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Extending Power BI with Python and R

You're reading from   Extending Power BI with Python and R Perform advanced analysis using the power of analytical languages

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781837639533
Length 814 pages
Edition 2nd 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 (27) Chapters Close

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

Installing RStudio

Installing RStudio (at the time of writing the chapter, the version is 2022.07.2+576) on your machine is very simple:

  1. Go to https://posit.co/download/rstudio-desktop/ and click the DOWNLOAD RSTUDIO...
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