I used to be engaged on a mission a wee bit in the past that the client had conditional formatting requirement on a Column Chart.
They needed to format the columns within the chart conditionally primarily based on the typical worth primarily based on the extent of hierarchy you might be at.
Right here is the state of affairs, I’ve a Calendar hierarchy as beneath:

• Calendar Hierarchy:
  • Yr
  • Semester
  • Quarter
  • Month
  • Day

I take advantage of “Journey Works DW2017, Web Gross sales” Excel as my supply in Energy BI Desktop. If I need to visualise “Complete Gross sales” over the above “Calendar Hierarchy” I get one thing like this:

Now I activate “Common Line” from “Analytics” tab of the Line chart.

Once I drill down within the line chart the Common line reveals the typical of that individual hierarchy degree that I’m in. That is fairly cool that I get the typical base on the extent that I’m in code free.

Straightforward, proper?

Now, the requirement is to indicate the above behaviour in a “Column Chart” (sure! visualising time collection with column chart, that’s what the client desires) and spotlight the columns with values beneath common quantity in Orange and depart the remaining in default theme color.

So, I must create Measures to conditionally format the column chart. I additionally want so as to add a little bit of clever within the measures to:

• Detect which hierarchy degree I’m in
• Calculate the typical of gross sales for that individual hierarchy degree
• Change the color of the columns which might be beneath the typical quantity

Let’s get it performed!

Detecting Hierarchy Degree with ISINSCOPE() DAX Operate

Microsoft launched ISINSCOPE() DAX perform within the November 2018 launch of Energy BI Desktop. Quickly after the announcement “Kasper de Jonge” wrote a concise blogpost about it.

So I attempt to hold it so simple as attainable. Right here is how is works, the ISINSCOPE() perform returns “True” when a specified column is in a degree of a hierarchy. As acknowledged earlier, we’ve a “Calendar Hierarchy” together with the next 5 ranges:

• Yr
• Semester
• Quarter
• Month
• Day

So, to find out if we’re in every of the above hierarchy ranges we simply must create DAX measures like beneath:

ISINSCOPE Yr		=	ISINSCOPE('Date'[Year])

ISINSCOPE Semester	=	ISINSCOPE('Date'[Semester])

ISINSCOPE Quarter	=	ISINSCOPE('Date'[Quarter])

ISINSCOPE Month		=	ISINSCOPE('Date'[Month])

ISINSCOPE Day		=	ISINSCOPE('Date'[Day])

Now let’s do a simple experiment.

• Put a Matrix on the canvas
• Put the “Calendar Hierarchy” to “Rows”
• Put the above measures in “Values”

As you see the “ISINSCOPE Yr” reveals “True” for the “Yr” degree. Let’s develop to the to the subsequent degree and see how the opposite measures work:

Consolidating Measures in One Measure

Now that we see how ISINSCOPE() perform works, let’s take one other step additional and see how we will consolidate all measures into only one measure. Bear in mind, our state of affairs is to calculate Common values for every hierarchy degree. I take advantage of a mix of “SWITCH()“, “TRUE()” and “ISINSCOPE()” features to establish every degree. There’s a caveat in utilizing the mix of the three features that I clarify.

Here’s what we would like obtain on this part. We wish to have the ability to present the hierarchy degree in a Matrix visible. To take action we use “SWITCH()” perform as beneath:

• If hierarchy degree is Yr then present “Yr”
• If hierarchy degree is Semester then present “Semester”
• If hierarchy degree is Quarter then present “Quarter”
• If hierarchy degree is Month then present “Month”
• If hierarchy degree is Day then present “Day”

Let’s replicate the above in DAX. One thing like this will work proper?

Hierarchy Degree = 
SWITCH(
    TRUE()
        , ISINSCOPE('Date'[Day]), "Day"
        , ISINSCOPE('Date'[Month]), "Month"
        , ISINSCOPE('Date'[Quarter]), "Quarter"
        , ISINSCOPE('Date'[Semester]), "Semester"
        , ISINSCOPE('Date'[Year]), "Yr"
        , "Different"
    )

As per the documentation of the “SWITCH()” perform the above expression should work like this:

Consider logical “TRUE()” in opposition to a listing of values that are the ISINSCOPE() features and return ONE of a number of consequence expressions. Subsequently, once we use the above measure in a Matrix with the “Calendar Hierarchy” we’ll get to detect every hierarchy degree in a single single measure.

