Advanced Analytics with Power BI + R

Data is everywhere. The world contains an astronomical amount of data, an amount that grows larger and larger each day. This vast collection of information has changed the way the world interacts uncovered breakthroughs in medicine and revealed new ways to understand trends in business and in our daily lives. With the increasing availability of data comes new challenges and opportunities as business leaders seek to gain important insights and transform information into actionable and meaningful results. As data becomes more accessible, manipulating vast amounts of available data to drive insights and make business decisions can be challenging.

Business leaders at every level need to become data literate and be able to understand data and analytical concepts that may have previously seemed out of reach, including statistical methods, machine learning, and data manipulation. With this spread of data literacy comes the powerful ability to make educated business decisions that rely on the smart use of data, rather than on an individual’s opinions. In the past, these tasks were extremely complex and would be handed off to engineers.

With the tools that exist today, business leaders are able to dive into their own analytics and uncover powerful insights. Microsoft Power BI brings advanced analytics to the daily business decision process, allowing users to extract valuable knowledge from data to solve business problems. This white paper will cover the advanced analytic capabilities of Power BI, including predictive analytics, data visualisations, R integration, and data analysis expressions.

Advanced Analytics with Power BI + R
Advanced Analytics
with Power BI

Table of contents
Advanced analytics in Power BI ……………………………………4
Predictive analytics with Azure
R integration
Quick Insights feature
Segmentation and cohort analysis ……………………………….9
Data grouping and Binning
Data streaming in Power BI …………………………………………11
Real-time dashboards
Setup of real-time streaming data sets
Visualizations in Power BI…………………………………………….12
Community-sourced visualizations
R visualisations
Custom visualizations
Data connection and shaping……………………………………….14
Azure services
DirectQuery
Data fetching with the R connector
Data shaping in Power Query with R
Data Analysis Expressions…………………………………………….17
Conclusion ……………………………………………………………………18