My question is that I'd like to calculate a daily average taking into account days with zeroes.
Let me clarify it:
I'd like to calculate the average daily value of units for each category, with the following table:
When I sum up the values for each day and category, I get:
I'd like to include in the average calculation the zeroes.
I use the following code:
SUMMARIZE(
Data,
Data[Category],
"Average",
AVERAGEX(
SUMMARIZE(
Data,
Data[Date],
"Sum of Units",
SUM(Data[Units])
),
[Sum of Units]
)
)
But the problem is that for category B it doesn't take into account those days with 0s.
Could you please guide me how to solve it?
Thanks in advance!
Jorge
One way to solve it would be to create a calendar table, which can then be used to count the number of days in any of your grouping periods. This also means you can use non standard calendars, for example something like a 4-4-5
With a calendar table created you can leverage the FIRSTDATE and LASTDATE functions in DAX.
I recommend adding a past dates column to the calendar table, which can be created using DAX with the following formula. The today function in DAX when used in a calculated column will only evaluate when the model is updated.
In my example I created a calculated column in the date table called Past_Dates
Past_dates =IF( TODAY() > [Date], TRUE(), BLANK())
So for example if I create the following measures, the Today one just being used for illustration.
Start_date:=FIRSTDATE( Dates[Date] )
End_Date:=LASTDATE( Dates[Date])
Today:=TODAY()
EndPhased:=CALCULATE( LASTDATE( Dates[Date] ), Dates[Past_dates] = TRUE())
Which when added to an empty pivot table evaluate to the following.
Note that you would want to have year somewhere in the pivot if you have multiple years of data.
The idea of having the Past flag is to keep from counting days where they would not be any data due to being in the future. So for example in September it would only use 11 days of sales and not the full 30.
As the below example shows, the finding of the start and end date even work on a Quarter basis.
So now that we have a way to get the Start and End date of a period, the next step is adding it into our Calculated measure.
In the below example, we are iterating though every unique Category name. Within the category, we are summing the units sold, and then dividing by the number of days between EndPhased and Start_Date + 1. Then averaging the results by the number of categories that have data in that period.
Average:=AVERAGEX (
VALUES ( Data[Category] ),
CALCULATE ( DIVIDE ( SUM ( Data[Units] ), [EndPhased] - [Start_date] + 1 ) )
)
It seems to me that you need to have an underlying row with the zero value in it (in your initial Data table). Right now, you don't actually have a zero value for B on 02/01/2017. If you add a row with the values | 02/01/2017 | B | 0 | I believe you will see that the average accounts for it. As things stand for you right now, I believe the pivot table actually reflects no value (blank) rather than zero value, so the zero isn't currently counted for the average.
I think the best way is to add the missing zeroes, using UNION function:
SUMMARIZE(
Data,
Data[Category],
"Average",
AVERAGEX(
UNION(
SUMMARIZE(
Data,
Data[Date],
"Sum of Units",
SUM(Data[Units])
),
ADDCOLUMNS(
EXCEPT(
ALL(Data[Date]),
VALUES(Data[Date])
),
"Sum of Units",
0
)
),
[Sum of Units]
)
)
Related
I am very new to this power pivot and dax Commands ,kindly help me out here
my sample table looks like this :
Now to calculate monthly efficiency i need to make a measure which satisfies below criteria:
Sum the efficiency for each distinct date & divide by the count of distinct dates for the month
so far for days my below formula is working , but when i group the date into month it is only showing the sum, so kindly help me how to achieve this ;
Actual Efficiency CL2:=CALCULATE(SUM(CL1[Day Wise Efficiency]),DISTINCT(CL1[Date (dd/mm/yy)]))
Divide part is not in your formula, below DAX might help:
Actual Efficiency CL2 :=
DIVIDE (
CALCULATE ( SUM ( CL1[Day Wise Efficiency] ) ),
CALCULATE ( DISTINCT ( CL1[Date (dd/mm/yy)] ) )
)
Note that DISTINCT ( CL1[Date (dd/mm/yy)] ) is not required in the sum as dates in rows will be unique.
To Divide the sum at month level DISTINCT ( CL1[Date (dd/mm/yy)] ) is required as we need how many days a month has.
At day level there will always be 1 to divide by.
Thanks
My Data is in weekly buckets. I want to split the number into a monthly number but, since there is an overlap in days falling in both the months, I want a weighted average of the data in terms of days that fall in each of the months. For example:
Now, in the above picture, I want to split that 200 (5/7*200 in Jan, 2/7 in Feb). How can I do that using Excel/ Power Pivot/ Dax Functions? Any help here is much appreciated.
Thank you!
Assuming your fact table looks something like below. Values are associated with the starting date of the week it occurred.
Although it may actually be a more granular data, having multiple rows for each week with additional attributes (such as identifiers of a person, a store, depending on the business), what being shown below will work the same.
What we need to do first is to create a date table. We can do that in "Design" tab, by clicking "Date Table", then "New".
In this date table, we need to add a column for starting date of the week which the date of each row is in. Set the cursor to "Add Column" area, and input following formula. Then rename this column to "Week Start Date".
