I have a measure calculated in the context of a selected month defined as:
MyMetric = COUNTROWS ( FILTER ( Entities, [Incident Count] > [Target] ) )
I need to calculate the YTD number while calculating each month separately. This is because a single Entity that exceeds the Target in two months needs to be counted twice, whereas a simple YTD calculation would only include it once. For example, when reporting March, a correct result is achieved with:
[MyMetric YTD] = [MyMetric]
+ CALCULATE ([MyMetric] , DATEADD(DateTable[Date], -1 , MONTH))
+ CALCULATE ([MyMetric] , DATEADD(DateTable[Date], -2 , MONTH))
Obviously, this is not the right way to do it. How can this kind of calculation be written efficiently?
Let's suppose that you have a Month column in your DateTable. If not, then you can create one.
Then you can try something along these lines:
MyMetric YTD
= SUMX(
CALCULATETABLE(
VALUES( DateTable[Month] ),
DATESYTD( DateTable[Date] )
),
[MyMetric]
)
Basically, you get a list of each month in the year so far and then sum your measure value for each one of those months.
Related
I need to write a DAX measure that calculates (e.g., "Count Rows"), but only when another measure value is evaluated (e.g., filtering "[Sales]>100"). So if-- in the context of the selected filters-- Sales is great than 100, then the measure is executed only for those rows.
The measure I have defined works in the context of lower smaller grain. But the totals do not sum correctly.
Any suggestions?
DAX Measure
License Usage =
// Users with active viewership in 3 months
IF (
NOT ( ISBLANK (
CALCULATE (
[Activity Date NEWEST],
KEEPFILTERS ( DATESINPERIOD ( dimCalendar[Date], TODAY (), -90, DAY ) )
)
)), 1
)
Activity Date NEWEST =
MAX('PBI Activity'[Date])
Okay, I figured something out that works.
DAX
License Usage =
// Users with active viewership in 3 months
CALCULATE (
[Count Users],
FILTER ( 'PBI Activity', 'PBI Activity'[Date] >= TODAY () - 90 )
)
Count Users = COUNTROWS('Users')
Also, I later came across this article which looks like it also does what I was hoping to do: Execute calculate expression over filtered rows based upon measure filter.
Reference: Specifying multiple filter conditions in CALCULATE - SQLBI
DAX
DEFINE
MEASURE Sales[Big Sales Amount] =
CALCULATE (
[Sales Amount],
KEEPFILTERS (
FILTER (
ALL ( Sales[Quantity], Sales[Net Price] ),
Sales[Quantity] * Sales[Net Price] > 1000
)
)
)
EVALUATE
SUMMARIZECOLUMNS (
Sales[Quantity],
"Sales Amount", [Sales Amount],
"Big Sales Amount", [Big Sales Amount]
)
How can I get the last day of month from the last date with sales? I want the result to be returned as a DAX calculated table.
The first step, calculated table:
LastDate = LASTNONBLANK( T[Date], CALCULATE( SUM( T[Amount] ) ) )
It returns a table (one column, one row) with the last date with sales. So far, correct, as expected. Say the last day with sales is 2020-04-15. But I want the end of month so 2020-04-30.
I hoped this should work, but it doesn't.
LastDate =
ENDOFMONTH (
LASTNONBLANK (
T[Date],
CALCULATE ( SUM ( T[Amount] ) )
)
)
It still returns 2020-04-15, instead of expected 2020-04-30.
The problem is caused by using dates from table T. Instead, you need to use dates from the calendar table, because list of dates for the time intelligence functions must be continuous.
Assuming you have a properly designed and connected calendar table 'Date' in your data model, change your DAX to:
LastDate =
ENDOFMONTH (
LASTNONBLANK (
Date[Date],
CALCULATE ( SUM ( T[Amount] ) )
)
)
ENDOFMONTH will use now a different table (Date), because LASTNONBLANK function will keep data lineage to it.
