I just want to select a range of days in a slicer and show in a table the number of days for each month/period (month-year).
I used DAX to create a table with the information I need and I don't have problems with the periods (first column), it changes dinamically, the problem is the column "Days" (second column) because it's always showing the total number of days for each month.
Here my DAX code
SelectedPeriods = GROUPBY(DimDate;DimDate[Period];"Days";COUNTX(CURRENTGROUP();DimDate[DateKey]))
Here the result
What I expect is:
2 for april, 31 for may, 1 for june
This is an issue with execution order.
SelectedPeriods = GROUPBY(DimDate;DimDate[Period];"Days";COUNTX(CURRENTGROUP();DimDate[DateKey]))
Generates a calculated table. These are calculated when the data model is refreshed and stored in it. They are not refreshed each time a connected dimension is changed within a dashboard.
In your case, while changing date filters may hide rows from this table the number of days remains fixed at the number calculated initially when there was no filter context on the data i.e. counting all days in the month.
If you want the result to change then you need to use a measure instead of a calculated table. Measures react to the current filter context within the report and so will adjust their output each time a slicer is changed.
The needed measure will depend on your model but might be something as simple as:
CountOfDays := CountRows(DimDate)
Related
I'm very stuck and I was hoping you can help. So have the following dataset (Table 1) with Month (5 years worth), Customer (1000 customers), Product (100 products), Units and Value (value is just unit multiple a price). The data only shows rows with unit and value, so for customers when there is no sale in a month, there is no data.
Click here for Table 1
I want to create a table (Table 2) where every product for every customer is shown for all time periods, where actual units and values are included and those missing in Table 1 are now showing 0.
Click here for Table 2
I have read many posts here and elsewhere, which only handles 1 column (e.g. only Customer not both Customer AND Product, and only 1 measure not Unit and Value). I tried to adapt the code but failed miserably.
I also want to do this in Power BI using M not DAX, because I would like to further transform the data.
Thank you so much everyone!!!
Good afternoon.
You can use DAX to create a calendar for the required period of time.
Use the minimum and maximum values from Table 1 for the interval (Calendar function).
Calendar = CALENDAR(Date(2022,5,1), TODAY() -1)
Link the calendar to the necessary dates.
calendar link example
In the settings of the table to which you will output data, select the "Show items without data" setting, and take the date from the calendar.
I am trying to recreate a Tableau table view in Power BI where I can compare two customizable date ranges and show the percent differences across metrics as a calculated row.
Reference Screenshot from Tableau
In practice, my table will have 3 rows: 1 row for each time period selected and a row for the percent difference. The percent difference row is a nice to have, not a need. All my table metrics are coming from the same source.
I also need to set up two different date slicers that each row of my table will reference. I've played around a little with setting up a separate date table for the comparison period to be selected from, following this thread: Comparing Data Across Date Ranges
The challenge now is showing two separate periods in the same table.
Appreciate any guidance!
I'm pretty new to Power BI. I'm unsure how to approach this.
I have one visualization that displays the ten most frequently bought products in a time frame that is set by a slicer. In another visualization, I display how those products have been selling over the past few years (this time frame is not determined by the slicer). I want to display only the ten products that come from the first visualization, not the ten most common over the time frame in the second visualization.
How can I accomplish this? The approach I have in mind (and I'm open to others) is to create a true/false column that changes with the first visualization. "True" would be for products that are frequently bought as determined by the first visualization in the slicer-determined time range, and the second visualization would only look at values with a "true" in that column. How can I create a column (or table, maybe?) that changes depending on a visualization?
Clarification: most of the pages will say Top10 ... Actually, the measure used was a simple Top5 that includes products with the same number of orders than the 5th product. Therefore, to avoid dealing with larger images, 7 products will be seen but it is a Top5 ranking. The idea is you can replace it with your custom TopN measure.
What I understood:
The simplification of your model plus the disconnected help table would be:
I have one visualization that displays the ten most frequently bought
products in a time frame that is set by a slicer.
The Date slicer belongs to the Dates table in the Data model.
The table viz represents the number of rows in the sales table in the
current context (for each product within the Date range).
The table viz is sorted according to the [#Rows] measure in descending
order.
The table viz only presents the TopN products even without the presence
of the [#Rows] measure due to the presence of the [TopOrders]
measure within Filters on this visual. [TopOrders] is 1.
On the second page you create:
A slicer with the Dates[Date] column (the same one used on the
previous page).
A matrix with Products[ProductName] on the rows, HDates[Year] on
the columns, and a measure on values.
From the View tab, you select the Sync Slicers option.
Inside the Sync Slicers pane:
In the Sync column, check the boxes related to the necessary pages.
In the Display column uncheck the box that contains the over
years report.
So far all we have done is pass the time frame context from page 1 to page 2.
Since the TopN context depends on the time frame context, we can now use the [TopOrders] measure as a Filters on this visual in the matrix. Again, [TopOrders] is 1.
Why do the numbers differ between rows and not between columns?
Also, in this example, the Sales table only has information up to 12/31/2020 but the visualization shows an additional year and the Sales[Amount] values for each order is $1 so that [#Orders] and [SalesAmount] are the same for easy comparison.
HDates is not related to the model and for each combination of HDates[Year]-Products[ProductName], the [SalesAmount] measure is using the information coming from the previously hidden slicer and the respective Products[ProductName] because the information coming from HDates[Year] has no effect yet.
