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

Implementing distances using R

The scenario will be the same as the one already described in the previous section. We will therefore enrich the data relating to some hotels in New York City with the distances separating them from the two major airports of New York, namely John F. Kennedy and LaGuardia.

The files containing the data to be processed can be found in the Chapter10 folder of the GitHub repository. In detail, you will find the hotels data in the hotels-ny.xlsx file and the airports data in the airport-codes.csv file.

Calculating distances with R

The R community is also fortunate to have a freely available package that implements spherical trigonometry functions for geographic applications. The package is called geosphere ( and, like the Python PyGeodesy package, it is inspired by the code that Chris Veness and Charles Karney have made publicly available.

First, you need to install this new package:

  1. Open...