I am struggling to get this going and could need some help. I have the following setup:
Order Item Material Value
22 1 100 27,5
22 1 200 27,5
22 1 300 27,5
22 2 100 33
22 3 500 101
26 1 500 88
26 1 600 88
I have duplicate values becaue of the Material, so I want to group by Order, Item and Value and then calculate the total Value in a DAX measure.
After grouping:
Order Item Value
22 1 27,5
22 2 33
22 3 101
26 1 88
The final Value:
Total Measure = 249,5
I tried the following DAX expression for the Total Measure:
Total Measure = Summarize('Table1'; 'Table1'[Order]; 'Table1'[Item]; "Sum Value:"; Sum('Table1'[Value]))
It gives me the error:
Multiple columns cannot be converted to a scalar value
So I tried:
Total Measure = Sumx('Table1'; Summarize('Table1'; 'Table1'[Order]; 'Table1'[Item]; "Sum Value:"; Sum('Table1'[Value])))
But this didnt work either. For every help thanks in advance.
The following code should be what you are looking for
Measure1 =
SUMX (
SUMMARIZE (
Table1;
Table1[Order];
Table1[Item];
Table1[Value];
"TotalSum"; SUM ( Table1[Value] )
);
[Value]
)
In this case, you can simply use the VALUES function instead of SUMMARIZE.
Total Measure = SUMX ( VALUES ( Table1[Value] ), [Value] )
This iterates over each unique Value and adds Value to the sum.
Related
I have 4 Categories (GP, ID, Age, Date). I would would like to create calculated column and group by GP, ID, and Age and Rank/ count by Date to see how many months each member has in past 24 month.
My Code works until I have members who cancelled their membership for a few months and then resumed after. I need to restart from the first month after skip. for example :
GP ID AGE DATE RKING Desired RANK
1 220 35-44 202206 12 6
1 220 35-44 202205 12 5
1 220 35-44 202204 12 4
1 220 35-44 202203 12 3
1 220 35-44 202202 12 2
1 220 35-44 202201 12 1
1 220 35-44 202012 24 24
1 220 35-44 202011 23 23
1 220 35-44 202010 22 22
1 220 35-44 202009 21 21
1 220 35-44 202008 20 20
1 220 35-44 202007 19 19
1 220 35-44 202006 18 18
1 220 35-44 202005 17 17
1 220 35-44 202004 16 16
… … … … … …
1 220 35-44 201901 1 1
This is what I have tried but doesn't work for dates skipping.
RKING Column=
RANKX (
CALCULATETABLE (
VALUES ('tbl'[Date] ),
ALLEXCEPT ( 'tblW', 'tbl'[GP], 'tbl'[ID] ),
'tbl'[AGE] = 'tbl'[AGE],
'tbl'[date] >= start_date && 'tbl'[date] <= end_date // date slicer
),
[Date] ,
,ASC
)
Looking through the code you were trying to make a measure for a visual (For a calcCol the measure is added as well). And as I got a point, you want to show a sum of consequtive months in a matrix for each date in accordance to ID/GP/AGE/DATE I see a following way.
As you know, calculations performs for each row in a matrix and filter the data model according to data presented in matrix rows and columns (slicers as well). So, my idea is -
Get date from matrixRow and use it as max date for the table.
Then use a FILTER(). FILTER() is an iterative function, so it goes throw each row and checks filtering condition - if true row remains if false - not.
I use following filtring conditions:
Get dateInMatrix-dateInACurrentTableRow (for example: 202203-202201= 2 months)
Then check how many rows in the table with min=202201 and max<202203
if there are less rows then date difference then it FALSE() and the row is out of table.
3) The last step is counting of rows it a filtered table.
