Using R and Python to interact with your data

In the previous section, you saw all the ways you can interact with your data in Power BI via R or Python scripts. Beyond knowing how and where to inject your code into Power BI, it is very important to know how your code will interact with that data. It's here that we see a big difference between the effect of scripts injected via Power Query Editor and scripts used in visuals:

• Scripts via Power Query Editor: This type of script will transform the data and persist transformations in the model. This means that it will always be possible to retrieve the transformed data from any object within Power BI. Also, once the scripts have been executed and have taken effect, they will not be re-executed unless the data is refreshed. Therefore, it is recommended to inject code in R or Python via Power Query Editor when you intend to use the resulting insights in other visuals, or in the data model.
• Scripts in visuals: The scripts used within the R and Python script visuals extract particular insights from the data and only make them evident to the user through visualization. Like all the other visuals on a report page, the R and Python script visuals are also interconnected with the other visuals. This means that the script visuals are subject to cross-filtering and therefore they are refreshed every time you interact with other visuals in the report. That said, it is not possible to persist the results obtained from the visuals scripts in the data model.

  Tip

  Thanks to the interactive nature of R and Python script visuals due to cross-filtering, it is possible to inject code useful to extract real-time insights from data, but also from external sources (you'll see how in Chapter 9, Calling External APIs to Enrich Your Data). The important thing to keep in mind is that, as previously stated, it is then only possible to visualize such information, or at the most to write it to external repositories (as you will see in Chapter 7, Logging Data from Power BI to External Sources).

In the final section of this chapter, let's look at the limitations of using R and Python when it comes to various Power BI products.