You can use R, a programming language widely used by statisticians, data scientists, and data analysts, in the Power BI Desktop Query Editor. This integration of R into Query Editor lets you perform data cleansing using R, and perform advanced data shaping and analytics in datasets, including completion of missing data, predictions, and clustering, just to name a few. R is a powerful language, and can be used in Query Editor to prepare your data model and create reports.

Installing R

To use R in Power BI Desktop's Query Editor, you need to install R on your local machine. You can download and install R for free from many locations, including the Revolution Open download page, and the CRAN Repository.

Using R in Query Editor

To show how to use R in Query Editor, we'll use an example from a stock market dataset, based on a .CSV file that you can download from here and follow along. The steps for this example are the following:

  1. First, load your data into Power BI Desktop. In this example we'll load the EuStockMarkets_NA.csv file. Select Get Data > CSV from the Home ribbon in Power BI Desktop.

  2. Select the file and select Open, and the CSV is displayed in the CSV file dialog.

  3. Once the data is loaded, you'll see it in the Fields pane in Power BI Desktop.

  4. Open Query Editor by selecting Edit Queries from the Home tab in Power BI Desktop.

  5. In the Transform tab, select Run R Script and the Run R Script editor appears (shown in the next step). Notice that rows 15 and 20 suffer from missing data, as do other rows you can't see in the following image. The steps below show how R can (and will) complete those rows for you.

  6. For this example, we'll enter the following script code:

    tempData <- mice(dataset,m=1,maxit=50,meth='pmm',seed=100)
    completedData <- complete(tempData,1)
    output <- dataset
    output$completedValues <- completedData$"SMI missing values"

    Note: You'll need to have the mice library installed in your R environment for the previous script code to work properly. To install mice, run the following in your R installation: | > install.packages('mice')

    When put into the Run R Script dialog, the code looks like the following:

  7. When we select OK, Query Editor displays a warning about data privacy.

  8. For the R scripts to work properly in the Power BI service, all data sources need to be set to public. For more information about privacy settings and their implications, see Privacy Levels.

    Once we do so, we see a new column in the Fields well called completedValues. Notice there are a few missing data elements, such as on row 15 and 18. We'll see how R handles that in the next section.


With just five lines of R script, Query Editor filled in the missing values with a predictive model.

Creating visuals from R script data

Now we can create a visual to see how the R script code using the mice library completed the missing values, as shown in the following image.

Once that visual is complete, and any other visuals we might want to create using Power BI Desktop, we can save the Power BI Desktop file (which saves as a .pbix file) and then use the data model, including the R scripts that are part of it, in the Power BI service.

Note: Want to see a completed .pbix file with these steps completed? You're in luck - you can download the completed Power BI Desktop file used in these examples right here.

Once you've uploaded the .pbix file to the Power BI service, a couple more steps are necessary to enable data refresh (in the service) and to enable visuals to be updated in the service (the data needs access to R for visuals to be updated). The additional steps are the following:

  • Enable scheduled refresh for the dataset - to enable scheduled refresh for the workbook that contains your dataset with R scripts, see Configuring scheduled refresh, which also includes information about Personal Gateway.

  • Install the Personal Gateway - you need a Personal Gateway installed on the machine where the file is located, and where R is installed; the Power BI service must access that workbook and re-render any updated visuals. You can get more information on how to install and configure Personal Gateway.


There are some limitations to queries that include R scripts created in Query Editor:

  • All R data source settings must be set to Public, and all other steps in a query created in Query Editor must also be public. To get to data source settings, in Power BI Desktop select File > Options and settings > Data source settings.

    From the Data Source Settings dialog, select the the data source(s) and then select Edit Permissions... and ensure that the Privacy Level is set to Public.

  • To enable scheduled refresh of your R visuals or dataset, you need to enable Scheduled refresh and have a Personal Gateway installed on the computer that houses the workbook and the R installation. For more information on both, see the previous section in this article, which provides links to learn more about each.

There are all sorts of things you can do with R and custom queries, so explore and shape your data just the way you want it to appear.