You're reading from Extending Power BI with Python and R - Second Edition

Product type Book

Published in Mar 2024

Publisher Packt

ISBN-13 9781837639533

Pages 814 pages

Edition 2nd Edition

Languages

Python

Concepts

Business Intelligence

Author (1):

Luca Zavarella

Table of Contents (27) Chapters

Preface

1. Where and How to Use R and Python Scripts in Power BI

2. Configuring R with Power BI

3. Configuring Python with Power BI

4. Solving Common Issues When Using Python and R in Power BI

5. Importing Unhandled Data Objects

6. Using Regular Expressions in Power BI

7. Anonymizing and Pseudonymizing Your Data in Power BI

8. Logging Data from Power BI to External Sources

9. Loading Large Datasets Beyond the Available RAM in Power BI

10. Boosting Data Loading Speed in Power BI with Parquet Format

11. Calling External APIs to Enrich Your Data

12. Calculating Columns Using Complex Algorithms: Distances

13. Calculating Columns Using Complex Algorithms: Fuzzy Matching

14. Calculating Columns Using Complex Algorithms: Optimization Problems

15. Adding Statistical Insights: Associations

16. Adding Statistical Insights: Outliers and Missing Values

17. Using Machine Learning without Premium or Embedded Capacity

18. Using SQL Server External Languages for Advanced Analytics and ML Integration in Power BI

19. Exploratory Data Analysis

20. Using the Grammar of Graphics in Python with plotnine

21. Advanced Visualizations

22. Interactive R Custom Visuals

23. Other Books You May Enjoy

24. Index

Appendix 1: Answers

1. Appendix 2: Glossary

Installing an IDE for R development

The need to install a state-of-the-art R IDE for the development of code in Power BI comes from the need to have all the tools necessary to identify any bugs and 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 JupyterLab, 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. For this reason, we suggest that you also use this IDE to test the code you will encounter in this book.