I have a dataset TransactionId, PolicyId, DebitnoteId and Amount. I need to add cumulative/rolling balances for the policy and debit note balances. I have illustrated what the PolicyID Rolling Balance and the DebitNote Rolling Balance should be using colors to display items which made up the balances in the image below. I am new to DAX and not able to get the balances working without using the Date Functions.
I have not tried it out but here's something that you should give a try:
PolicyID Rolling Balance = calculate(
sumx(values(policiID);[sum(amount) or lastnonblank(amount;0) I'm not sure :/]);
filter(all(Table) ; transactionID [<= or >=] MAx(transaticonID)))
or with earlier function in a calculated column instead of max or in maxx (instead of max: maxx(Table; transactionID[<= >=]earlier(transationID)))
I hope at least it gives you a little hint which is working.
The DebitNote Rolling Balance should be done similarly (with DebitnoteId).
and another note instead of sumx one could use groupby function too, I guess. Like this:
calculate(sum(amount); groupby(table; policyID); /\*filter like before*/)
Oh. And one more thing I used ';' because I have this in my locale settings or somewhere. If it's underscored then you should use ','.
//sorry for this "Answer". There's one answer I want to ask something but I can't, because I need some "reputation"...
Related
I've written some code in Siddhi that logs/prints the average of a batch of the last 100 events. So the average for event 0-100, 101-200, etc. I now want to compare these averages with each other to find some kind of trend. In first place I just want to see if there is some simple downward of upward trend for a certain amount of averages. For example I want to compare all average values with all upcoming 1-10 average values.
I've looked into Siddhi documentation but I did not find the answer that I wanted. I tried some solutions with partitioning, but this did not work. The below code is what I have right now.
define stream HBStream(ID int, DateTime String, Result double);
#info(name = 'Average100Query')
from HBStream#window.lengthBatch(100)
select ID, DateTime, Result, avg(Result)
insert into OutputStream;
Siddhi sequences can be used to match the averages and to identify a trend, https://siddhi.io/en/v5.1/docs/query-guide/#sequence
from every e1=HBStream, e2=HBStream[e2.avgResult > e1.avgResult], e3=HBStream[e3.avgResult > e2.avgResult]
select e1.ID, e3.avgResult - e1.avgResult as tempDiff
insert into TempDiffStream;
Please note you have to use partition to decide this patter per ID is you need averages to be calculated per Sensor. In your app, also use group by if you need average per sensor
#info(name = 'Average100Query')
from HBStream#window.lengthBatch(100)
select ID, DateTime, Result, avg(Result) as avgResult
group by ID
insert into OutputStream;
What might be an alternative way, possibly more effective, for checking if context is total. I use this measure as benchmark:
IsTotal1 = CALCULATE(COUNT(Tab[Store]), ALLSELECTED(Tab)) = COUNT(Tab[Store])
The idea is that it calculates COUNT on a table with filters removed (left side, so we get counts for all dimensions in context) and checks it against the COUNT with current context. If both are the same, we have total.
I know that using the function HASONEVALUE might be tempting:
IsTotal2 = NOT(HASONEVALUE(Tab[Store]))
However, using this approach has a serious drawback. If we make a table displaying sales by store and by product then the first measure will work and the second will fail. Moreover, if we display sales by product only the first measure will still work, and the second should be retyped to HASONEVALUE(Tab[Product]).
So I want the measure to be resistant to any change of granularity due to adding new dimension to table visual.
Based on the information you provided in the comments, it sounds like you have a page- or report level filter. In that case, you can't rely on functions such as ISFILTERED(...) or ISCROSSFILTERED(...), as these external filters or slicers could impact the result returned from these two functions.
So you have to either stick with your approach (perhaps changing COUNT(...) to COUNTROWS(Tab) could improve the performance slightly), or write something like
ISINSCOPE('Tab'[Store]) || ISINSCOPE('Tab'[Product]) || etc...
where you repeat ISINSCOPE for every column that could potentially be used to slice the data, as ISINSCOPE is the only function that distinguishes using a column on a filter/slicer vs. using it as a row/column grouping on a table/matrix visual.
I hope somebody can help me with some hints for the following analysis. The students may do some actions for some courses (enroll, join, grant,...) and also the reverse - to cancel the latest action.
The first metric is to count all the action occurred in the system between two dates - these are exposed like a filter/slicer.
Some sample data :
person-id,person-name,course-name,event,event-rank,startDT,stopDT
11, John, CS101, enrol,1,2000-01-01,2000-03-31
11, John, CS101, grant,2,2000-04-01,2000-04-30
11, John, CS101, cancel,3,2000-04-01,2000-04-30
11, John, PHIL, enrol, 1, 2000-02-01,2000-03-31
11, John, PHIL, grant, 2, 2000-04-01,2000-04-30
The data set (ds) is above and I have added the following code for the count metric:
evaluate
sumx(
addcolumns( ds
,"z+", if([event] <> "cancel",1,0)
,"z-", if([event] = "cancel",-1,0)
)
,[z+] + [z-])
}
The metric should display : 3 subscriptions (John-CS101 = 1 , John-PHIL=2).
There are some other rules but I don't know how to add them to the DAX code, the cancel date is the same as the above action (non-cancel) and the rank of the cancel-action = the non-cancel-action + 1.
Also there is a need for adding the number for distinct student and course, the composite key . How to add this to the code, please ? (via summarize, rankx)
Regards,
Q
This isn't technically an answer, but more of a recommendation.
