Category: Power BI DAX

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Introduction to DAX

I have gone through a fair few training courses on DAX now because its an important part of Microsoft Analytics. I thought It would be nice to include all my notes from one of the first DAX courses I attempted.

You don’t have to be an expert on DAX to get started with Power BI. You can start with a few of the basics and get lots of help along the way in the forums.

DAX (Data Analysis Expressions)  is an expression language for slicing and dicing analytical data

Where is DAX Used?

  • Power BI
  • Power Pivot (Excel)           
  • SSAS Tabular Mode (You can’t use it in Multidimensional mode)
  • Azure Analysis Services (This is only available in Tabular Mode)

DAX IS USED HEAVILY IN THE MICROSOFT BI STACK

Where Does DAX Shine?

  • Aggregations and filtering. Its optimized for both

What you need

  • An easy way of defining key metrics.
  • You need to be able to slice and dice
  • You need to be able to do historical analysis
  • DAX is an easy way of defining key metrics.

What is DAX not good at?

  • Operational Reporting – Detail heavy used for day to day operating. Line by Line Reports
  • Wide tables – Tables with a lot of columns
  • Many to Many relationships. There are ways around this, but it can be difficult to resolve

Its worth noting that this visual was provided over a year ago when Azure Analysis Services was the only way of creating the back end centralised model. we are now at December 2020 and Power BI Has moved on to become not only Self Service, but a really great way to implement a Standard centralised model for Enterprise reporting. You do this by creating data flows in Service, which establishes your transformations in one specific area for reuse. Then over this, data sets can be created that can be reused by other developers once you have promoted them.

Thinking in columns not rows

  • OLTP – Online Transactional Processing. (Normalised schemas for frequent updates)
  • OLAP – Online analytical Processing (Fact and Dimensions. Star Schemas etc.)
  • Single column aggregations – Sales by area etc. (OLAP)
  • Large number of rows – Sales by year, we have 10 years of data. Lots of rows (OLAP)
  • Repeated values – Data is flattened unlike in an OLTP
  • Need to quickly apply filters By Area, Postcode, Year etc.

We may only want 3 columns in a table that has 50 columns. We just want the slice of those 3 columns and we don’t want to read the other columns

For this we store data as columns Not Rows

Vertipaq

Also known as xVelocity. It is the engine used to store data as columns. The data is imported into Power BI data center as Columns not rows

Direct Query

If you set up your connection as a direct Query, Power BI has to translates DAX formulas into relational SQL queries so you lose a lot of the functionality like Time DAX because its too complex for SQL.

Compression and Encoding

This is how Power BI Compresses your data. Each column is compressed separately

  • Value Encoding
  • Dictionary Encoding
  • Run-Length Encoding

Value Encoding

Let’s take the lowest value in the range

and then store everything else as the delta (Difference) of that value C. 

This makes the Code shorter in length, compressing the data

Dictionary Encoding

Actual Column

The value gets assigned a number which is then used in place of the actual data item

This takes up far less space.

Run-length encoding

The data is sorted

This just keeps the colour and the number of repeats. Repeating values creates excellent compression. Unique values, not so much.

Adding Business Logic with Calculated Columns and Measures

The following examples have been set up using the old faithful Adventureworks data base.

Im initially bringing through two tables

  • Sales.SalesOrderDetail
  • Production.Product

and renaming them to Products and SalesOrderDetail

We are now ready to try some examples

Calculated Columns

Where should you be creating calculated columns? In Power Query using M Language or in DAX.

the speedy answer is, if you can, always create them in Power Query. However, you do need to understand how the calculated column works in DAX in order to understand measures

  • Expands a table by adding a new column.
  • Operational, Detailed information. Can only look at the specific row
  • It’s an expression that produces a column. Computes at the time of data refresh and is stored within the table
  • Limited by a row context. Price-Discount. They don’t take advantage of the columnar database

In Power BI we are going to set up a Line total in SalesOrderDetail because you need where possible to put the new column in the right table with the data that is being used to create it

Click on … Against the table under fields in power BI and New Column

LineTotal = SalesOrderDetail[UnitPrice] * SalesOrderDetail[OrderQty] * (1- SalesOrderDetail[UnitPriceDiscount])

It calculates the values for each row

The new column now sits in the model with the other fields (Go to Data Tab)

And you can use this like any other metric. In this table we are filtering by product Colour. Line Total has already been calculated within the row context.

