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)
1
Section 1: Best Practices for Using R and Python in Power BI
5
Section 2: Data Ingestion and Transformation with R and Python in Power BI
11
Section 3: Data Enrichment with R and Python in Power BI
17
Section 3: Data Visualization with R in Power BI

Adding a dash of interactivity with Plotly

There is an open source JavaScript library for data visualization, which is declarative and high-level and allows you to create dozens of types of interactive graphs, named Plotly.js. This library is the core of other Plotly client libraries, developed for Python, Scala, R, and ggplot. In particular, the library developed for R, named Plotly.R (https://github.com/ropensci/plotly), provides the ggplotly() function, which does all the magic for us: it detects all the basic attributes contained in an existing graph developed with ggplot and transforms them into an interactive web visualization. Let's see an example.

First, you need to install the Plotly.R library (https://github.com/ropensci/plotly) on your latest CRAN R engine via the install.packages('plotly') script.

Important Note

For simplicity, we'll make sure to run the custom visual on the latest version of the CRAN R engine, since all the necessary libraries...