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Anonymous
Not applicable

Help Applying Calendar Functions

I am currently using a date table in Power BI that is built as follows:

 

Date =
ADDCOLUMNS (
CALENDAR (DATE(2000,1,1), DATE(2025,12,31)),
"DateAsInteger", FORMAT ( [Date], "YYYYMMDD" ),
"Year", YEAR ( [Date] ),
"Monthnumber", FORMAT ( [Date], "MM" ),
"YearMonthnumber", FORMAT ( [Date], "YYYY/MM" ),
"YearMonthShort", FORMAT ( [Date], "YYYY/mmm" ),
"MonthYear", FORMAT ([Date], "mmm-yyyy"),
"MonthNameShort", FORMAT ( [Date], "mmm" ),
"MonthNameLong", FORMAT ( [Date], "mmmm" ),
"DayOfWeekNumber", WEEKDAY ( [Date] ),
"DayOfWeek", FORMAT ( [Date], "dddd" ),
"DayOfWeekShort", FORMAT ( [Date], "ddd" ),
"Quarter", "Q" & FORMAT ( [Date], "Q" ),
"YearQuarter", FORMAT ( [Date], "YYYY" ) & "/Q" & FORMAT ( [Date], "Q" )
)

 

This has served me well in the ability to represent calendar based performance.  That said, I am looking for someone who can help me define Week Number as part of this representation.

 

Additionally, because my business has a very particular calendar I am looking for some guidance on how I can build or leverage a "custom calendar" to anaylyze business results.  For instance 1/1/2017 fell on a Monday, while 1/1/2016 was on a Friday.  Clearly, in 2017 the first week of 2017 allowed more business days than 2016.  How can I compare performance of Week 1 of each year given the variability in the calendar?

 

So here's my questions boiled down:

 

  1. Using the DAX statement above, how can I incorporate Week Number (based on standard/traditional) calendar?
  2. What is the best way to do WOW, MOM and YOY analysis?
  3. Can I create a custom calendar that allows me to normalize calendars given the busienss day variability in calendars?

 

Thanks for all the help.  

1 REPLY 1
v-chuncz-msft
Community Support
Community Support

@Anonymous,

 

Take a close look at this article: Week-Based Time Intelligence in DAX.

Community Support Team _ Sam Zha
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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