You can now use Line Total in a table visualization because we can treat this column like any other column and its now being set in the implicit filter context which in this case is Color.

Implicit context filters are when you add a description column which aggregates your metric within the visual. Or if you add a filter to the visual. We will look at Explicit filters later.

Related

When you create a column you can only create it from data in the same table unless you add RELATED or RELATEDTABLE into your DAX

Adding Color into the SalesOrderDetailTable from Products

1 Product can be sold Many times.

Colour = RELATED(Products[Color])

I have added data from another table into a table. RELATED allows you to pull data across from another related table. Note that in the previous DAX I only used data from one table, so I didn’t need to use RELATED.

RELATED is for the One to Many Relationship. Using data from the one side in the many side. Colour (from 1) now sits in salesOrderDetail (M)

RELATEDTABLE

RELATEDTABLE uses the data from the many in the one side

We are in Product

TotalSales = SUMX(RELATEDTABLE(SalesOrderDetail),SalesOrderDetail[LineTotal])

The SUMX is an Iterator. Takes a table and an expression to evaluate. This means We are going through each row in the Products table and its running the evaluation. It’s using the related table to go through all of the LineTotal’s that we created in SalesOrderDetail

So for example

Row for Product ID 707. Sum up the Line total in salesOrder Detail where ProductID 707 and add the line total into Product

Next iteration. Row for ProductID 708. Sum up the Line total in salesOrder Detail where ProductID 708 and add the line total into Product

And repeat until you have iterated through the entire table for each row

Again, a quick rule of thumb is, if you are creating a calculated column using data from the same table, do it in Power Query Editor. If you have to use data from other tables, use DAX.

Your data will be compressed like any other field if its been created with Power Query Editor but it wont be if its a calculated column because it happens after compression.

Measures

  • Summarises all the data into a single value. Not stored on the table.
  • Analytical. Takes a column and brings back a summary
  • They are computed at runtime so stored temporarily
  • Every time you open a report, your Measures are computed
  • Limited by a filter context (Rather than a row context)
  • A measure looks at the data minus what has been filtered out by the user at that time.
  • They are more loosely associated with a table, so you don’t need to RELATE tables

Again, In Power BI on the SalesOrderDetail Table:  Create Measure

Minimum Price = Min(SalesOrderDetail[UnitPrice])

You can’t add your measure to a slicer or use as a filter because its created after the filters have been set

Implicit Measure – Underneath the hood is a measure for anything that you aggregate that is created by the DAX engine. For example your Total column. Here we are implicitly filtering UnitPrice by Colour to get the minimum value.

If you go into the Data tab, you wont see this measure because it only exists after you create your visual.

Filter data Using Calculate

Implicit

Applied by the user or the layout of the report. The visuals implicitly filter the data. When you drill up and down on a visual for example.

Explicit

Coded into the DAX Expression. Explicit filtering overrides Implicit filtering

Calculate

Calculate changes the current evaluation context

In Power BI set up a Table containing Products Colour and Unit Price (Set to Average rather than Sum)

BlackAveragePrice = CALCULATE(AVERAGE(SalesOrderDetail[UnitPrice]),Products[Color]=”Black”)

This Calculates the Sales Total Only on the colour Black. This overrides the implicit filters of colour. It changes the Current Evaluation context, e.g. White or yellow

Add a filter and notice that this is also ignored because the Explicit context filter ignores the Implicit Context Filter of red

Next create a measure

IsColorFiltered = ISFILTERED(Products[Color])

ISFILTERED Return value. TRUE when columnName is being filtered directly. 