A measure for matrix:
Ranking =
VAR matrixDate=MAX('table'[DATE])
VAR filteredTable =
FILTER(
ALL('table')
,DATEDIFF(
DATE(LEFT([DATE],4),RIGHT([DATE],2),1)
,DATE(LEFT(matrixDate,4),RIGHT(matrixDate,2),1)
,MONTH
)
=
VAR dateInRow=[DATE]
RETURN
CALCULATE(
COUNTROWS('table')
,'table'[DATE]>=dateInRow
,'table'[DATE]<matrixDate
)
)
RETURN
COUNTROWS(filteredTable)
[![enter image description here][1]][1]
A measure for calcColl:
RankColl =
VAR currentDate=[Start_Date]
Var MyFilt={('Table'[AGE],'Table'[ID],'Table'[GROUP])}
VAR withColl =
ADDCOLUMNS(
CALCULATETABLE(
'table'
,ALL('Table')
,TREATAS(MyFilt,'Table'[AGE],'Table'[ID],'Table'[GROUP])
)
,"dateDiff",
DATEDIFF(
[Start_Date]
,currentDate
,MONTH
)
,"RowsInTable",
VAR dateInRow=[Start_Date]
Var startDate=IF(dateInRow<currentDate,dateInRow,currentDate)
VAR endDay =IF(dateInRow>currentDate,dateInRow,currentDate)
VAR myDates = GENERATESERIES(startDate,endDay,1)
RETURN
COUNTROWS(
CALCULATETABLE(
'Table'
,ALL('Table')
,TREATAS(MyFilt,'Table'[AGE],'Table'[ID],'Table'[GROUP])
,TREATAS(myDates,'Table'[Start_Date])
)
)
)
VAR filtered =
FILTER(
withColl
,[dateDiff]=[RowsInTable]-1 -- for ex.:
-- dateDiff=01/01/2022-01/01/2022=0,
-- but it will be 1 row in the table for 01/01/2022
)
RETURN
CountRows( filtered)
I have a Redshift database with the following entries:
table name = subscribers
time_at
calc_subscribers
calc_unsubscribers
current_subscribers
2021-07-02 07:30:00
0
0
0
2021-07-02 07:45:00
39
8
0
2021-07-02 08:00:00
69
17
0
2021-07-02 08:15:00
67
21
0
2021-07-02 08:30:00
48
23
0
The goal is to calculate current_subscribers with the previous value.
current_subscribers = calc_subscribers - calc_unsubscribers + previous_current_subscribers
I do the following:
UPDATE subscribers sa
SET current_subscribers = COALESCE( sa.calc_subscribers - sa.calc_unsubscribers + sub.previous_current_subscribers,0)
FROM (
SELECT
time_at,
LAG(current_subscribers, 1) OVER
(ORDER BY time_at desc) previous_current_subscribers
FROM subscribers
) sub
WHERE sa.time_at = sub.time_at
The problem is that in the sub query "sub" a table is generated that is based on the current values in the table, and thus previous_current_subscribers is always 0. Instead of going through this row by row. So the result is: current_subscribers = calc_subscribers - calc_unsubscribers + 0 I have also already tried it with CTE, unfortunately without success:
The result should look like this:
time_at
calc_subscribers
calc_unsubscribers
current_subscribers
2021-07-02 07:30:00
0
0
0
2021-07-02 07:45:00
39
8
31
2021-07-02 08:00:00
69
17
83
2021-07-02 08:15:00
67
21
129
2021-07-02 08:30:00
48
95
82
I am grateful for any ideas.
The problem you are running into is that you want to use the result of one row in the calculation of the current row. This is recursive which I think you can do in this case but is expensive.
The result you are looking for is the sum of all calc_subscribers for this row and previous rows minus the sum of all calc_unsubscribers for this row and previous rows. This is the difference between 2 window functions - sum over.
sum(calc_subscribers) over (order by time_at desc rows unbounded preceding) - sum(calc_unsubscribers) over (order by time_at desc rows unbounded preceding) as current_subscribers
I'm calculating the difference of "closed column". All data is in one column and I'm calculating the difference between Row2-Row1 for all the rows. I'm getting results as some positive values and some negative. Positive values are coming correct but negative values are incorrect. I'm applying the formula
diff =
Table3[Value] -
CALCULATE(
SUM (Table3[Value]),
FILTER(
Table3,
Table3[Index] = EARLIER(Table3[Index])- 1
)
).
Screenshot of my formula
Output after applying formula, -ve and +ve values
Please help how can I correct my -ve values?
Month Week Month End Closed Open GT IN
01/2020 W01-2020 N 71 178 249 71
01/2020 W02-2020 N 284 189 473 213
01/2020 W03-2020 N 550 210 760 266
01/2020 W04-2020 N 861 185 1046 311
01/2020 W05-2020 Y 1185 205 1390 324
02/2020 W06-2020 N 370 206 576 370
02/2020 W07-2020 N 665 209 874 295
In Power Query Editor, I have added an Index column started from 1 to the data and the output is as below-
Now, create this below measure to get previous rows Closed value in the current row-
prev_row_closed_value =
CALCULATE(
SUM (your_table_name[Closed]),
FILTER(
ALL(your_table_name),
your_table_name[Index] = MIN(your_table_name[Index]) - 1
)
)
For calculating difference, use this below measure-
diff =
MIN(your_table_name[Closed]) -
CALCULATE(
SUM (your_table_name[Closed]),
FILTER(
ALL(your_table_name),
your_table_name[Index] = MIN(your_table_name[Index]) - 1
)
)
Here is output from the above measure-
In Dax you can use the following formulas.