I having trouble calculating the cumulative sum of a column on PowerBI.
I have a big offer table and I want to run a pareto analysis on it. Following many tutorials, I created a SUMMARIZED table by offer and a sum of their sales. So the table definition is:
summary = SUMMARIZE(big_table; big_table[offer]; "offer sales"; sum(big_table[sales]))
Many of the forums and stackoverflow answers I found have direct me to the following formula for cumulative sum on column:
cum_sales =
CALCULATE(
sum([offer_sales]);
FILTER(
ALLSELECTED(summary);
summary[offer_sales] <= max( summary[offer_sales])
)
)
However the resulting table is not correct:
What I need is simply to have the offers ordered by sales descending and then add the current row's sales amount to the previous row's sales,
So I excepted numbers closer to:
1st row: 1.5M
2nd row: 2.1M
3rd row: 2.6M and so on
But (maybe) because of my data structure and (certainly) lack of knowledge on how PowerBI works, I'm not getting the right results...
Total Amount = SUM ( 'Fact'[Amount] )
Offer Visual Cumulative =
VAR OfferSum =
ADDCOLUMNS (
ALLSELECTED ( 'Offer'[Offer] ),
"amt", [Total Amount]
)
VAR CurrentOfferAmount = [Total Amount]
VAR OffersLessThanCurrent =
FILTER (
OfferSum,
[amt] <= CurrentOfferAmount
)
RETURN
SUMX (
OffersLessThanCurrent,
[amt]
)
There's no need to pre-aggregate to a summary table. We can handle that as in the measure above.
This assumes a single fact table named 'Fact', and a table of distinct offers, 'Offer'.
Depending on what you're doing in terms of other filters on 'Offer', you may need to instead do as below:
Offer Visual Cumulative =
VAR OfferSum =
ADDCOLUMNS (
ALLSELECTED ( 'Offer'[Offer] ),
"amt", CALCULATE ( [Total Amount], ALLEXCEPT ( 'Offer', 'Offer'[Offer] ) )
)
...
The rest of the measure would be the same.
The measure is fairly self-documenting in its VARs. The first VAR, OfferSum is a table with columns ('Offer'[Offer], [amt]). This will include all offers displayed in the current visual. CurrentOfferAmount is the amount for the offer on the current row/axis label of the visual. OffersLessThanCurrent takes OfferSum and filters it. Finally, we iterate OffersLessThanCurrent and add up the amounts.
Here's a sample:
I have a table which contains a list of products scores by date:
From this table, I have to make a plot of the cumulative percentage of each quality by date.
At this moment I have the percentage of each class by day:
For that I used this measurement:
Measure =
CALCULATE (
SUM ( Table1[Percentage_By_Class] ),
FILTER ( Table1, Table1[Date] = MAX ( Table1[Date] ) ),
ALLEXCEPT ( Table1, Table1[Score] )
)
/ CALCULATE (
SUM ( Table1[Percentage_By_Class] ),
FILTER ( ALL ( Table1 ), Table1[Date] = MAX ( Table1[Date] ) )
)
But this only considers the percentage of each day. I need to consider all previous dates. E.G. for day 2 I need to consider days 1 and 2, for day 3 I need to consider days 1,2,3 and so on.
How can I accomplish this?
in my opinion, you need a calendar for the date first, you can create a table easily bay dax function =CALENDARAUTO() And mark it as a calendar table,
After that, you can use a DATEMTD or a DATEYTD function for your coding purpose.
here are the steps:
1 - https://learn.microsoft.com/en-us/dax/calendarauto-function-dax
select left pane --> table --> modelling / create table and add dax formula
2- reference the table as a date table, right click on the table from the right pane
after then you can use data functions like YTD MTD ,
new measure :
1st mesure : AVG1 = AVG(DATA_)
2nd measure : YTD AVG ALL = CALCULATE([AVG1];DATESYTD(CALENDAR[DATE]))
REF: https://learn.microsoft.com/en-us/dax/dateadd-function-dax
then you can use MONTH(CALENDAR(DATE)) on left and YTD AVG as a value at any table...
regards.
