I have a table in Excel, utilizing Power Pivot that I then display/filter using a Pivot Table. Within my dataset, calculating a ratio within Power Pivot that "sums" correctly in the pivot table based on slicers is fine - this utilizes a SUMX(Cost)/SUMX(Total) and everything works fine. By sums correctly, I mean if I further break down the data set based on Region/State/Product/Employee, all those Rows sum up correctly for the ratio percentage.
The dataset is filtered based on a single month or range of months. The result of this works fine for either the single month or range of Months. What I'm trying to do is within my Pivot Table, show a current month ratio AND a year to date ratio. I've tried messing around with equations I've found online, but nothing seems to work. This includes the following attempts:
=CALCULATE([Cost],[ProductID]="224594")/CALCULATE([Total],[ProductID]="224594")
=SUMX (FILTER(ALL('TableName'),PATHCONTAINS ('TableName'[ProductID], EARLIER('TableName'[ProductID]))),'TableName'[Cost]) / SUMX(FILTER(ALL('TableName'),PATHCONTAINS('TableName'[ProductID], EARLIER ('TableName'[ProductID]))),'TableName'[Total])
I need the "sumifs" to sum the cost for Product A for all months divided by the sum of total for Product A for all months. I do not want to hard code in the the Product ID into the equation, but simply sum all previous records for that product, but I can't seem to get this to work.
Any suggestions?
Sample Data Set
I used the calculate and filter functions in a column instead of trying to use them in a measure, which fixed the problem.
Related
I'm trying to create a calculated column based on a derived measure in SSAS cube, this measure which will count the number of cases per order so for one order if it has 3 cases it will have the value 3.
Now I'm trying to create a bucket attribute which says 1caseOrder,2caseOrder,3caseOrder,3+caseOrder. I tried the below one
IF([nrofcase] = 1, "nrofcase[1]", IF([nrofcase] = 2, "nrofcase[2]",
IF([nrofcase] = 3, "nrofcase[3]", "nrofcase[>3]") )
But it doesn't work as expected, when the level of the report is changed from qtr to week it was suppose to recalculate on different level.
Please let me know if it case work.
Calculated columns are static. When the column is added and when the table is processed, the value is calculated and stored. The only way for the value to change is to reprocess the model. If the formula refers to a DAX measure, it will use the measure without any of the context from the report (eg. no row filters or slicers, etc.).
Think of it this way:
Calculated column is a fact about a row that doesn't change. It is known just by looking at a single row. An example of this is Cost = [Quantity] * [Unit Price]. Cost never changes and is known by looking at the Quantity and Unit Price columns. It doesn't matter what filters or context are in the report. Cost doesn't change.
A measure is a fact about a table. You have to look at multiple rows to calculate its value. An example is Total Cost = SUM(Sales[Cost]). You want this value to change depending on the context of time, region, product, etc., so it's value is not stored but calculated dynamically in the report.
It sounds like for your data, there are multiple rows that tell you the number of cases per order, so this is a measure. Use a measure instead of a calculated column.
I've been Googling around this problem for hours and haven't found a solution that suits my needs.
I have a large data set with agent activities and the total time in seconds each activity lasts. I'm pulling this together in a matrix, to display agent names on the left and the start date of each week across the top like so:
This is working as intended (I've used a measure to convert the seconds into hours) but I need the average of the displayed weeks as another column, or to replace the Total column.
I've tried solutions involving DAX measures but none are applicable, likely because I'm using a custom column (WeekStart) to roll up my numbers into weeks. Adding more complexity is I have 2 filters on the matrix; one to exclude any weeks older that 5 weeks in the past and another to exclude any future weeks.
In Excel I'd just add another column next to the table, averaging the 5 cells to the left of it. I could add it to the data table with a SUMIFS checking the Activity date is within the week range and dividing the result by 5. I can't do either of these in PowerBI and I'm new to the software so I'm at a loss as to how to do this.
I am trying to create a "meetingroom occupancy" matrix in Power BI. The raw data contains bookings per day per Room. The maximum daily available time per room is 12 hours. I have created a Date Dimension Table for the dates.
I have tried to change datatypes, added the available time column in the query editor, added the available time as DAX column and as calculated measure, but all with no success. I have changed the available time for Room B to 1, and the result of the Subtotal was 13, so it looks like subtotals is only summing unique values, but I do not know how to solve this.
Could someone please explain to me what is happening and how I could solve this?
The input data is as follows:
And my Date_Dimension is as follows:
This is the current and desired result:
I'm trying to figure out the difference between the in-application modifier as_rate() and the rollup function per_second().
I want a table with two columns: the left column shows the total number of events submitted to a Distribution (in query-speak: count:METRIC{*} by {tag}), and the right column shows the average rate of events per second. The table visualization applies a sum rollup on left column, and an average rollup on the right column, so that the left column should equal the right column multiplied by the total number of seconds in the selected time period.
From reading the docs I expected either of these queries to work for the right column:
count:DISTRIBUTION_METRIC{*} by {tag}.as_rate()
per_second(count:DISTRIBUTION_METRIC{*} by {tag})
But, it turns out that these two queries are not the same. as_rate() is the only one that finds the expected average rate where left = right * num_seconds. In fact, the per_second() rollup does this extra weird thing where metrics with lower total events have higher average rates.
Is someone able to clarify why these two functions are not synonymous and what per_second() does differently?
I have a pivot table that looks like this:
My hope is to make the columns of the anomalies (A,B,C,D,M) that is the frequency of the anomaly. So that the column is basically
Anomaly/# of Inspections
How can I change the format of these cells to show this frequency so that they can be then plotted over time?
From your question, and a little help from the comments on it, it seems you want to display the volume of anomalies as a percentage of the number of inspections. For example in week 11 you had one Anomaly C, which would be 20% of the 5 inspections.
To display 20% instead of 1, the only way to do this is to change the column formula in the criteria to pretty much what you wrote in your question.
100*(Anomaly/# of Inspections)
You can't do this through formatting – you can't format a number into different number, you have to change the calculation to do that.