I have a table with a column named Brand. This column contains ours as well as our competitors' product name. I am trying to calculate the price difference of our product with each of our competitor's products.
What DAX function should I use? Also, IF() is not allowing to create measures without any aggregation function.
Try and refer below links posted in power bi community.
https://community.powerbi.com/t5/Desktop/IF-Statement-Power-BI-Desktop/td-p/32306
https://community.powerbi.com/t5/Desktop/IF-statement-in-dax/td-p/524310
https://community.powerbi.com/t5/Desktop/Multiple-IF-statement-DAX/td-p/982633
https://learn.microsoft.com/en-us/dax/if-function-dax
And try conditional columns in transform data section
Related
Power BI allows to add calculated field and measure to table. Both create new column and allow me to add DAX formula.
When to use calculated field vs measure in Power BI?
The most important difference is that calculated columns are calculated once when the dataset is loaded. Their value does not change later, i.e. it is not affected by slicers for example. Measures are dynamic. They are calculated whenever necessary, thus they will respond to slicers in the report.
I would recommend to read this article - Measure vs Calculated Column: The Mysterious Question? Not!
Rule of thumb: If you want to use it in a filter or a slicer, put it in a column. Otherwise, you can create it as a measure.
Link: https://learn.microsoft.com/en-gb/learn/modules/dax-power-bi-add-measures/5-compare-calculated-columns-measures
Regarding similarities between calculated columns and measures, both are:
Calculations that you can add to your data model.
Defined by using a DAX formula.
Referenced in DAX formulas by enclosing their names within square brackets.
The areas where calculated columns and measures differ include:
Purpose - Calculated columns extend a table with a new column, while measures define how to summarize model data.
Evaluation - Calculated columns are evaluated by using row context at data refresh time, while measures are evaluated by using filter context at query time.
Storage - Calculated columns (in Import storage mode tables) store a value for each row in the table, but a measure never stores values in the model.
Visual use - Calculated columns (like any column) can be used to filter, group, or summarize (as an implicit measure), whereas measures are designed to summarize.
Is it possible to refer from Power Query (M) to DAX calculated table? I would like to get DAX table as a source to my power query.
The purpose. I have grouping table made in DAX. I would like to make econometric model with R. So I would like to transform the DAX table with R to get the model parameters. I would like to use these parameters further in DAX measures (not just display them).
Currently I dump the DAX grouping table to Excel file and then pull it up with Power Query.
Actually, there is a way.
DISCLAIMER: This is a hack. You should not rely on this way.
1. Create DAX calculated table
Input any DAX formula that evaluates to a table in Modeling > New Table.
2. Check port number using DAX Studio
Connect to your PBI Desktop data model using DAX Studio, and check the port number where the data model is hosted. It should be displayed in the right bottom of the window.
3. Import the table to Power Query
Click Get Data > Analysis Services and input the address (in my example "localhost:50293") to Server. Then navigate to your DAX calculated table.
it's not possible to refer to a DAX calculated table in M as it's loaded into DAX/Power Pivot engine after M has done the transformations. You can't write to a DAX table after loading into R as well. You can do grouping in M, or if needed run R in the Power Query. One approach that I have used has to load the data, duplicate the query, run a group/filter on the new query, then use that data in a later stage in the report.
Hope that helps
Jonee is correct. This is not possible. DAX calculated tables are computed after the M queries have loaded and you cannot feed them back into Power Query without saving them externally like you are currently doing.
The M language is more powerful than you might think and very likely could do the same grouping operations, though depending on what they are, it might be fairly difficult. You can also use R or Python script within an M query if you are more comfortable with those.
I am new to Power BI and with the limited time given, I am stuck at how to come up with:
Below Table B-Row1 ("1/20" and "M"-Monday cell) - how to
specifically place the date measures in their specific cell and put
it in one column?
How can I merge the cells under the Total column?
How to add all the numbers from the Type1 and Type2 columns and place it in the merged cell in #2?
Any clues/direction/links on how to achieve the Target Table B below will be much appreciated.
PS. Below Table A. Current is just using Matrix Visualization in Power BI.
You can't exactly do what you are after. PowerBI allows you to rapidly put amazing visuals together however that comes at the price of lack of (easy) flexibility. You could build your own custom visual or look in App Source for a visual that does this, or build the Visual in some other tool (via custom code).