SUNAY
I need some help creating a measure in PowerPivot. I have Googled and tried all the options I could find with success. I have a Fact table with sales leads. Some of the leads gave us a sell some not. I need to measure the value of the leads. I sum the values and divide it with the number of records I have in the table
Average Total of Leads:=calculate(Table1[Sum of Value]/Table1[Count of Lead name])
My problem is to create the measure which give me 3 months rolling average.
I have tried:
Roll3Average:=[Average Value of leads]/CALCULATE(DISTINCTCOUNT(dimdate[MonthName]),
DATESINPERIOD(dimdate[Dates],
LASTDATE(dimdate[Dates]),-3,Month
)
)
I have tried:
Rolling3Average:=IF(COUNTROWS(VALUES(dimdate[MonthName])) = 1,
CALCULATE(
[Average of Value]/ COUNTROWS(VALUES(dimdate[MonthName] ) ) ,
DATESBETWEEN(
dimdate[Dates],
FIRSTDATE(PARALLELPERIOD(dimdate[dates], -2, MONTH)),
LASTDATE(PARALLELPERIOD(dimdate[dates], 0, MONTH))
), ALL(DimDate)
)
)
I have tried:
Total Sales rolling:=
CALCULATE (averagex(Table1,[Average Total of deals]),
FILTER (ALL ( dimdate),dimdate[Month] >= MAX (dimdate[Month]) -2&& dimdate[Month] <= MAX ( DimDate[Month])))
I cannot get it right.
I hope someone can see where I go wrong.
#Marcus
Click here and see my datamodel Thanks
I have still troubles with my data mode.
I have linked a very simplified example. I hope someone can help me.
Thank you
Note in the example I am using, I am using [Sales] instead of leads.
The main structural change you will want to make is to create a month_index in your dimDates table. The advantage of have that field is that it makes calculating the total over 3 months easier, since it removes having to handle cross year like in your 2nd example. The other advantage is having a month_index will handle non standard calendars, e.g. 4-4-5
To start with:
Sales:=SUM(Data[Qty])
The next part would be to calculate the sales(or leads) over the 3 months. The below we are using the month_index field to quickly define the date range in which we want to sum.
3_Month_Sales :=
CALCULATE (
[Sales],
FILTER (
ALL ( dimdate ),
dimdate[Month_index] <= MAX ( dimdate[Month_index] )
&& dimdate[Month_index]
>= MAX ( dimdate[Month_index] ) - 2
)
)
The next part depends on the ask, since the average could be calculated two ways. The main question is it a 3 month average, or is it based on the numerator being the number of months that have a value greater then 0.
The simple way:
3_Month_Average:=DIVIDE( [3_Month_Sales], 3)
The more complex way, in which I learned you can wrap SUMX in a calculate. The idea being the calculate is looking at the period for your 3 months, and then the sumx is iterating down by year then Month. At the end it is checking if sales is greater then 0, if it then 1 is assigned. Then those 1's are summed
Count_of_Periods :=
CALCULATE (
SUMX (
VALUES ( dimdate[Year] ),
SUMX ( VALUES ( dimdate[Month] ), IF ( [Sales] > 0, 1 ) )
),
FILTER (
ALL ( dimdate ),
dimdate[Month_index] <= MAX ( dimdate[Month_index] )
&& dimdate[Month_index]
>= MAX ( dimdate[Month_index] ) - 2
)
)
And then finally
3_Month_Alternative:=DIVIDE([3_Month_Sales], [Count_of_Periods])
Below would be an image using some random sample data, and how the different fields interact. As part of the example the April 2017 data was removed, to show how the count_of_periods calculation was able to handle the fact there was no data in that period