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)
1
Section 1: Best Practices for Using R and Python in Power BI
5
Section 2: Data Ingestion and Transformation with R and Python in Power BI
11
Section 3: Data Enrichment with R and Python in Power BI
17
Section 3: Data Visualization with R in Power BI

Summary

In this chapter, you learned about the most popular free R engines in the community. In particular, you learned what advantages have been introduced by Microsoft releasing its distribution of R to the market. Unfortunately, this distribution will be retired in the near future.

Taking note of the unique features of Power BI Desktop and the Power BI service, you have learned how to properly choose the engines and how to install them.

You have also learned about the most popular IDE in the R community and how to install it.

In addition, you were introduced to all of the best practices for properly configuring both Power BI Desktop and the Power BI service with R, whether in a development or enterprise environment.

Finally, you've learned some of the limitations of using R with Power BI, knowledge of which is critical to avoid making mistakes in developing and deploying reports.

In the next chapter, we'll see which Python engines and IDEs to install and...