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

Importing large datasets with R

The same scalability limitations illustrated for Python packages used to manipulate data also exist for R packages in the Tidyverse ecosystem. Even in R, it is not possible to use a dataset larger than the available RAM on the machine. The first solution that is adopted in these cases is also to switch to Spark-based distributed systems, which provide the SparkR language. It provides a distributed implementation of the DataFrame you are used to in R, supporting filtering, aggregation, and selection operations as you do with the dplyr package. For those of us who are fans of the Tidyverse world, RStudio actively develops the sparklyr package, which allows you to use all the functionality of dplyr, even for distributed DataFrames. However, adopting Spark-based systems to process CSVs that together take up little more than the RAM you have available on your machine may be overkill because of the overhead introduced by all the Java infrastructure needed...