However, I'd recommend sticking with the PowerBI matrix, which will give you a cascading drill down and work out how best to align your data to it and other out of the box visuals. Once you start to delve in to convoluted work-arounds to give users data in exactly the format they request you start to burn a lot of time. Look for alternatives to tell the data's story and work with your end-user to buy in to it.
Just wanna share that I have resolved my problem not using one type of visualization, but through using 3 different visualizations in Power BI. I used:
1 Table visual for Date column
1 Table visual for Total column
1 Matrix visual for the Code+Type mapping and counts
I also used DAX function to get the Date format and another DAX function used for both Total and Code+Type counts(to filter data according to the specified date).
Thanks for the response, #Murray and #RADO.
I’m new to Power BI. Currently facing similar issue explained below in my product development.
I have created power bi modle with below dimensions and facts from adventureworksDW.
Then I created a calculated table, which gives result as sum of sales group by ProductSubCategory and ProductCategory. Below is the DAX for the calculated table.
Now I want to create a new calculated table, which gives me TOPn ProductSubCategory based on the Total sales amount.
Below is the DAX to do this.
and model relationships looks like below.
I want this TOPn rows to be displayed based on filter condition on product category. Something like below.
This works fine when I hardcode the product category value in the DAX itself. But if I want to change this product category values from the slicer selection, then I didn’t get any results.
What you are asking for is not possible as Power BI is currently designed. Slicers cannot affect calculated tables. Calculated columns and calculated tables are evaluated once when the data is first loaded and are static until the data is refreshed.
However, you can get the table visual you want in a much simpler manner by writing the appropriate measure and putting that in the table instead of defining an entirely separate table.
TotalSales = SUM(FactInternetSales[SalesAmount])
The Top N filtering is available in the visual level filters settings.
You can simply use the SELECTEDVALUE function as shown below.
var __SelectedValue = SELECTEDVALUE('ProductSales'[EnglishProductCatogaryName])
return
Filter(
'ProductSales',
'ProductSales'[EnglishProductCatogaryName] = __SelectedValue
)
)
I have been working on Power BI for a while now and I often get confused when I browse through help topics of it. They often refer to the functions and formulas being used as DAX functions or Power Query, but I am unable to tell the difference between these two. Please guide me.
M and DAX are two completely different languages.
M is used in Power Query (a.k.a. Get & Transform in Excel 2016) and the query tool for Power BI Desktop. Its functions and syntax are very different from Excel worksheet functions. M is a mashup query language used to query a multitude of data sources. It contains commands to transform data and can return the results of the query and transformations to either an Excel table or the Excel or Power BI data model.
More information about M can be found here and using your favourite search engine.
DAX stands for Data Analysis eXpressions. DAX is the formula language used in Power Pivot and Power BI Desktop. DAX uses functions to work on data that is stored in tables. Some DAX functions are identical to Excel worksheet functions, but DAX has many more functions to summarize, slice and dice complex data scenarios.
There are many tutorials and learning resources for DAX if you know how to use a search engine. Or start here.
In essence: First you use Power Query (M) to query data sources, clean and load data. Then you use DAX to analyze the data in Power Pivot. Finally, you build pivot tables (Excel) or data visualisations with Power BI.
M is the first step of the process, getting data into the model.
(In PowerBI,) when you right-click on a dataset and select Edit Query, you're working in M (also called Power Query). There's a tip about this in the title bar of the edit window that says Power Query Editor. (but you have to know that M and PowerQuery are essentially the same thing). Also (obviously?) when you click the get data button, this generates M code for you.
DAX is used in the report pane of PowerBI desktop, and predominantly used to aggregate (slice and dice) the data, add measures etc.
There is a lot of cross over between the two languages (eg you can add columns and merge tables in both) - Some discussion on when to choose which is here and here
Think of Power Query / M as the ETL language that will be used to format and store your physical tables in Power BI and/or Excel. Then think of DAX as the language you will use after data is queried from the source, which you will then use to calculate totals, perform analysis, and do other functions.
M (Power Query): Query-Time Transformations to shape the data while you are extracting it
DAX: In-Memory Transformations to analyze data after you've extracted it
One other thing worth mentioning re performance optimisation is that you should "prune" your datatset (remove rows / remove columns) as far "upstream" - of the data processing sequence - as possible; this means such operations are better done in Power Query than DAX; some further advice from MS here: https://learn.microsoft.com/en-us/power-bi/power-bi-reports-performance