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

Why interactive R custom visuals?

Let's start with a graph you've already implemented in R. Consider, for example, the raincloud plot of Fare versus Pclass variables introduced in Chapter 14, Exploratory Data Analysis:

Figure 16.1 – Raincloud plot for Fare (transformed) and Pclass variables

Focus for a moment only on the boxplots you see in Figure 16.1. Although the Fare variable is already transformed according to Yeo-Johnson to try to reduce skewness, there remain some extreme outliers for each of the passenger classes described by the categorical variable, Pclass. If, for example, you want to know the values of the transformed variable Fare corresponding to the whiskers (fences) of the boxplot on the left so that you can then determine the outliers located beyond those whiskers, it would be convenient that these values appear when you pass the mouse near that boxplot, as in Figure 16.2:

Figure 16.2 – Main labels...