Power BI's built-in R and Python integration allows you to run custom machine learning models directly in reports, providing a convenient and seamless way to incorporate advanced analytics into your reporting process.
Here's an example of how you can use R to run a linear regression model in Power BI :
* Load the necessary libraries and datasets into Power BI's R environment.
library(dplyr)
library(tidyverse)
data("mtcars")​
* Run the linear regression model, using mtcars as the input data.
fit <- lm(mpg ~ wt, data = mtcars)​
* Plot the model results using a scatterplot with a regression line.
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)​
* Publish the report to Power BI Service and display the results in a Power BI dashboard.
By integrating R and Python into Power BI, you can leverage the power of these languages to perform advanced analytics and machine learning tasks, while still utilizing Power BI's intuitive reporting and visualization tools to communicate your results to stakeholders.