In step one we create a column to get your Week Column in an order:
YearWeek = CONVERT(RIGHT(Sheet1[Week], 4) & MID(Sheet1[Week],2,2),INTEGER)
This is creating an integer value our of your year and month. Next we can use this to get the previous closed amount to be substracted where we filter first on the correct month. Be aware that I take the assumption this is a date column.
In =
var curMonth = Sheet1[Month]
var curYW = Sheet1[YearWeek]
var filterMonthYW = FILTER(Sheet1, curMonth = Sheet1[Month] && curYW > Sheet1[YearWeek])
var MaxYW = CALCULATE(MAX(Sheet1[YearWeek]), filterMonthYW)
return Sheet1[Closed] - CALCULATE(MAX(Sheet1[Closed]), FILTER(filterMonthYW, MaxYW = Sheet1[YearWeek] ))
Sheet1 is your table..
End result:
I have two tables, with:
Entrydate, several categories
ChurnDate, several categories
The categories are connected via different tables, and the dates are connected with a Calendar.
Now I want to calculate how many customers I have. So I have following DAX formulas
1. SumChurn =
CALCULATE(
SUM('kuendigungen'[KUENDIGUNG]);
FILTER(
ALLSELECTED('Calendar'[Date]);
ISONORAFTER('Calendar'[Date]; MAX('Calendar'[Date]); DESC)
)
)
2. SumEntry =
CALCULATE(
SUM('eintritt'[NEUMITGLIED]);
FILTER(
ALLSELECTED('Calendar'[Date]);
ISONORAFTER('Calendar'[Date]; MAX('Calendar'[Date]); DESC)
)
)
3. TotalCustomers = SumEntry - SumChurn
This works, but in my diagram I want to filter the dates, so that it only visualizes 2020 or the last 3 years.When I do this the calculation is wrong because it only counts in this interval.
Is there a solution that I can filter the date in my visuals but in my calculation the start date of the cummulative sum is always fixed?
I dont't want a new column because I still want to filter my categories of customers...
Thanks,
Michaela
Edit: Try to explain it clearer
Example Table 1: contains new customers
Date unique_id1 unique_id2 unique_id3 cat1 cat2 cat3 cat4 cat5 cat6
1886-02-01 2070030124 550261 207000152145 207 0 0 1 0 0
1887-01-01 4350002756 4081878 435000010707 435 0 0 1 0 0
1888-01-01 7030000597 3206858 703000001279 703 0 0 1 0 0
1888-06-01 7030016696 3208056 703000005002 703 0 0 1 0 0
1888-09-01 8210024182 204124 821000008664 821 1 0 1 0 1
1889-01-01 7050055324 1988250 705000018309 705 1 0 1 0 0
1889-01-01 8250000278 439485 825000600296 825 0 0 1 0 0
1889-05-01 7030023754 3208355 703000000884 703 0 0 1 0 0
1889-10-01 2110071206 2849359 211000330019 211 0 1 1 0 0
1889-10-01 2110071236 2851371 211000120014 211 0 0 1 0 0
1889-11-14 5190529889 4260192 519000123846 519 1 0 1 0 0
1890-07-01 7330349030 4819467 733000013102 733 0 0 1 0 0
1890-07-01 7330152914 4817492 733000075604 733 1 0 1 0 1
1890-07-01 8190000889 486170 819000215708 819 0 0 1 0 0
1890-07-01 8190444976 486199 819000215740 819 0 0 1 0 0
1890-12-01 8190001388 476049 819000100005 819 0 0 1 0 0
1891-01-01 7030001248 3206975 703000000043 703 0 0 1 0 1
Example Table 2: contains leaving customers
similiar to table 1
Example Calendar Table:
01.01.1990
02.01.1990
03.01.1990 ... (till today)
Output shut be a measure
for each day in calendar: number of customer at this date = cumulative_sum(newcustomer) - cumulative_sum(churncustomer)
I get exactly this output, when I run the calculations I wrote, but I want the measure in a way, ehen I filter the date, the sum is still the cummulative sum from the very first date, otherwise the numbers are wrong.
