Tips and Tricks for R scripts in the query editor in Power BI

Especially if you are new to R, there are some things one needs to know to successfully run R-scripts in the query editor of Power BI. I will share them here along with some tricks that made my R-life in Power BI easier:

How to get started – useful links:


You can feed multiple tables into the R-script

If you click the icon “R script”, the table from the previous step will automatically be passed as the “dataset” to the R-script. So if you don’t fill in any R-code, this will happen:


But if you need the content from other tables as well, you just add them into the square brackets like this:


“Documentation” looks like this:


You can use parameters in the R-script

Apart from tables, you can also use text strings as parameters in the script. They need to be inserted into the code with preceeding “& and trailing &”:

RExportCsv= R.Execute(“write.csv(dataset,” “&CsvExportPath&” “)”,[dataset=Actuals])

Beware that they must be text. So if you want to pass a number, wrap it into Text.From(…).

You cannot use anything else apart from tables and parameters in the R-script

Well, at least I haven’t managed it Smile

R-life gets easy-peasy if you use M-functions for your R-script

For an R-script like export to csv for example, you have to reverse all slashes in your path-definition. This is a bit tedious. So just create an M-function that reverses them automatically (row 4) before passing to the R-script like this:

// Exports the current table to csv. Pass full PathFile specification as parameter
(path as text, dataset as table) =>
ConvertedPath = Text.Replace(path, "\", "/"),
RScript = R.Execute("write.csv(dataset,"""&ConvertedPath&""")
output <- dataset
output = RScript{[Name="output"]}[Value]

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Another advantage: You can create a nice user-dialog by adjusting the function metadata like this for example:


So the user doesn’t have to see that R-code is used here.

let func =
// fnStat.Trend
// Author: Imke Feldmann – – Link to blogpost:
(Actuals as table, FCPeriods as list) =>
FCPeriods = Table.FromList(FCPeriods, Splitter.SplitByNothing()),
#"Run R Script" = R.Execute("y <- as.matrix(Actuals[1])
x <- as.matrix(Actuals[2])
trendline <- fitted(lm(y ~ x))
forecast <- predict(lm(y ~ x), list(x = as.matrix(FCPeriods)))
outTrendline <- cbind(Actuals, data.frame(trendline))
outForecast <- cbind(FCPeriods, data.frame(forecast))"
,[Actuals=Actuals, FCPeriods=FCPeriods]),
RStatTrend = #"Run R Script"
, documentation = [
Documentation.Name = " fnRStat.Trend
", Documentation.Description = " R-function for trend (fitted(lm) and forecast (predict(lm)
" , Documentation.LongDescription = " R-function for trend (fitted(lm) and forecast (predict(lm)
", Documentation.Category = " R Statistical Functions
", Documentation.Source = " local
", Documentation.Author = " Imke Feldmann:
", Documentation.Examples = {[Description = " 1) Choose table with actual figures 2) choose column holding the forecast periods
" , Code = " Check this blogpost explaining how it works:
", Result = "
Value.ReplaceType(func, Value.ReplaceMetadata(Value.Type(func), documentation))

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R Trend.pq

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Metadata-adjustment starting in row 18. This post describes the details for adding the function-metadata:


Output of the R-script has to be a data-frame

This one has fooled me a bit: My script worked fine in RStudio, but didn’t return any results in Power BI. As it turned out, the result of the script was a list (or vector) and not a dataframe. So I had to convert it to a dataframe (see row 12 & 13 in R Trend m) and then it worked alright.

You need to assign desired outputs

This might also not be too obvious for beginners: You can successfully run R-code, but not get any result returned from your R-script: Check the ExportCsv-script above: It would export the table to the desired path without the code in row 6 (“output <- dataset”), but we wouldn’t be able to see the data or further work on it in the query editor. So if you use the R-script for transformations and then further work on the results or load them to the data model, you need to create an output by assigning via the “<-“.

You can have multiple output entities

If you run the trend-function from above, you will get a table like this back:


outTrendline contains the trendline for the actual figures and outForecast the trend-predictions for the future periods (…the assignments from row 8-11 are not returned here, because they are not in dataframe-format).

I hope this will help getting you started quickly with R in M !

Do you agree that it’s a real pity that we don’t have the R-integration in Power Query in Excel (yet?)?. Then please vote for this feature here.

Also check out this post explaining how to create and use a function library for your R-functions and applications.

Enjoy & stay queryious 🙂

Comments (11) Write a comment

  1. Pingback: Tips and tricks on using R to query data in Power BI | A bunch of data

  2. I really like your articles, and was thrilled when I saw this. But I just can’t seem to get it to work. I’ve tried to do exactly like in the example, but when I try to load two datasets like this [dataset=Source, dataset2=tbl_A] where Source is the last loaded table and tbl_A is another independent table, it just raises a Formula.Firewall error. Could you provide any more details with regards to how you’ve built your example? Thanks!


    • Hi Arnie,
      this is a general issue with R-scripts and external data sources: You need to adjust your privacy settings to always ignore privacy settings.
      Thanks for bringing this up, should have mentioned it in the post!


  3. How do I call this export to csv function for each row table with a path_to_csv column and a list of table’s column ? Would each line export an csv file? I want to “invoke Custom Funcion” passing columns as parameters.

    (path as text, dataset as table) =>
    ConvertedPath = Text.Replace(path, “\”, “/”),
    RScript = R.Execute(“write.csv(dataset,”””&ConvertedPath&”””)
    output <- dataset
    output = RScript{[Name=”output”]}[Value]


    • Yes, if you call this function in a custom column, for each row 1 csv-file would be created.


  4. Hi Imke,
    One question, I’m using an R script in Power BI (Power Query/Transform) to cluster customers according to their historical transactions.
    My script looks like this:
    k.means.fit_log <- kmeans(dataset[2:4], 3)
    output$cluster <- k.means.fit_log$cluster
    This adds a column to the table with the custer number. It’s fine, but the output of the custer algorithm is k.means.fit_log and it contains a lot more information I’d like to analyse, like custer centers, size, etc.
    In R k.means.fit_log is a list of 9, so I’d like to create 9 tables in Power BI with all these details.
    How can I do this? Any idea?
    Thank you so much for your help and keep writing excellent stuff!


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