Book Image

Extending Power BI with Python and R

By : Luca Zavarella
Book Image

Extending Power BI with Python and R

By: Luca Zavarella

Overview of this book

Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you’ll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.
Table of Contents (22 chapters)
Section 1: Best Practices for Using R and Python in Power BI
Section 2: Data Ingestion and Transformation with R and Python in Power BI
Section 3: Data Enrichment with R and Python in Power BI
Section 3: Data Visualization with R in Power BI

Solving LP problems with R

If the Python community is very active, certainly the R community is not standing still! In fact, the Optimization Modeling Package (OMPR) is available (, which is a domain-specific language created to model and solve LP problems in R.

In general, all other packages developed in R that serve the same purpose are mostly matrix-oriented, forcing you to transform all objects into matrices and vectors before passing them to the solver. This task may seem simple enough at first glance, but when the problems to be solved become complex, it may become difficult to write R code to solve them.

The ompr package, on the other hand, provides enough expressive power to allow you to model your LP problems incrementally, thanks also to the use of the %>% pipe. Therefore, you will feel like you are writing code as if you were using dplyr functions, forgetting about matrices and vectors.

In addition, the ompr package relies...