Edit3:
I did exactly the same thing, as mkrabbani posted, but it doesnt't work for me, following calculations:
TotalKuendigungen =
CALCULATE(
SUM('kuendigungen'[KUENDIGUNG]);
FILTER (
ALL ( 'Calendar'[Date] );
( 'Calendar'[Date] <= MAX ( ( 'Calendar'[Date] ))
)))
TotalNeukunden = CALCULATE(
SUM('eintritt'[NEUMITGLIED]);
FILTER (
ALL ( 'Calendar'[Date] );
( 'Calendar'[Date] <= MAX ( ( 'Calendar'[Date] ))
)))
AnzahlMitglieder = [SummeNeumitglied] - [SummeKuendigung]
This is how it looks for me: (Neukunden: new customers, kündigungen: leaving, aktuellemitglieder: number of customers)
Picture 1 correct calculation
Picture 2: also correct calculation, but filter doesnt work
thanks for adding some sample data with more explanation. If I get your requirement correct, this below steps with explanation will help you solving your issue I hope.
Assumption: If my understanding is correct, you have 3 tables with Date, new_customer and leaving_customer and they are related as below diagram shown.
Now, I have created some sample data for 10 days, to visualize your requirement/issue. Hope, cumulative counts in the below table is correctly calculated (using basics of cumulative calculation).
At this stage, you need a measure that will calculate current number of customer for each row based on calculation > "cumulative_new_customer - cumulative_leaving_customer" which is not a tough job for you.
But, you are having issue when you are slicing your data using Date slicer. If you are selecting date number 5, which is "January 05 2020" in my sample data. You wants the final counts based on date January 01 to 05, but you are getting only counts from one single date "January 05 2020".
If the above explanation is correct, I would suggest to write 3 separate Measure as explained below in this answer. You can have a look on the output in the below picture I have added with comparison with before and after slicing the data. You can see the number of current user for "January 05 2020" is 41 for both case (Before and After Slicing)
Now, if everything above is meeting your expectation, you can use this below 3 measures as written.
1.
cumulative_new_customer =
CALCULATE (
COUNT(new_customer[unique_id]),
FILTER (
ALL ( 'Dates'[Date] ),
'Dates'[Date] <= MAX ( 'Dates'[Date] )
)
)
2.
cumulative_leaving_customer =
CALCULATE (
COUNT(leaving_customer[unique_id]),
FILTER (
ALL ( 'Dates'[Date] ),
'Dates'[Date] <= MAX ( 'Dates'[Date] )
)
)
3.
number_of_cutomer_today = [cumulative_new_customer] - [cumulative_leaving_customer]
Hope the above details will help you.
I have a matrix Power BI visualization which is like
Jan Feb Mar April
Client1 10 20 30 10
Client2 15 25 65 80
Client3 66 22 54 12
I have created 3 what if parameters slicer table (having values from 1 to 4) for each client
For example, If the value of the first slicer is 1 and the second is 2 and the third is 2 then I want
Jan Feb Mar April
Client1 0 20 30 10
Client2 0 0 65 80
Client3 0 0 54 12
That is, it should replace the value with zero. I have been able to achieve that for one client using Dateadd function (by adding month)
Measure = CALCULATE(SUM('Table'[Value]),
DATEADD('Table'[Column], Parameter[Parameter Value], MONTH))
and I have used this measure to display the value, but how to make it work for the other two clients as well .
Let say you have three parameter tables as follows
Parameter1 Parameter2 Parameter3
Value1 Value2 Value3
------ ------ ------
1 1 1
2 2 2
3 3 3
4 4 4
and each of them has its own slicer. Then the measure you are after might look something like this:
Measure =
VAR Val1 = MAX(Parameter1[Value1])
VAR Val2 = MAX(Parameter2[Value2])
VAR Val3 = MAX(Parameter3[Value3])
VAR CurrClient = MAX('Table'[Client])
VAR CurrMonth = MONTH(DATEVALUE(MAX('Table'[Month]) & " 1, 2000"))
RETURN SWITCH(CurrClient,
"Client1", IF(CurrMonth <= Val1, 0, SUM('Table'[Value])),
"Client2", IF(CurrMonth <= Val2, 0, SUM('Table'[Value])),
"Client3", IF(CurrMonth <= Val3, 0, SUM('Table'[Value])),
SUM('Table'[Value])
)
Basically, you read in each parameter and compare them to the month in the current cell.