You're reading from Extending Power BI with Python and R Ingest, transform, enrich, and visualize data using the power of analytical languages

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801078207

Length 558 pages

Edition 1st Edition

Languages

Python

Tools

Power BI

Concepts

Business Intelligence

Author (1):

Luca Zavarella

View More author details

Table of Contents (22) Chapters

Preface

1. Section 1: Best Practices for Using R and Python in Power BI

2. Chapter 1: Where and How to Use R and Python Scripts in Power BI FREE CHAPTER

3. Chapter 2: Configuring R with Power BI

4. Chapter 3: Configuring Python with Power BI

5. Section 2: Data Ingestion and Transformation with R and Python in Power BI

6. Chapter 4: Importing Unhandled Data Objects

7. Chapter 5: Using Regular Expressions in Power BI

8. Chapter 6: Anonymizing and Pseudonymizing Your Data in Power BI

9. Chapter 7: Logging Data from Power BI to External Sources

10. Chapter 8: Loading Large Datasets beyond the Available RAM in Power BI

11. Section 3: Data Enrichment with R and Python in Power BI

12. Chapter 9: Calling External APIs to Enrich Your Data

13. Chapter 10: Calculating Columns Using Complex Algorithms

14. Chapter 11: Adding Statistics Insights: Associations

15. Chapter 12: Adding Statistics Insights: Outliers and Missing Values

16. Chapter 13: Using Machine Learning without Premium or Embedded Capacity

17. Section 3: Data Visualization with R in Power BI

18. Chapter 14: Exploratory Data Analysis

19. Chapter 15: Advanced Visualizations

20. Chapter 16: Interactive R Custom Visuals

21. Other Books You May Enjoy

Installing an IDE for R development

The need to install a state-of-the-art IDE for the development of code in Power BI comes from the need to have all the tools necessary to identify any bugs and to quickly test the results of code chunks on the fly.

Tip

It is strongly suggested to test your R code in the IDE and verify the results before using it in Power BI.

There are many IDEs for R development on the market. Some examples are R-Brain IDE (RIDE), IntelliJ IDEA, and Jupyter Lab, but it is estimated that over 90% of R programmers use RStudio as their primary IDE because of the countless features that simplify their daily work. That's why we suggest you also use this IDE to test the code you'll encounter throughout this book.

Installing RStudio

Installing RStudio on your machine is very simple:

Go to https://rstudio.com/products/rstudio/download/ and click on DOWNLOAD under the RStudio Desktop column:
Figure 2.9 – Go to the Download page of RStudio...