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!
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'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 need a calculated column based on conditions in two columns (Business Unit Number in both tables and L1/Account Categories in 1st table and the second table) which sum and then repeat for several rows before the conditions change and a new sum is repeated for several rows and so on. The L1/Account Categories columns have different names because it's the raw data.
For example, any time ASSETS and 111 appear in the same row, I would want to use those as conditions and with the sum of all of the other matching rows in a new column and the sum would repeat each time both conditions appeared in the same row. Any time P/L and 111 appear in the same row, that would be a sum of all other P/L and 111 appearances in the dataset (about 1000 rows overall)... and so on.
I've tried formulas with DAX using FILTER, SUMX, nested IF statements and also tried the Power Query language among other attempts. Maybe I have to create one or more than one new table? If you need to take a look at a few of my attempts, just let me know.
The top image is how I imagine the output will look in the power query editor and the bottom image is a sample of the source data.
This last pic is from Tableau - I need to make a table in Power BI which essentially a duplicate of this image. The last 2 columns are pulling from different tables.
This should be very simple to achieve with relationships and measures - no need for calculated columns or power query merges. You need to build a relationship between these two tables. In fact, I would introduce a third table in your model for Business unit.
The limitation of Power BI model relationships is that they can only be based on a single column. So to build a relationship between these two tables you would have to add a calculated column in both of them that would contain both a BU and the financial statement line, for instance: JoinCol = CONCATENATE([Business_Unit_Number], [L1]). Then you could create a relationship and do what you want.
The better (one that I would recommend) approach would be to separate Business Unit into a separate table and have relationships built like this:
Then all you have to do in your visual is drag Business unit name from the BU table, L1 from the FS Lines table and a measure to sum the amounts Amount = SUM('Financial Data'[Rolled Up Detail]).
Here is a working sample: https://1drv.ms/u/s!AmqvMyRqhrBpgtUT5HKnZP1U3Gzc9w?e=en91dV
I have the following tables
I have several tables on coal consumption, natural gas, .....
I want to have a power BI model that can allow viewing these data with years (x-axis) while filtering on countries.
I have transposed the data to have a column with years and countries on the columns. But I cannot filter on countries.
Another thing I have done is to unpivot all the data and have four columns (country, year, value, fuel type) but the problem is that I could not manage to create suitable relationships between the tables as there is no primary key.
I have thought on putting all the data fro the different energy sources in one table. But how can I manage to link it to more data per country at year as well.
Another thing I have done is to unpivot all the data and have four columns (country, year, value, fuel type)
This is totally the right approach.
The next step required in minimum is to combine all unpivotted tables vertically into one EnergyConsumption table. You can utilize Append Queries command in Power Query Editor, or Table.Combine function in M language.
Additionally, you should consider to create three tables: Years, Countries, and FuelTypes, which have unique values of the respective dimensions, and establish one to many relationship with EnergyConsumption table.
I have a FactLosses Table, and a DimAccumulation table. I have brought them into PowerBi and I have placed a slicer to choose which accumulation zones i am interested in.
Once the user has selected the zones, i want to perform a group by year on the losses and sum the losses into year buckets. But only on the data that applies to the zones the user picked.
I am using the following DAX code to do the group by like so...
Table = SUMMARIZECOLUMNS(FactForwardLookingAccumulation[Year], "Losses By Year", SUM(FactForwardLookingAccumulation[Net Loss Our Share Usd]))
The problem is the new table always produces the same result. i.e When i make changes to which accumulation perils should be included it makes no difference to the summation. (it is summing the entire table)
I'd like to use the slicer to filter the fact table and then have the DAX query run on the filtered list. Is this possible?
If you want these tables to be responsive to filters or slicers on your report, then you can't write these as calculated tables that show up under the Data tab since those are computed before any filtering happens.
To get what you want, you have to do everything inside of a measure, since those are what respond to slicers. If you're looking for the max loss year once the grouping and summing are completed, you can write a measure along these lines:
Year Max =
VAR CalculatedTable = SUMMARIZECOLUMNS(FactForwardLookingAccumulation[Year], "Losses By Year", SUM(FactForwardLookingAccumulation[Net Loss Our Share Usd]))
RETURN MAXX(CalculatedTable, [Losses By Year])
Writing it this way will allow the calculated table to respond to your slicers and filters.