Colour is filtering each of the line items but is not filtered for the total because it’s the average of everything

Now the Grand total is also filtered by Grey once we have filtered on grey using the slicer so both are now true

Calculate is designed to be fast. For more complicated examples we need to use Filter.

Calculate only lets you compare a single column to a fixed value

Filter

CALCULATE( SUM ( [Total] ), FILTER (Sales, QTY * Price < 100 ))

Filter takes in two values. The Quantity and Price and then applies the filter of less than 100

Now, We want to see The Average Unit Price Discount Where Order Quantity * Unit Price * Unit Price Discount Any time the total discount is greater than 10 dollars, we want that to apply to the average

As an example, In Power BI we need another table of Color and UnitPriceDiscount (Set as Average)

Create a measure in SalesOrderDetail

DAX is a functional language, its created from functions like SUMX etc.

DAX is executed from the innermost parameter so don’t create all your DAX on one line.

AverageDiscountGreaterthanTen =
CALCULATE(AVERAGE
(SalesOrderDetail[UnitPriceDiscount]),
FILTER(SalesOrderDetail,
SalesOrderDetail[OrderQty] *
SalesOrderDetail[UnitPrice] *
SalesOrderDetail[UnitPriceDiscount]>10
)
)

Filter takes in two parameters. We need to tell it the table and then the expression we want.

Filter is creating a table of values to work with because it is a table function. Its always embedded in other functions, in this case CALCULATE,

You cant have measures inside a filter.

to start to understand this I’m going to add details for a specific order and product. Im going in at the lowest level of granularity

I wanted to see exactly what was going on in the filter so I created a calculated column working on the row context

Qty*Price*Discount = SalesOrderDetail[OrderQty]*SalesOrderDetail[UnitPrice]*SalesOrderDetail[UnitPriceDiscount]

So for Full Finger Gloves L.44 were bought, Around 17 dollars each with a discount of 15%

the Average of UnitPriceDiscount is the same as the Summed UnitPriceDiscount because the products are all identical with the same discount applied

44 $17 gloves would bring in $752. Applying the discount of 15% (0.15) shows is that £112.83 has been discounted from the total price.

This is above 10 so we can simply display the average Unit Price

ALL

Historical Sales = CALCULATE (SUM ([Total]), ALL (‘Date’) )

ALL clears any filters in the entire date table

For this example, we want the Sum of Order Quantity for the specific context filter divided by (We now need calculate because we are manipulating filters) Sum of all Order Quantities.

ALL takes in just the product table because we want to undo any filters from the Product table (New Measure in the Products Table)

% of total sales = SUM(SalesOrderDetail[OrderQty]) / CALCULATE(SUM(SalesOrderDetail[OrderQty]), ALL(Products))

So for Black context filter the Order Quantity is 81937 / the total (We have removed the expression filter for the entire product table) of 274914 so we can see that Black is 29.80% or the order total.

With a slicer on the report. We choose One Product and even though its the only row in the visual Total Sales shows -.13% instead of 100% which is just what we want.

We still have 18% of Total Sales instead of it showing 100% because that’s the only row left in the visual. Perfect. This is what would have happened without ALL

% of total sales = SUM(SalesOrderDetail[OrderQty]) / CALCULATE(SUM(SalesOrderDetail[OrderQty]))

Looping over the Data with Iterators

Allows you to use multiple columns in an aggregation

What is an iterator?

Power BI’s DAX engine is optimized for working with columns not rows.

Iterators can process on a row by row manner. Its less performant because its working with rows. 

Takes in a table parameter (What is it looping through)

Then takes in an expression (For each row, evaluate this expression)

We have used something before that takes in the table and then an expression. The Filter function. The Filter function is an iterator.

Many of them end in X. SUMX MAXX CONCTENATEX etc

Average Gross Sales =

AVERAGEX(Category, [Quantity] * [Price] )

You could create Quantity* Price as a calculated column so each row gets this valuated first, and then average this column separately. The iterator does both in one formula

Add a new Measure into SalesOrderDetail and start typing in the following (Type it in so you bring up the intellisense)

AverageGrossSales = AVERAGE(SalesOrderDetail[OrderQty] * Uni

If you type in the above you notice intellisense stops working when you get to Unit price. This doesn’t work. The average function only accepts a single column.