In order to complete this exercise, it only remains to modify the [SalesAmount] measure in such a way that it removes the filter on the time frame (Dates[Date]) and it recognizes HDates[Year] as Dates[Year].
SalesAmount :=
CALCULATE(
SUM(Sales[Amount]),
ALL(Dates),
TREATAS(VALUES(HDates[Year]),Dates[Year])
)
And this is the final result.
I hope it works for someone or the idea can be improved.
I have a "strange" problem in visualizing the values of the "last" year (that is the most current year with data of a full year). In the example delivered it is 2019 which holds the bookings facts. The year should be derived by a related table (LetztesVollesJahrDim) that holds a min/max date and year value for each company/entity. The DateDim table holds all dates and is configured to be the Date dimension.
The relations are shown in the graphic below.
I created a Report that should display different matrix tables with different values:
one, that shows the last full year's monthly values by Cost Center (which works correct)
one, that shows all years monthly values including an estimated value for the current year (2020) (which also works correct)
one, that could drill down to the details level of the Facts and display the figures per month (in columns) of the last full year (i.e. 2019) putting the Cost Center and other groups down the most detailed level into rows. This Matrix makes use of Calculated Measures created with Tabular Editor.
At this point I can here you say... try it without the Calculated measures and indeed I did that by simply displaying a Card that got a Visual Filter to DateDim[M]=7 to simulate the appliance of each matrix's column. Problem is the same: The Month filter of the Visual (or within the Calculated Measure) is ignored and the yearly sum is displayed (~48k) which is wrong.
As I am currently an Expert in SQL Server DBMS but not in the Architecture of Power BI's DAX Models I am not truely aware about the consequences when and why filters are removed, ignored, overwritten or added at which level of modeling.
Originally I tried to create a simple Calculated Measure that reflects the last full year of the company. That works, but its usage in the huge Matrix was impossible as it was calculating forever. That's why I created the simple table "LetztesVollesJahrDim" to hold a persisted value for each company.
The idea is simple: Create a Query that inner joins these tables and display the Sum for each month [M] like this:
Fact[Turnover] - Fact[CompanyKey] -> CompanyDim[CompanyKey] <-> LastFullYear[CompanyKey] - LastFullYear[MostCurrentYear] -> DateDim[Y] - DateDim[DateKey] -> Fact[StapleDateKey]
So, what is the Problem?
I tried a couple of DAX queries and all come up with a different wrong value.
Following three different approaches for a Calculated Measure "Sum LY":
1.
Σ LY = CALCULATE(SUM(KontobuchungenFact[UmsatzNegiert]), DATESBETWEEN(DateDim[DateKey], DATE(2019, 1, 1), DATE(2019, 12,31)))
2.
Σ LY = CALCULATE(SUM(KontobuchungenFact[UmsatzNegiert]), SAMEPERIODLASTYEAR(DateDim[DateKey]))
[
3.
Σ LY = CALCULATE(SUM(KontobuchungenFact[UmsatzNegiert]), KEEPFILTERS(DateDim[M]), USERELATIONSHIP(DateDim[J], LetztesVollesJahrDim[AktuellstesJahr]), USERELATIONSHIP(DateDim[DateKey], KontobuchungenFact[StapelDateKey]))
Filters being applied on the page/visual:
CompanyKey
DateKey >= 2018-01-01 (page filter that limits the displayed rows in yearly matrix to the last 3 ys)
Some other irrelevant keys
Visual configuration:
Relations:
Values/Rows in the LetztesVollesJahrDim table:
Calculated Measures/Table in Tabular Editor showing the calculation of the "01 Jan" column of the first Matrix, which displays correct results:
Impression of the Report:
So in summary I need a clue/DAX formula that recognizes the Monthes in each column and ideally uses the relations to the last year table trespassing the year's filter through DateDim to the Facts.
It is funny, that the upper matrix works, but not the one on the bottom. It is not possible to use the calculated measures approach of the first matrix in the last matrix because the performance would drop to > minutes calculation. So I cannot use the same approach and need a fast one.
Anybody an idea? :-)
I have a table in Power BI, where I have two columns like Date and Daily Targets. Daily Targets are always same on the same date so I need a measure to only SUM 1 value for 1 date instead of calculating every row because these two columns contains duplicate values. Please see at attached screenshot for the data table.
As you look into my data, there are two distinct dates and all I need is when I add this Daily Target Column in any visualization, instead of adding 11653+11653+11653 for 3rd Jan, it should only Sum 11653 for 3rd Jan. Please help me with it, I will be very grateful to you.
To get a measure that takes the maximum value of the Daily Target by date, you can do something like this:
Daily Target = SUMX(GROUPBY(Table1, Table1[Date], "Max Daily Target", MAXX(CURRENTGROUP(), [DailyTarget])), [Max Daily Target])
Assuming your table is called Table1
The inner GROUP BY says to identify the highest daily target for each date. This assumes any given date will only have a single daily target (you could equally pick the MIN or AVG as they should all result in the same number). Note, if you have a single date with 2 different daily targets, this formula will fall down because it will only pick the biggest.
The outer SUMX sums each day's biggest daily target. This is important if you are aggregating by month or year. At the end of January, you want to have up to 31 daily targets added together.
Note: In general, I would roll up the daily target by day before loading the data into Power BI. It's not fully clear from your screenshot why you have records at a lower granularity, so I can't explain how I'd do it in your particular case. However, this post by DAXPatterns.com does go into how to handle "sales vs. budget", which may be relevant to you: http://www.daxpatterns.com/handling-different-granularities/