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

Correlation between categorical and numeric variables

We have shown that, in the case of two numeric variables, you can get a sense of the association between them by taking a look at their scatterplot. Clearly, this strategy cannot be used when one or both variables are categorical. Note that a variable is categorical (or qualitative, or nominal) when it takes on values that are names or labels, such as smartphone operating systems (iOS, Android, Linux, and so on).

Let's see how to analyze the case of two categorical variables.

Considering both variables categorical

So, is there a graphical representation that helps us understand whether there is a significant association between two categorical variables? The answer is yes and its name is a mosaic plot. In this section, we will take the Titanic disaster dataset as a reference dataset. In order to have an idea of what a mosaic plot looks like, let's take into consideration the variables Survived (which takes values...