Book Image

DAX Cookbook

By : Greg Deckler
Book Image

DAX Cookbook

By: Greg Deckler

Overview of this book

DAX provides an extra edge by extracting key information from the data that is already present in your model. Filled with examples of practical, real-world calculations geared toward business metrics and key performance indicators, this cookbook features solutions that you can apply for your own business analysis needs. You'll learn to write various DAX expressions and functions to understand how DAX queries work. The book also covers sections on dates, time, and duration to help you deal with working days, time zones, and shifts. You'll then discover how to manipulate text and numbers to create dynamic titles and ranks, and deal with measure totals. Later, you'll explore common business metrics for finance, customers, employees, and projects. The book will also show you how to implement common industry metrics such as days of supply, mean time between failure, order cycle time and overall equipment effectiveness. In the concluding chapters, you'll learn to apply statistical formulas for covariance, kurtosis, and skewness. Finally, you'll explore advanced DAX patterns for interpolation, inverse aggregators, inverse slicers, and even forecasting with a deseasonalized correlation coefficient. By the end of this book, you'll have the skills you need to use DAX's functionality and flexibility in business intelligence and data analytics.
Table of Contents (15 chapters)

Constructing a patient cohort (AND slicer)

In the healthcare industry, researchers often desire to create what are called patient cohorts. Patient cohorts are groups of patients that all share the same diagnoses, treatments, or other characteristics. These patient cohorts are used for research and study purposes. It is important to stress here that these cohorts generally contain multiple criteria and that every patient included in the cohort must meet all of the criteria. In other words, the inclusion requirements generally represent a logical AND of conditions, not a logical OR.

This recipe demonstrates how to construct a patient cohort from a set of patient diagnoses. To accomplish this, this recipe effectively changes the normal operation of a Power BI Slicer visualization from a logical OR to a logical AND. Thus, it has many other potential applications outside of just building...