Book Image

Extending Power BI with Python and R

By : Luca Zavarella
Book Image

Extending Power BI with Python and R

By: Luca Zavarella

Overview of this book

Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you’ll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.
Table of Contents (22 chapters)
Section 1: Best Practices for Using R and Python in Power BI
Section 2: Data Ingestion and Transformation with R and Python in Power BI
Section 3: Data Enrichment with R and Python in Power BI
Section 3: Data Visualization with R in Power BI

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...