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Extending Power BI with Python and R

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

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801078207
Length 558 pages
Edition 1st Edition
Languages
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Author (1):
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Luca Zavarella Luca Zavarella
Author Profile Icon Luca Zavarella
Luca Zavarella
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Table of Contents (22) Chapters Close

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:

  1. 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...

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