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

Implementing missing value imputation algorithms

From here on, all missing value analysis will be done in R because very statistically specialized and simple-to-use packages that do not exist in the Python ecosystem have been developed for this language.

Suppose we need to calculate the Pearson correlation coefficient between the two numerical variables, Age and Fare, of the Titanic disaster dataset. Let's first consider the case where missing values are eliminated.

Removing missing values

The impact of applying listwise and pairwise deletion techniques is evident in the calculation of Pearson's correlation between numerical variables in the Titanic dataset. Let's load the data and select only numeric features:

dataset_url <- ''
tbl <- readr::read_csv(dataset_url)
tbl_num <- tbl %>% 
  select( where(is.numeric) )

If you now calculate the correlation matrix for the two techniques...