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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

R visuals limitations

R visuals have some important limitations regarding the data they can handle, both as input and output:

  • An R visual can handle a data frame with only 150,000 rows. If there are more than 150,000 rows, only the first 150,000 rows are used and a relevant message is displayed on the image.
  • R visuals have an output size limit of 2 MB.

You must also be careful not to exceed the 5 minutes of runtime calculation for an R visual in order to avoid a time-out error. Moreover, in order not to run into performance problems, note that the resolution of the R visual plots is fixed at 72 DPI.

As you can imagine, some limitations of R visuals are different depending on whether you run the visual on Power BI Desktop or the Power BI service.

To create reports in Power BI Desktop, you can do any of the following:

  • Install any kind of package (CRAN, GitHub, or custom) in your engine for R visuals.
  • Install only CRAN packages in your engine for custom...
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