![]() ![]() 'C:/Users/Dave/REditorWrapper_./input_df_.csv' , (Code is slightly formatted for readability.) Click the arrow icon for this option.įor me, this opened RStudio with a few lines of code pre-written to create an R data frame. But Power BI gives us the option to use an external development environment. The R script editor pane is small and doesn't look user-friendly for development activity. The fields in the R script editor are updated accordingly: I want the Backup Finish Date as a whole, though. The individual hierarchy fields are referenced in the R script editor code. Note the hierarchy for Backup Finish Date. This auto-populates the R script editor, where you'll see the names of the fields that were selected. (You can also drag & drop fields into the Values pane, or on the R script visual on the canvas. With the R script visual selected on the canvas, tick the checkbox next to each data field you want to use for the visual. The source of this data is a simple SQL Server query: In the images above, note the existing "Query1" with three data fields: Backup Finish Date, BackupType, and Database. When clicked the first time in the Power BI report, you'll be prompted to enable script visuals:Ĭlick Enable, and you'll see a placeholder for the R script visual on the canvas, with the R script editor below it: In the Visualizations pane, the block "R" icon should be easy to spot. ![]() Here you'll see options to set the R home directory and the desired R IDE. Then click R scripting in the list of Global Options. Within Power BI desktop, take a moment to click File | Options and settings | Options to open the Options page. I had already installed the R component as part of Machine Learning Services for SQL Server 2017. I won't go into detail on this, so just know you'd need to install that separately. The R engine isn't included with the installation of Power BI desktop. Hopefully you get something of value from this recap. ![]() But so be it-I learned a lot in the process. There may have been other, better options to reach the goal I was aiming for. My experience with both R and Power BI is fairly limited, so I covered a lot of new territory with this exercise. I decided to give this a try after exhausting the limits of my knowledge with the other built-in Power BI visualizations. Power BI desktop provides an option to visualize data with R. ![]()
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