= [Date] - [Day Of Week Number] + 1
Now, we can define the measure to calculate the number allocated to each month with following formula. What this measure is doing is:
Iterating over each row of the fact table
Count the number of days for the week visible in the filter context
Add the value portion for the visible days
Value Allocation := SUMX (
MyData,
VAR WeekStartDate = MyData[Week]
VAR NumDaysInSelection = COUNTROWS (
FILTER (
'Calendar',
'Calendar'[Week Start Date] = WeekStartDate
)
)
VAR AllocationRate = DIVIDE ( NumDaysInSelection, 7 )
RETURN AllocationRate * MyData[Value]
)
Result in the pivot table will be looking like this.
I am working on a matrix in Power BI and I am not figuring out how to sum each column recursively until the total:
And this should be the resulting matrix (as an example, rows):
Some clarifications:
The months (columns) are dynamically generated based on the transaction month. I could filter the data to get the same data for only three months.
"Nombre proveedor" stands for "Vendor name".
I don't care about "Total" row.
These are my values:
So, I think I should create a measure with DAX to replace "Accounting Balance" to sum the previous column (month) or show nothing (to avoid zeroes).
Searching on internet I found several sites to get the running totals by rows, but not by columns.
Any suggestions?
Try Something like this:
Maesure =
CALCULATE (
[Accounting Balance],
FILTER (
ALL ( 'table' ),
'table'[Transaction month] <= MAX ( 'table'[Transaction month] )
)
)
I have a fact table that covers several weeks of tasks in MS Project, and each week is timestamped with that week's week-ending date. I am trying to get the previous week's remaining work for certain tasks (they have a flag called Should Finish This Week set to TRUE).
I have a formula that enables me to create a pivot table that, when I manually sum the values column (it's basically a MIN of the previous week's remaining hours by assignment id), it gives me the result I want.
However, I cannot figure out how to "build this pivot table in DAX" so that I can summarize the resulting sum of hours by department. Right now, I just have the number of hours by task (assignment id) for last week.
Here's the formula:
Simple Min :=
CALCULATE (
MIN ( DataMerge[PreviousWeekRemainingWork] ),
AssignmentHistory[ShouldFinishThisWeek] = TRUE (),
DataMerge[BestPercentComplete2] < 1,
DataMerge[CompletedFLAG] = FALSE (),
USERELATIONSHIP ( 'Calendar'[Date], DataMerge[End of Week] )
)
And with that formula, I can output the following pivot table (this is just a small sample):
The desire is to get a SUM of the Simple Min value by Department. I've tried all the tricks of PowerPivot that I know. Here' the latest iteration of a formula I tried, but the amounts by department are all incorrect, and the grand total is off by about 1200 to the high side.
HRS Remaining More Simple :=
SUMX (
SUMMARIZE (
DataMerge,
DataMerge[ResourceDepartments_R],
DataMerge[AssignmentId]
),
[Simple Min]
)
Any help on this last formula would be greatly appreciated. When I manually sum the pivot table, I come up with ~4300 hours, which is the correct number, so I can verify the formula once given.
Thanks!
I'm trying to show the total closing balance by month for the dataset below:
[Tranche] [Maturity Date] [Balance]
T1 1-Jan-16 1000
T2 2-Jan-16 200
T3 1-Jan-16 3000
T3 3-Jan-16 2900
T1 31-Jan-16 1000
T2 1-Feb-16 200
T3 31-Jan-16 3000
T3 2-Feb-16 2900
I have joined the dataset (table LoanSched) with a dates lookup table (Dates).
Here's the DAX calculated field formula:
=CALCULATE (
SUM(LoanSched[Balance]),
FILTER ( Dates, Dates[FullDate] = MAX(LoanSched[Maturity Date]) )
)
However, I get the result below which is incorrect. Since Tranche T2's balance ends on a date earlier than T3, the balance is excluded in the monthly total. The way the dataset works, is that the total balance should included balances that appear on the last day of each month and tranche. I'm missing the tranche condition.
I need to calculate the correct balances (highlighted in yellow) below:
So what you have here is a form of a semi-additive measure, though I don't quite understand that grand total as it relates to the subtotals - what it says to me is that each "tranche-maturity date" combination is an independent instrument, so it doesn't entirely make sense to use traditional time intelligence - like instead of months that could just be some other arbitrary hierarchy. Is that correct?
Anyway, based on your criteria, what you want is basically
a calculated measure that returns the last non blank balance within a month for a given tranche;
another measure which adds up that measure for each tranche to get a "maturity month balance";
and then a final measure that adds up that measure for each maturing month to get a "total balance".
For #1, this is the traditional formula:
TrancheEndingBalance := CALCULATE (
SUM ( ClosingBalance[Balance]),
LASTNONBLANK (
Dates[FullDate],
CALCULATE ( SUM ( ClosingBalance[Balance] ) )
)
)
And then #2 is just a SUMX across tranches:
MaturityMonthEndingBalance :=
SUMX ( VALUES ( ClosingBalance[Tranche] ), [TrancheEndingBalance] )
And #3 a SUMX across maturity months:
TotalEndingBalance :=
SUMX ( VALUES ( Dates[MonthYear] ), [MaturityMonthEndingBalance] )
Please note these measures essentially only work for the layout you've described, but it sounds like that's the only way to get at the correct balance for a given set of tranches and maturity dates, so form follows function, as it were.