This is where you could create this part as a calculated column but we want everything in one measure

AverageGrossSales with Discount =

AVERAGEX(SalesOrderDetail,

SalesOrderDetail[OrderQty] *SalesOrderDetail[UnitPrice]*

(1 – SalesOrderDetail[UnitPriceDiscount])

)

Also, you cant use AVERAGE because Average only accepts one value. AVERAGEX can be used in a measure because it iterates through each row and then aggregates

CONCATENATEX example

Add the following measure to Colours

Colours Incorrect = CONCATENATEX(Products, Products[Color],” “)

Its only called Incorrect because this is the wrong way to do it

This measure should show us what colours we have filtered on but as you can see below, now I have added a Card. We get the filtered colours for every row and we don’t want that. The Concatenate is iterating through every row and adding every colour together

Colours = CONCATENATEX(DISTINCT(Products[Color]),Products[Color],” “)

If you change the Table part of the expression to Distinct Colour, this gives you the distinct colour filtered on

This time we added a Distinct and if we choose more than one colour a space will be between each colour.

Evaluation Context

Defines what values an expression can see

Nesting Evaluations

If the above is a little confusing (and it will be) here it is again

RANKX is an iterator

Its iterating through products so we are going to start at the first product. Tulip

Its passing everything from Internet Sales related to Tulips into basically a temporary table

We are passing this information into the SUM of X and we repeat for every Product

And now the RANKX has information to work with

Analysing History with Time intelligence

We need to be able to compare periods. We can make use of DAX time intelligence

  • Single date functions – Return a single date First date
  • Date table functions – Returns a table as the output. Year to date. Takes in a date and takes in a column filtered year to date
  • Date aggregation functions Total Year to Date

Create a Date Table

Why can’t I use the date table in sales?  Dates need to be contiguous

How to 1:

  • Get Data > Blank Query
  • Next click on Advanced Editor

let

    Source = #date(2011,1,1),

    Dates = List.Dates(Source,3650,#duration(1,0,0,0))

in

    Dates

Create the source date which is the 1st Jan 2011. Then take the source and create Dates. I’m creating 3650 rows with a duration of 1 day between each

Then click on ‘To Table’ to convert to table. Then I renamed the column to Date,

I can then add more dates using M query by duplicating the date and changing to Year, Month and Day. Make sure Date is a Date

How to 2: You can also make this table using DAX

  • Modelling tab > New Table

DatesDaxCALENDAR = CALENDAR(“01/01/2011″,”01/01/2021”)

This is your starting point, but we can amend this to include other date columns

DatesDaxCALENDAR = ADDCOLUMNS(CALENDAR(“01/01/2011″,”01/01/2021″),”Year”,YEAR([Date]),”Month”,MONTH([Date]),”Day”,DAY([Date]))

If you used CALENDARAUTO it looks at the date that you used and it automatically calculates the start and end based on your data set

Connect your new date table up to the Sales table

For the Power BI exercise, I am going to use the Date table created with M.

Dates as Surrogate Keys

Sometimes when you work with data warehouses you have an integer as a date for the surrogate key. 20190206 for example

If you have to join to a surrogate key.

  • Modelling > Mark as Date table. Say which is the date and now this will work. The only reason to mark as date table is if you are joining to a surrogate key.

If you move everything to an Analysis Service, you MUST have a date table marked as date

Year over Year and Year to Date Analysis

First, Add Sales Header to your Power BI Report from the Database

  • Edit Queries > Recent Source > Choose the Adventureworks Source > Sales.SalesOrderHeader > Import
  • Rename to Sales Header and close and apply
  • Go into relationships and set as follows

Sales Header OrderDate to Date in DateM (Ensuring they are both dates)

Disconnect the other Date table. This is for reference only

Now create this year to date measure in the DateM table which uses date from the date table

YTD DatesM = TOTALYTD(SUM(‘Sales Header’[TotalDue]),DatesM[Date].Date)

As you can see this works. Year to date resets when we get to the next year.

