DAX Formula and Measure Question

[PrivateAnalytic]

Hello everyone! My data is depicted below (1st picture). It is pretty simple, I created the Total Subtasks Completed and the % of PP Progress columns. The total subtasks completed is the sum of the all the different subtask result columns, and the % of PP progress uses the values in the Total Subtasks completed columns divided by 17 because thats how many different subtasks there are. On the second picture I attached, I made a quick table visual which shows how many subtasks and their % of PP progress a person has. I was looking to create a measure or column which can bucket the % of PP Progress into a 25% bucket. Such as those between 0 and 25% are in 1, 26-50% in another, 51-75% in a third, and lastly 76-100%. Is there a DAX Calculation I can use to make this successful?

[Anonymous]

Hi @PrivateAnalytic ,

Try a measure like below:

Measure =
var %Progress = sum([% of PP Progress])
Return
SWITCH(
    TRUE(),
    %Progress <= 0.25, 1,
    %Progress > 0.25 && %Progress < = 0.5, 2,
    %Progress > 0.5 && %Progress <=0.75, 3,
    4
)

[FreemanZ · Accepted solution]

Supposing your table named Data, try to add a column with the code below:

PctRange =

SWITCH(

    TRUE(),

    Data[% of PP Progress] <=0.25, "0-25%",

    Data[% of PP Progress] <=0.5, "26%-50%",

    Data[% of PP Progress] <=0.75, "51%-75%",

    "76%-100%"

)

[PrivateAnalytic]

I got this error when I tried it <ccon>A circular dependency was detected: PP-Task Completion[% of PP Progress], PP-Task Completion[PctRange], PP-Task Completion[% of PP Progress].</ccon>

[FreemanZ]

Rewrite your column as below:

% of PP Progress =

CALCULATE(

    DIVIDE(Data[Total Subtasks completed],17),

    ALLEXCEPT (Data[Total Subtasks completed])

)

[Syk]

You can group your data without DAX! Click the column you're wanting to group and under column tools > data groups > New data groups

Then you can toss those %s in buckets

[PrivateAnalytic]

I never knew this! When I tried to do it, I got this popup which I've never seen before.