Use R in Query Editor
R is a powerful programming language that many statisticians, data scientists, and data analysts use. You can use R in Power BI Desktop's Query Editor to:
Prepare data models
Do data cleansing, advanced data shaping, and dataset analytics, which include missing data completion, predictions, clustering, and more.
You need to have the mice library installed in your R environment. Without mice, the sample script code won't work properly. The mice package implements a method to deal with missing data.
To install mice:
Launch the R.exe program (for example, C:\Program Files\Microsoft\R Open\R-3.5.3\bin\R.exe)
Run the install command:
Use R in Query Editor
To demonstrate using R in Query Editor, we'll use an example stock market dataset contained in a .csv file and work through the following steps:
Download the EuStockMarkets_NA.csv file. Remember where you save it.
Load the file into Power BI Desktop: from the Home ribbon, select Get Data > Text/CSV.
Select the file and then Open. The CSV data is displayed in the Text/CSV file dialog.
Once the data is loaded, you can see it in the Fields pane.
To open Query Editor, from the Home ribbon select Edit Queries.
In the Transform ribbon, select Run R Script. The Run R Script editor appears.
Rows 15 and 20 have missing data, as do other rows you can't see in the image. The steps below show how R completes those rows for you.
For this example, enter the following script code. Be sure to replace '<Your File Path>' with the path to EuStockMarkets_NA.csv on your local file system, for example, C:/Users/John Doe/Documents/Microsoft/EuStockMarkets_NA.csv
dataset <- read.csv(file="<Your File Path>/EuStockMarkets_NA.csv", header=TRUE, sep=",") library(mice) tempData <- mice(dataset,m=1,maxit=50,meth='pmm',seed=100) completedData <- complete(tempData,1) output <- dataset output$completedValues <- completedData$"SMI missing values"
After selecting OK, Query Editor displays a warning about data privacy.
For the R scripts to work properly in the Power BI service, you need to set all data sources public. For more information about privacy settings and their implications, see Privacy Levels.
After selecting Save, the script runs. Notice a new column in the Fields pane called completedValues. Notice there are a few missing data elements, such as on row 15 and 18. Take a look at 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.
Create 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:
You can save all completed visuals in one Power BI Desktop .pbix file and use the data model and its R scripts in the Power BI service.
You can download a .pbix file with all these steps completed.
Once you've uploaded the .pbix file to the Power BI service, you need to take additional steps to enable service data refresh and updated visuals:
Enable scheduled refresh for the dataset - to enable scheduled refresh for the workbook containing 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 and R are located. The Power BI service accesses that workbook and re-renders any updated visuals. For more information, see 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. All other steps in a Query Editor query must also be public. To get to data source settings, in Power BI Desktop, select File > Options and settings > Data source settings.
In the Data Source Settings dialog, select the data source(s) and then Edit Permissions.... Set the Privacy Level 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 containing the workbook and R. 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.