Get the Sales from the Previous Year (Added measure to the Sales Header Table)

Prev Year = CALCULATE(SUM(‘Sales Header'[TotalDue]),PREVIOUSYEAR(DatesM[Date]))

YoY Growth = ([Prev Year] – Sum(‘Sales Header'[TotalDue])) / Sum(‘Sales Header'[TotalDue])

So take the current total Due from the Previous years Total Due. Then divide by the total Due

This is only a starting point really but gives you a good idea of some basic DAX queries, How to write it and why.

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DAX – ISLOGICAL

This function checks whether a value is a logical value (TRUE or FALSE), and returns TRUE or FALSE.

I just looked a question that asks, what does this DAX expression do?

is logical measure = IF(ISLOGICAL("true"),"Is boolean or Logical","Is different type")

I could just have a guess based on If “true” is logical then “Is Boolean or Logical”. Else “Is different type”.

Well, We have added “True” true within quotation marks which makes it text, not Boolean or logical.

As expected. Lets change the DAX

is logical measure = IF(ISLOGICAL(true),"Is boolean or Logical","Is different type")

True is Boolean because you can only have True or false

is logical measure = IF(ISLOGICAL(false),"Is boolean or Logical","Is different type")

Still Logical because its Boolean so the card stays the same

So why would you use ISLOGICAL?

You would mostly use this to test data so its great for making sure columns of true and false only contain boolean true and false information.

So, you always need to test how good your data is. Add an ISLOGICAL for true /false columns and you can report back on the validity of the data.

Salaried Flag in Adventureworks is a boolean column

Here is where the confusion is. You cant feed Salaried flag into this because we have created a measure and salaried flag is not an aggregation. You cant aggregate true or false

So instead I create a calculated column on the Employee table

Is Salary flag true or false? = IF(ISLOGICAL(DimEmployee[SalariedFlag]),"Is boolean or Logical","Is different type")

Great, Salaried flag has passed the test.

So ISLOGICAL, great for testing within a calculated column

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Creating an open Account Measure using DAX

Thank goodness for the amazing people on the Power BI Forums helping with this. I have the solution but what I wanted to do was to create a post to explain the why

The Problem

I have accounts and events occurring on each account. What I want to know is the time period they are open

Just for this example I have taken One account with 11 activities

If you run the visual against start date all you get is a measure againt the start time period.

We are more interested in creating a measure to show how long the activity went on for.

For this example I am going to choose Jan 28 2017 to work with to continue because the end date for this is March 21st 2017 so this activity should span 3 months

The Date Dimension

To do this you must have a date dimension. For the purposes of this example I’m going to create one in DAX (Create table) Using the following:

Notice that I have used Start and End Dates to set the min and Max years. You should already have a date dimension to work with though.

Set up the relationship between the Fact table and Date dim

Start date is the Active join and End Date is the Inactive Join

You could create a role playing dimension for the End date and have that as an active join but in this case I’m going to go for the above design.

Account Start flag (New Measure)

  • CALCULATE – You can modify your expression using a filter
  • ALLSELECTED – If you have a slicer set to Year (Date) , ALLSELECTED takes the ‘Context’ from the slicer Like 2018.
  • Date is less than or equal to the Max Date in the Filter Context

Account End Flag (New Measure)

  • ENDOFMONTH – Returns the last date of the month in the current context for the specified column of dates.
  • The date at the end of the month is less than the Max date in the Filter Context
  • USERELATIONSHIP Because End Date is the inactive relationship we need to specify that this relationship is used, Not Start Date

Add an Open Measure

To understand the results we need to try and understand how the new measures work

Jan

  • Start Flag =        1 because 18th Jan is less than Jan 31st
  • End Flag  =        NULL because March 21st (Reset to March 31st) is NOT Less than Jan 31st
  • Open =  1- null = 1

