Power BI & R – Data Connector

This is my third post on integrating predictive modeling with R into a Power BI report. The first two posts covered the methods of integration using:

  1. R Custom Visuals
  2. R Scripts in the Power Query Editor

A third method is using an R Script as a data source directly. This means all of the ETL steps typically done with M code in the Power Query Editor are all done in the R Script instead. I have covered the R Data Connector in a previous post.

In this example, I created two predictive models from the Premier League Fantasy Football data set and wanted to visually compare the results in a Power BI report.

I created two exclusive subsets of the data set by separating the top six Premier League teams from the rest of the league.

I wanted to predict the points scores by players using three other features:

  • price of the player
  • minutes played in the season
  • percentage of fantasy teams that selected the player

I created a linear model on each subset and then tidied the results using the broom package. I combined these results into one table and used this table as a source for my Power BI report.

My full R Script I used for this example can be found below:

I loaded the R Script into the Power Query Editor.

I loaded the query and began to visualize the results of the two models. Two main insights jumped out from a visual comparison with a clustered column chart:

  1. The intercept, teams selected by and price features all had a similar significance in predicting points for both groups.
  2. The minutes played feature is more significant in predicting points for players who play for teams outside of the top six in the Premier League.

That is how I used the R data connector to visualize the results of two predictive models in Power BI. I will continue to explore how to bring more machine learning into Power BI over the coming weeks!

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