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Extending Power BI with Python and R - Second Edition

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
Author (1):
Luca Zavarella Luca Zavarella
Profile icon 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

Why interactive R custom visuals?

Let’s start with a graph you’ve already implemented in R. For example, consider the raincloud plot of Fare versus Pclass variables introduced in Chapter 19, Exploratory Data Analysis (this time not grouped by Sex):

A screenshot of a graph  Description automatically generated

Figure 22.1: Raincloud plot for Fare (transformed) and Pclass variables

Focus for a moment just on the boxplots you see in Figure 22.1. Although the Fare variable is already transformed according to Yeo-Johnson to try to reduce skewness, there are still some extreme outliers for each of the passenger classes described by the categorical variable Pclass. For example, if you wanted to know the values of the transformed variable Fare that correspond to the whiskers (fences) of the boxplot on the left so that you could then identify the outliers that are beyond these whiskers, it would be convenient to have these values appear when you hover the mouse near this boxplot. It would be even more interesting to know the...

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