As you see we appropriately detected the hierarchy ranges in a single measure. Right here is the caveat, we’ve to create a listing of values in reverse order as we see within the our hierarchy. So, “Day” in “Calendar Hierarchy” is degree 5 and “Yr” is degree 1, subsequently, we begin with “Day” once we write our SWITCH() perform. If we need to write the above measure with IF() we’ll have one thing like beneath:

Hierarchy Degree with IF = 
IF(ISINSCOPE('Date'[Day]), "Day"
    , IF(ISINSCOPE('Date'[Month]), "Month"
        , IF(ISINSCOPE('Date'[Quarter]), "Quarter"
            , IF(ISINSCOPE('Date'[Semester]), "Semester"
                , IF(ISINSCOPE('Date'[Year]), "Yr", "Different")
            )
        )
    )
)

Calculate Common of Gross sales Hierarchy Ranges

The following step is to calculate Common Gross sales for every hierarchy degree as beneath:

Every day Avg = 
AVERAGEX(
    ALL('Date'[Date])
    , [Total Sales]
    )

Month-to-month Avg = 
CALCULATE(
    AVERAGEX(
        ALL('Date'[Year], 'Date'[Month], 'Date'[MonthNumberOfYear])
        , [Total Sales]
        )
    , ALLEXCEPT('Date', 'Date'[Year], 'Date'[Month], 'Date'[MonthNumberOfYear])
    )

Observe that I used ‘Date'[Month] together with ‘Date'[MonthNumberOfYear] in each ALL and ALLEXCEPT features. The explanation for that’s that I sorted ‘Date'[Month] column by ‘Date'[MonthNumberOfYear]. Be taught extra about potential unintended effects of sorting a column by one other column right here.

Quarterly Avg = 
CALCULATE(
    AVERAGEX(
        ALL('Date'[Year], 'Date'[Quarter])
        , [Total Sales]
        )
    , ALLEXCEPT('Date', 'Date'[Year], 'Date'[Quarter])
    )

Semesterly Avg = 
CALCULATE(
    AVERAGEX(
        ALL('Date'[Year], 'Date'[Semester])
        , [Total Sales]
        )
    , ALLEXCEPT('Date', 'Date'[Year], 'Date'[Semester])
    )

Yearly Avg = 
CALCULATE(
    AVERAGEX(
        ALL('Date'[Year])
        , [Total Sales]
        )
    , ALLEXCEPT('Date', 'Date'[Year])
    )

Now we have to create one other measure just like the “Hierarchy Degree” measure we created earlier utilizing SWITCH(), TRUE() and ISINSCOPE() features so it reveals “Gross sales Common” for every related hierarchy degree. The measure seems to be like beneath:

Common Gross sales by Hierarchy Degree = 
SWITCH(TRUE()
        , ISINSCOPE('Date'[Day]), [Daily Avg]
        , ISINSCOPE('Date'[Month]), [Monthly Avg]
        , ISINSCOPE('Date'[Quarter]), [Quarterly Avg]
        , ISINSCOPE('Date'[Semester]), [Semesterly Avg]
        , ISINSCOPE('Date'[Year]), [Yearly Avg]
    )

Creating Conditional Formatting Measure

The final piece of the puzzle is to create a measure that we’re going to make use of to format our column chart conditionally. The beneath measure determines if the “Gross sales” is beneath “Common Gross sales by Hierarchy Degree” then returns “Orange” else it does nothing.

Column Chart Avg Conditional Formatting = 
SWITCH(
    TRUE()
    , ISBLANK([Total Sales]), BLANK()
    , [Total Sales] < [Average Sales by Hierarchy Level], "Orange"
    , BLANK()
)

Now we’re all set. The one remaining half is to make use of the above measure to conditionally format a column chart that reveals “Gross sales” Over “Calendar Hierarchy”.

• Put a Column Chart on the report web page
• Put “Complete Gross sales” to “Values”
• Put “Calendar Hierarchy” to Axis

• Develop “Information color” from “Format” tab from “Visualisations” Pane
• Hover over default color
• Click on ellipsis button
• Click on “Conditional Formatting”
• Choose “Area Worth” from “Format by” dropdown
• Choose the latter measure we created from the “Primarily based on area” part then click on OK

Here’s what you get:

As you may see we decided Gross sales beneath common primarily based on hierarchy degree we’re at. To make this even higher we will allow a median line within the bar chart. This may be performed from the “Analytics” tab and enabling “Common line”.

Now in case you develop all the way down to the opposite ranges you may rapidly see the when you could have Gross sales beneath common.

Observe: The above measure used within the conditional formatting of the Bar Chart DOESN’T work in case you allow “Drill down” because it places filters on the chosen merchandise that you simply drilled down. So that you’d be higher to disable “Drill down” button from the “Visible Header” settings.

Observe: This solely impacts the reader view when the report is revealed to Energy BI Service, subsequently, you can’t see its impact in Energy BI Desktop.

Have you ever used this methodology earlier than? Are you aware a greater method to deal with this? Please tell us within the feedback part beneath.