Feb

  • Start Flag =        1 because 18th Jan is less than Feb 28th
  • End Flag  =        NULL because March 21st (Reset to March 31st) is NOT Less than Feb 28th
  • Open =  1- null = 1

Mar

  • Start Flag =        1 because 18th Jan is less than Mar 31st
  • End Flag  =        NULL because March 21st (Reset to March 31st) is NOT Less than March 31st
  • Open =  1- null = 1

Apr

  • Start Flag =        1 because 18th Jan is less than April 30th
  • End Flag  =        1 because March 21st (Reset to March 31st) is Less than April 31st
  • Open =  1- 1 = 0

There are usually lots of ways to answer one question so this is one of many but the Open Flag now allows me to pull up all my activities and show in a Matrix where the are open.

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Create a visual Axis Category from a measure (Create Table with DAX)

Take this data as an example

  • ID      Total
  • 99                   1
  • 99                   1
  • 101                  1
  • 101                  1
  • 101                  1
  • 333                  1
  • 333                  1
  • 333                  1
  • 333                  1
  • 635                  1
  • 635                  1
  • 635                  1
  • 635                  1

If you do the usual measure SUM(Total) you would get 

  • ID       Total
  • 99                   2
  • 101                  3
  • 333                  4
  • 635                  4

But I don’t want the visual to show the above. I want the number of occurences on the axis and then the number of IDs within that occurence as the value

You cant use a measure on the axis, they can only be used as values. How you you solve this problem?

Go to Modelling and New Table

SummaryTable = 
SUMMARIZE (
    'fact Fact',
    'fact Fact'[ID],
    "Frequency", COUNT ( 'fact Fact'[ID]))

SUMMARIZE Returns a summary table for the requested totals over a set of groups. In this case, Our group is the IDs.

For the Summary we are simply counting our IDs as the Frequency

Next, Add a new Table

PotentialFreqs = GENERATESERIES ( MIN (SummaryTable[Frequency] ),max('SummaryTable'[Frequency]), 1 )

This creates a list of Frequencies starting at 1 and Ending at our Max Frequency. there is is issue here. we have a lot of NULL Values that creates a large Frequency number that we don’t want in our series of frequencies. this means we have values from 1 to 98 and there should only be values 1 to 4. How do we remove these values?

SummaryTable = 
SUMMARIZE (    
        'fact Fact',    
        'fact Fact'[ID],    
        "Frequency", IF(
                       'fact Fact'[ID] 
                       <>"0",COUNT 
                       ('fact Fact'[ID]))  
          )

Note the addition of the IF statement. If the ID is not zero then count the IDs. Else do nothing

Create relationships

In order to use the new tables we need to create relationships between them

the Summary table is joined to the fact table by the ID (Business key) within the fact table

PotentialFreqs is joined to the SummaryTable via Value to Frequency

the first visual to try this out is a stacked bar chart. Value from PotentialFreqs table is in the Axis and Frequency from the Summary Table is used as the Value.

So we can see that 33 of our IDs have 3 records in the table.

the PotentialFreqs table is really useful if you want to plot your data in a histogram as it gives you a continuous type to plot against rather than categorical

In conclusion, if you need to use a measure on an Axis rather than a value, Create a summary table and then join to the main tables in your model

Update

Unfortunatly the above solution doesnt quite work because I want to base my metrics against each year. sometimes the IDs are split into Years. I will be looking at this later.

