I'm trying to create a measure which can give me the primo date value for a given timeframe eg. start date of: current month, current year, YTD etc, I'm using calculation groups, but i've boiled the problem down to this.
In this example i'm tying to find the value of the stat date of current YTD-period, which should be 01/01/2022 :
primotest =
var primoDateValue = TOTALYTD(
FIRSTDATE( Dato[FuldDato])
, Dato[FuldDato]
)
//in my actual measure i have to filter the primoDateValue by [level2]-column, which i why it is important to include it in this example.
var randomMeasure =
CALCULATE(1
,'Dim funktionsbudgetter'[level2] = "this doesn't matter"
)
return
primoDateValue
for some unknown and really weird reason the randomMeasure interacts with the primoDateValue measure.
if 'Dim funktionsbudgetter'[level2] = "this doesn't matter" is included in randomMeasure. the result is incorrect
if I remove ''Dim funktionsbudgetter'[level2] = "this doesn't matter" the value is correct
My datamodel looks like this:
I'm guessing that i have somekind of problem with my datamodel. but no matter what that problem is. I can't think of a single scenario that causes an interaction between primoDateValue and randomMeasure.
I'm hoping that some of you can think up a scenario that would cause the weird interaction between the measures. so that I can figure out where the problem actually is.
okay so i figured out the problem or actually problems.
it turns out that it is a combination of using time intelligent functions eg. TotalYTD, calculation groups (CG) and composit models.
basically, you should be really careful when using CG and composit models.
in short, you can't use remote data with a local defined CG and vice versa. it will straight up ignore the CG item and just return the normal measure.
you can read more about it here: sqlbi - guide
I also found that the time intelligence function doesn't always play nice with composit models. which i also the problem in my "boiled-down" measure in my OP.
I don't know exactly why. it works in other scenarios.
for now i can just rewrite my measure to avoid TI functions
Related
I'm kind of new to DAX and I'm basically learning as I'm using it in my work. We are building reports in PowerBI and we have data model that gets data from Oracle database. So I'm using DAX to create measures in this data model.
I need to substract 2 numbers from each other. So I created simple measure which looked like this:
[MEASURE1] - [MEASURE2]
Whether it works or it doesn't depends on my Period filter which uses another table. I don't know how could period be related to any of this. So when I change filter to some values, I get normal number. However, when I switch it to different values, I get numbers like 2,27483058473905E-13.
Weird thing is that if I check those two measures that I'm subtracting, they have exactly the same numbers, so the difference should be 0.
I know this is not the best explanation, but it is impossible to describe entire data model here. So I'm just looking for some ideas what could possibly be causing this and what should I check.
I have literally no idea what could be causing this.
Floating point precision.
Either use fixed decimal data types, specify the format string of the measure, or wrap your measure in ROUND, e.g.:
Diff =
ROUND (
[Measure 1] - [Measure 2] ,
2
)
2,27483058473905E-13 is not a huge number, but as close as a decimal calculator can get to zero.
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.
Apologies up front, this is going to take a second to explain but I'd rather be thorough so the problem is clear. The main problem that I am having is that people are not being properly filtered out of a matrix that I am using and I believe I have isolated the problem to some measures that I have written. Below is a picture of what I'm working with (I guess you'll have to follow the link):
My problem is that when I try to apply a filter using the slicer, you would expect the available names to be filtered out of the matrix based on if they fall under the particular team leader or director selected. However, they do not, as seen in this picture:
Clearly, people are not being filtered out because there are still values in the AnswerRatePass column and the SurveyScorePass column. I will focus on just AnswerRatePass to cut the problem in half. The purpose of AnswerRatePass is to output text to identify if the Answer Rate of a person exceeded their goal, met the goal, or missed the goal. My code for AnswerRatePass is:
AnswerRatePass = if([AnswerRateGoal] = 1,"Exceed",
if([AnswerRateGoal]=0,"Achieved","Missed"))
As you surely noticed, [AnswerRatePass] is using the value of [AnswerRateGoal] to determine what to do. I use this because it sort of simplifies the code for my Recommendation measure, which assesses if a person met their goals for both criteria, meaning we suggest to promote this person, or what their case is. My code for AnswerRateGoal is:
AnswerRateGoal =
if(ISBLANK([AnswerRate]),0,
if(values(TMD[Title])="Executive",
if([AnswerRate]>=.94,1,
if([AnswerRate]>=.92,0,-1)),
if(values(TMD[Title])="President's Club",
if([AnswerRate]>=.96,1,
if([AnswerRate]>=.94,0,-1)),
if([AnswerRate]>=.96,0,-1)
)))
If these measures (AnswerRatePass, SurveyScorePass, and Recommendation) are removed from the matrix, the slicers work exactly as expected. So, long story short, what I'm pretty sure I need is some sort of filter for these measures so that these values will disappear when using a slicer that doesn't apply to that person, allowing the individual person to also be filtered out by the slicer as well. Unless someone has a different idea, I'm pretty sure that's what I need but I haven't been able to come up with a way to do it. I'm open to any help/suggestions.
Yep, I've seen this before. I think what you want is for AnswerRatePass to return a blank if AnswerRate is blank. Try something like this:
AnswerRatePass =
IF(
ISBLANK([AnswerRate]),
BLANK(),
IF(
[AnswerRateGoal] = 1,
"Exceed",
IF(
[AnswerRateGoal] = 0,
"Achieved",
"Missed"
)
)
)
Side note: The SWITCH function is often a cleaner approach then nested IF functions.
I have a classroom of students with test scores taken weekly. I expect the test results to improve over time. I want to identify a poor performer as an outlier based on not improving over time using SAS (have 9.2). Also are there accepted criteria for being an outlier for part of the time interval but not the complete time interval? This is the bulk of my present code (not looking for outliers yet, just longitudinal analysis):
proc mixed data= XYZ_LONG ;
title1 'XYZ Analysis';
class group day subject ;
model TV = group day group*day / ddfm=satterthwaite;
repeated day / type = cs sub = subject ;
Your definition of "poor performer" is not a definition of outlier, I don't think. However:
If you want to find people who did not improve over time, that's pretty easy, but you have to define it more precisely. Did not improve between any two weeks? The first and last weeks? Something else?
And what do you mean by "not improve" exactly? Do you mean it literally (same or worse score at later time?)
In either case, I'd use an array and find difference scores and then identify difference scores that were negative (or whatever you want).
However, if you are going to be doing modelling, then an outlier should probably be defined in terms of that model - that is, in your model, accounting for group. But if you have a lot of outliers and they aren't bad data, you should not throw out those people, but use a better model.
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.