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

Chapter 2: Configuring R with Power BI

Power BI Desktop is not equipped with the analytical language engines presented in the previous chapter by default. Therefore, it is necessary to install these engines and properly configure Power BI Desktop to correctly interface with them. It is also recommended to install an Integrated Development Environment (IDE), enabling you to work in the way you are most comfortable.

We'll look at how to get those engines up and running and give you some general guidelines on how to pick the most appropriate one for your needs. After that, we'll look at how to make these engines interface with both Power BI Desktop and the Power BI service.

Finally, we will give some important tips on how to overcome some stringent limitations of R visuals on the Power BI service.

In particular, this chapter will deal in detail with the following topics:

  • The available R engines
  • Choosing an R engine to install
  • Installing an IDE for R...