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Power BI – Dax – RANKX how to use Rank X with 2 values, Filters and Slicers in a Matrix

I have been struggling to implement RANKX into my Reports. I have 3 tables

The date table is used for lots of other reports and goes up to the end of 2019

Our facts for this particular report only go up to March

We want to rank the Average Fact against the Group by Month.  The report will have a slicer on Year

We also don’t want to rank against the full set of groups. Only a subset of them so I am applying a filter against group for three of the groups

The original Ranking DAX used

Rank = RANKX(CROSSJOIN(ALL(‘dim Date'[date].[Month]),ALL(‘dim'[Group])), [Avg Fact],,DESC)

RANKX – RANKX is an Iterator. It takes a table and an expression to evaluate. RANKX looks at each row in the table and running its evaluation which is to return the ranking for each row in the table argument. RANKX creates a row context because it’s an iterator

Row Context –  Calculated at processing time instead of at run time. This is a calculated column rather than a measure because the data is set on each row. A good example of a Row context calculation is, Is this value greater than 100.If yes Set to True. If no set to False. This is applied on each row

CROSSJOIN – Cross join allows you to recreate a table from all Tables and Columns in the cross join

ALL – Returns all the rows in the table, ignoring any applied filters

So essentially You are ranking against ALL months in the date and All groups within the other dimension table. We are ranking the Avg Fact measure

Now this works to some extent if you add in the table with the date filter set but no filter on the groups you want. Looking at January for example

However Later on in the data set the null values are set as

AND when I apply the filter of only having certain groupings in the table the RANKX fails even further

Note that the RANKX is still ranking every single value, even though we have applied a filter for the Group

This is because of the ALL. The Cross Join takes ALL month Values and ALL Group Values, and dismisses the fact that we have applied a filter

You need ALL because if you just ranked against the 1 value that the row context is on, all ranking would be 1.

Rank = RANKX(CROSSJOIN(ALLSELECTED(‘dim Date'[date].[Month]),ALLSELECTED(‘dim'[Group])),  [Avg Fact],,DESC)

ALLSELECTED is different to ALL because ALL calculates everything ignoring filters. ALLSelected Takes into account the filter on the visual. An important part of the solution is that we are slicing on Year and we are filtering for specific groups. As a consequence we need to use ALLSELECTED

Remember we are ranking the measure against each Group. What is the best Group this Month?

Next stop, ensuring we don’t rank against null values

Rank =

IF(

    NOT ISBLANK( ‘fact'[Avg Fact]),

    RANKX(CROSSJOIN(ALLSELECTED(‘dim Date'[date].[Month]),ALLSELECTED(‘dim'[Group])),

    [Avg Fact],,DESC)

)

We have wrapped up the original RANKX into An IF block

NOT ISBLANK (If our measure its not blank then assign the RANKX function

This is great, Its working in the table because we can see in February ‘Age’ is  top ranked and ‘Digi’ is bottom ranked.

Changing the DAX Query issues from table to matrix

However , if the data is to be displayed in a matrix we may not want to rank in this way at all

As you can see, in January we rank against each group. But what we could require is to Rank the best month for a group.

So we have created a rank to rank each group in a Month. NOT to rank the best month for a group.

As yet I haven’t come up with a solution to this one so the mystery continues but once I have a solution I will certainly create a post about it.

If you have a solution please let me know

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Power BI Time based Measure not working when slicer (Year) applied

SAMEPERIODLASTYEAR DAX not working with a year filter

I wanted to create a KPI showing year to Date and Last year to date.

So I have Measures:

YTD Complaints = TOTALYTD(‘fact Fact'[Complaints],’dim Date'[date].[Date])

LY YTD Complaints = CALCULATE([YTD Complaints],SAMEPERIODLASTYEAR(‘dim Date'[date].[Date])) 

I have a slicer on year so i can select the year to look at. Without the year selected its fine (Ive put the data into a table simply to have a look at)

As you can see the last year information is shown in the column 

if I choose 2019 to look at i want to see 2019 with Last year against it

See how the Last year value disappears. I was expecting that creating this measure would allow you to see Last years metric even with a year slicer set

The answer to this is actually quite simple

Here I have used Year which is an actual data column in my date table to slice the data with and its not working

This time I have used year from my date hierarchy and its worked

Which to me meas that if you are using these kind of time based measures, if you want to slice by date you HAVE to use the time period from the date hierarchy in the slicer rather than another data item (As in the example. I used the Year column created in the date dimension)

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