I have 2 columns one containing order place time and another containing order delivered time and I have created a custom column where we calculate the delivery time in (dd:hh:mm:ss) format now I wanted to calculate average delivery time (i.e average of delivery time in (dd:hh:mm:ss) format).
can any one please suggest me any dax code for the same.
Related
Looking for some help to calculate the cost increase/decrease between two date points per product. I would like to display any increases/decreases per item. Each item in the list are part of a BOM for an overall product. An example scenaro is needing to show any cost differences between 2022-03-01 & 2022-04-01 which would show changes in A ($0.50) and C ($0.20).
I'm guessing I would need to utilize the MAX/MIN functions in a measure, but having trouble creating a measure that compares each item. There are too many date points to due the calculation per date via a new column and all the data is unpivoted.
2 date values are selected via the standard PBI slicer visual
The underlying data format in PBI is displayed below:
Power BI Data Format
I am trying to create a forecast but this is the error that I get:
I am working with about 300,000 rows of data. Most of the report has already been built. My data just doesn't cotain certain dates. How can I solve this issue?
So the issue boils down to the problem of "How to create an evenly spaced timeline". You can easily achieve this in PowerQuery
Create a separate daily date table.
Outer join your observations onto the dates, which will give you "null" for the unobserved days
Apply the "fill down" operation on your values column, which basically means that the last value will be repeated until a new observation appears.
These evenly distributed time series is suitable for ML forecasting, at least when it comes to predicting trends. But the real power of this feature in Power BI is in predicting seasonality, and you most likely won't get that right with the above interpolation.
We have a table in Power BI where we are trying to display the location, the current average of the location and then finally the average of the location based on the time period. Thus the location and the average stays constant, but the average across the location over the selected period changes with the period that one selects.
Do you have any advice on how we can implement this? Since the first two columns is linked to only the last ID/row (relationship) but the last column must go back to get the average across the selected period.
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/
I am working on a report that has data by month. I have created a measure that will calculate a cost per unit which divides the sum of dollars by the sum of production volume for the selected month(s):
Wtd Avg = SUM('GLData - Excel'[Amount])/SUM('GLData - Excel'[Production])
This works well and gives me the weighted average that I need per report category regardless of if I have one or multiple months selected. This actual and budget data is displayed below:
If you take time to total the actual costs you get $3.180. Where I am running into trouble is a measure to sum up to that total for a visual (This visual does not total sadly). Basically I need to sum the aggregated values that we see above. If I use the Wtd Avg measure I get the average for the total data set, or .53. I have attempted another measure, but am not coming up with the correct answer:
Total Per Unit Cost = sumX('GLData - Excel','GLData - Excel'[Wtd Avg])/DISTINCTCOUNT('GLData - Excel'[Date])
We see here I return $3.186. It is close, but it is not aggregating the right way to get exactly the $3.180:
My Total Per Unit Cost formula is off. Really I am simply interested in a measure to sum the post aggregated Wtd Avg measure we see in the first graph and total to $3.180 in this example.
Here is my data table:
As you probably know already, this is happening because measures are dynamic - if you are not grouping by a dimension, they will compute based on the overall table. What you want to do is to force a grouping on your categories, and then compute the sum of the measure for each category.
There are 2 ways to do this. One way is to create a new table in Power BI (Modeling tab -> New Table), and then use a SUMMARIZE() calculation similar to this one to define that table:
SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg])
Unfortunately I do not know your exact column names, so you will need to adjust this calculation to your context. Once your new table is created, you can use the values from that table to create your aggregate visual - in order to get the slicers to work, you may need to join this new table to your original table through the "Manage Relationships" option.
The second way to do this is via the same calculation, but without having to create a new table. This may be less of a hassle. Create a measure like this:
SUMX(SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg]),[Wtd Avg])
If this does not solve your issue, go ahead and show me a screenshot of your table and I may be able to help further.