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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

Importing the custom visual package into Power BI

Now that the bulk of the work is done, importing your custom visual into Power BI is a breeze. First of all, you need to install the xml2 package in your R engine, as it is used by the provided utility functions:

  1. Open RStudio and make sure it is referencing your latest CRAN R (version 4.0.2 in our case).
  2. Click on the Console window and enter this command: install.packages('xml2'). If you remember, this library is listed in the dependency file you saw in the previous section. Then, press Enter.

Let's now import the custom visual in Power BI:

  1. Make sure that Power BI Desktop references the correct R engine (the latest one) in the Options.
  2. Click on Get Data, search for web, select Web, and click on Connect.
  3. Enter the following URL as source: http://bit.ly/titanic-dataset-csv. Then press OK.
  4. Make sure that the File Origin is 65001: Unicode (UTF-8) and press Load.
  5. Click the ellipses...
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