It sounds like your challenge is that you have actions that may then be cancelled. There is specific logic that determines whether an action is cancelled or not (i.e. the cancellation has to be the immediate next row and the dates must match).
What I would recommend, which doesn't answer your specific question, is to adjust your data model rather than put the cancellation logic in DAX.
For example, if you could add a column to your data model that flags a row as subsequently cancelled, then all DAX has to do is check that flag to know if an action is cancelled or not. A CALCULATE statement. You don't have to have lots of logic to determine whether the event was cancelled. You entirely eliminate the need for SUMX, which can be slow when working with a lot of rows since it works row by row.
The logic for whether an action is cancelled or not moves to your source system (e.g. SQL or even a calculated column in Excel), or to your ETL (e.g. the Query Editor in Power BI) which are better equipped for such tasks. The logic is applied 1 time and then exists in your data model for all measures, instead of needing to apply the logic each time a measure is used.
I know this doesn't help you solve your logic question, but the reason I make this recommendation is that DAX is fundamentally a giant calculator. It adds things up. It's great at filters (adding some things up but not others), but it works best when everything is reduced to columns that it can sum or count. Once you go beyond that (e.g. wanting to look at the row below to adjust something about the current row), your DAX is going to get very complicated (and slow), whereas a source system or the Query Editor will likely be able to handle such requirements more easily.
I have a sheet that has become complex, it has a lot of filters and I need to populate a table with data. I am counting how many times a month we run a product. If we run it once a day, or more, I want to only count that as one.
Date Product Grizzly
1/1 2.5 open
1/1 1.5 closed
1/1 2.5 closed
1/1 2.5 open
Something a little more elaborate than this, but what I need is to count this as one day of 2.5 grizzly closed, instead of two.
Any help? I feel like there is something simple I am missing but cant put my thumb on it and I have spent well over 15 hours working on this.
As found on the following link:
Link to Answer
Try using the DCOUNTA function within Excel.
Although I'm also sure that you could use a pivot chart to provide the same outcome.
EDIT:
I am assuming you are starting with data like the following:
I then created the following table which you can extend to comply with you total data value. For example, you can add extra dates and products.:
The table is populated using a COUNTIFS function.
=COUNTIFS("DateRange",DateCriteria, "ProductRange", ProductCriteria, "GrizzlyRange", GrizzlyCriteria)
So in my table the formula looks like this but the cell references will obviously changes according to your spreadsheet (I used absolute references to make it easier to use the fill handle to copy the formula down:
=COUNTIFS($A:$A,$E4,$B:$B,$F$3,$C:$C,$F$2)
I next nestled this formula in to an IF statement and applied a conditional formatting to end up with the following:
So the formula for each of the cells is now:
=IF("OriginalFormula),"Yes","No")
Example of my 01/01/2015, 1.5 open value:
=IF(COUNTIFS($A:$A,$E4,$B:$B,$F$3,$C:$C,$F$2),"Yes","No")
BUT - You can change the "Yes" and "No" to "1" and "0" if you want to stick with numerical values.
I need to define a calculated member in MDX (this is SAS OLAP, but I'd appreciate answers from people who work with different OLAP implementations anyway).
The new measure's value should be calculated from an existing measure by applying an additional filter condition. I suppose it will be clearer with an example:
Existing measure: "Total traffic"
Existing dimension: "Direction" ("In" or "Out")
I need to create a calculated member "Incoming traffic", which equals "Total traffic" with an additional filter (Direction = "In")
The problem is that I don't know MDX and I'm on a very tight schedule (so sorry for a newbie question). The best I could come up with is:
([Measures].[Total traffic], [Direction].[(All)].[In])
Which almost works, except for cells with specific direction:
So it looks like the "intrinsic" filter on Direction is overridden with my own filter). I need an intersection of the "intrinsic" filter and my own. My gut feeling was that it has to do with Intersecting [Direction].[(All)].[In] with the intrinsic coords of the cell being evaluated, but it's hard to know what I need without first reading up on the subject :)
[update] I ended up with
IIF([Direction].currentMember = [Direction].[(All)].[Out],
0,
([Measures].[Total traffic], [Direction].[(All)].[In])
)
..but at least in SAS OLAP this causes extra queries to be performed (to calculate the value for [in]) to the underlying data set, so I didn't use it in the end.
To begin with, you can define a new calculated measure in your MDX, and tell it to use the value of another measure, but with a filter applied:
WITH MEMBER [Measures].[Incoming Traffic] AS
'([Measures].[Total traffic], [Direction].[(All)].[In])'
Whenever you show the new measure on a report, it will behave as if it has a filter of 'Direction > In' on it, regardless of whether the Direction dimension is used at all.
But in your case, you WANT the Direction dimension to take precendence when used....so things get a little messy. You will have to detect if this dimension is in use, and act accordingly:
WITH MEMBER [Measures].[Incoming Traffic] AS
'IIF([Direction].currentMember = [Direction].[(All)].[Out],
([Measures].[Total traffic]),
([Measures].[Total traffic], [Directon].[(All)].[In])
)'
To see if the Dimension is in use, we check if the current cell is using OUT. If so we can return Total Traffic as it is. If not, we can tell it to use IN in our tuple.
I think you should put a column in your Total Traffic fact table for IN/OUT indication & create a Dim table for the IN & Out values. You can then analyse your data based on IN & Out.