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

Importing the custom visual package into Power BI

Now that the bulk of the work is done, importing your custom visual into Power BI is a breeze. First of all, you need to install the xml2 package in your R engine, as it is used by the provided utility functions:

  1. Open RStudio and make sure it is referencing your latest CRAN R (version 4.0.2 in our case).
  2. Click on the Console window and enter this command: install.packages('xml2'). If you remember, this library is listed in the dependency file you saw in the previous section. Then, press Enter.

Let's now import the custom visual in Power BI:

  1. Make sure that Power BI Desktop references the correct R engine (the latest one) in the Options.
  2. Click on Get Data, search for web, select Web, and click on Connect.
  3. Enter the following URL as source: Then press OK.
  4. Make sure that the File Origin is 65001: Unicode (UTF-8) and press Load.
  5. Click the ellipses...