DAX (Function) ALL With FILTER: How To Use It

how-to Nov 13, 2023

If you're a data enthusiast like me, you're probably well aware of the incredible capabilities of DAX (Data Analysis Expressions) in Microsoft Power BI and Excel.

DAX is a powerful language that empowers you to perform complex calculations and transformations on your data.

In this blog post, I'll explore a particularly useful DAX function: ALL with FILTER.

DAX Overview

Before we dive into the intricacies of ALL with FILTER, let's get acquainted with some basics of DAX.

DAX is a formula language primarily used for data modeling and analysis in Power BI, Excel, and other Microsoft tools.

With a plethora of functions at your disposal, DAX lets you create custom calculations and aggregations, making it an indispensable tool for data professionals.

One crucial concept in DAX is context.

DAX calculations rely on the current row context and the filter context.

Row context represents the individual row being evaluated, while filter context defines which rows are included or excluded from calculations.

Understanding these contexts is pivotal for mastering DAX.

ALL and FILTER

Now, let's look at ALL and FILTER.

ALL is a DAX function that acts as a filter remover.

It's like saying, "Let's temporarily disregard all filters and focus on everything."

FILTER, on the other hand, allows you to create a new table by specifying conditions for the data you want to include.

Practical Use Cases: ALL and FILTER

Aggregations Free from Filters:

Imagine you have a dataset with sales data for various product categories, and you want to calculate the total sales for each category, irrespective of any filters applied. This is precisely where ALL with FILTER comes into play. You can use ALL to eliminate existing filters and then apply a FILTER to select the rows you want for your calculation.

Total Sales = 
SUMX(
    FILTER(
        ALL('Product'),
        'Product'[Category] = "Electronics"
    ),
    'Sales'[Amount]
)

In this scenario, you utilize ALL to remove filters on the 'Product' table and FILTER to include only the rows with the category "Electronics." The result? The total sales for the "Electronics" category, without any interference from external filters.

Comparative Analysis Made Easy:

ALL with FILTER proves invaluable when you need to conduct comparative analysis. Let's say you want to compare the sales performance of a specific product against the average sales of all products within the same category. Here's your solution:

Product vs. Category Average Sales = 
DIVIDE(
    SUMX('Sales', 'Sales'[Amount]),
    CALCULATE(
        AVERAGEX(
            FILTER(
                ALL('Product'),
                'Product'[Category] = SELECTEDVALUE('Product'[Category])
            ),
            'Sales'[Amount]
        )
    )
)

In this formula, ALL with FILTER is employed to calculate the average sales for all products within the same category as the selected product. This ensures a fair and accurate comparison.

Conclusion

The ALL with FILTER combination is a versatile tool that can help you solve a wide range of data analysis challenges

Experimenting with ALL and FILTER will help you get familiar with the syntax and solve problems with your data more quickly.

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