

campaign  user

X              123

 

I felt it would be kind of odd to stick a calculated column in TABLE_A, because whle the data exists in TABLE_A to determine which users did not confirm at all, I tend to think of keeping calculated/summary data separate from non calculated.

 

 

  • CahabaData's avatar
    CahabaData
    Icon for Memorable Member rankMemorable Member
    10 years ago

    It would be easier for me to revert to SQL logic rather than DAX for something like this.  I would:

    a. created a calculated table: TableFalse      that is just the false records

    b. created a calculated table: TableTrue      that is just the true records

    c. do an outer join on the ID field of TableFalse to TableTrue so all False records result

    .....in this resulting table some rows will have the TableTrue fields as blanks

    d. create a calculated table: NoMethod       that filters out the records with blank TableTrue fields (only 1 field is needed)

     

    this might not get you quite there - - air code and haven't completely thought thru every possible variation.....but will get you close

     

    am pretty sure I forgot to include Distinct.... you'll want 1 record per ID in each table true/false......

     

     

     

     

     

    • jkrewpbi's avatar
      jkrewpbi
      Frequent Visitor
      10 years ago
      CahabaData

       

      I've created two tables with something like the following:

       

      calc_conf_false = FILTER('TABLE_A','TABLE_A'[confirmed]=FALSE())

      calc_conf_false = FILTER('TABLE_A','TABLE_A'[confirmed]=TRUE())

       

      But when I attempt to perform a left outer join I get an 'No common join columns detected' error. They have the same number of columns.

       

      calc_joined_conf_falsetrue = NATURALLEFTOUTERJOIN(calc_conf_false,calc_conf_true)

       

      Now here's where I'm treading into newish territory. I can do this via SQL but I haven't created a join as you mentioned in PowerBI before, so I'm not quite certain how it's automatically trying to join these tables togther.

       

      I'm tackling both approaches on this thread since I can see cases where I need to break out data in Power BI that comes from non-relational structures. So in this case, I can see breaking out multiple tables from one. Am I totally on the wrong path the way I'm creating the calculated tables to begin with, perhaps?

  • OwenAuger's avatar
    OwenAuger
    Icon for Super User rankSuper User
    10 years ago

    Hi jkrewpbi

     

    Here are two ways I can think of to do what you just described, i.e. produce table containing campaign & user_id for user_ids with no confirmed contact_method:

     

    TABLE_NO_CONFIRMED_V1 = 
FILTER (
    SUMMARIZE ( TABLE_A, TABLE_A[campaign], TABLE_A[user_id] ),
    CALCULATE (
        COUNTROWS ( TABLE_A ),
        ALLEXCEPT ( TABLE_A, TABLE_A[user_id] ),
        TABLE_A[confirmed]
    )
        = 0
)
    TABLE_NO_CONFIRMED_V2 = 
CALCULATETABLE (
    SUMMARIZE ( TABLE_A, TABLE_A[campaign], TABLE_A[user_id] ),
    EXCEPT (
        VALUES ( TABLE_A[user_id] ),
        CALCULATETABLE ( VALUES ( TABLE_A[user_id] ), TABLE_A[confirmed] )
    )
)
    • jkrewpbi's avatar
      jkrewpbi
      Frequent Visitor
      10 years ago

      Thanks for your response. I'm evaluating your suggestion along with the above suggestion to split out the tables and peform a join.

       

      Both of the examples you provided produce results - running into some weirdness in that the calculated tables are not picking up some of the users that have values of False for all confirmation methods. Troubleshooting my backend data and how Summarize works, to see if perhaps users are being eliminated that belong to multiple notifications. Thanks again and I might have to follow up once I get a better handle on the pattern surrounding the rows I'm not seeing.

      • OwenAuger's avatar
        OwenAuger
        Icon for Super User rankSuper User
        10 years ago
        No problem, there may have been something about the structure/interpretation of your full table that I didn't understand.
        For example, do you care about users on a per campaign basis?
        Anyway, get back in touch if needed.
        Owen ;)
  • v-caliao-msft's avatar
    v-caliao-msft
    Icon for Microsoft Employee rankMicrosoft Employee
    9 years ago

    Hi jkrewpbi,

     

    Maybe you needn't create additional table in your report, you can add some calculation in your table. I have tested it on my local environment, you can refer to the calculations below.

     

    Calculated column:

    Confirmed_0 = IF(TABLE_ALL[Contact_Method_0]="False",0,1)Total = CALCULATE(SUM(TABLE_ALL[Confirmed_0]),ALLEXCEPT(TABLE_ALL,TABLE_ALL[Campaign],TABLE_ALL[User_ID]))Total2 = CALCULATE(DISTINCTCOUNT(TABLE_ALL[User_ID]),FILTER(ALLEXCEPT(TABLE_ALL,TABLE_ALL[Campaign]),TABLE_ALL[Total]=0))

     

    Measure:

    Measure = CALCULATE(SUM(TABLE_ALL[Confirmed_0]),ALLEXCEPT(TABLE_ALL,TABLE_ALL[Campaign],TABLE_ALL[User_ID]))

     

    Create a table looks like below:

    Regards,

    Charlie Liao