R Regex matching of mentions in twitts. with Power Query and R

Reading a Zip file in Power Query via R

This example demonstrates how to read a compressed CSV file directly from a local ZIP archive using Power Query’s R integration. The visualization shows the result of an R script that extracts the file `R_Twitter_sample.csv` from `R_Twitter_sample.zip` and loads it into a table. The Power Query `R.Execute` function runs the R code, which relies on R’s base `read.csv` and `unz` functions to handle the archive and file parsing. The output is then returned to Power Query via the `[Name="datacsv"]` reference, making the data available for further transformation or charting. No external visualization library is used; the focus is purely on data ingestion.

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Fetching Remove Password comments from Github. Using Power Query and R

This example visualizes a GitHub code search for commits that remove passwords, using a Power Query workflow that calls the GitHub Search API to collect matching commits and their file patches. The data flows through Power Query’s table expansion functions before being handed to an R script via `R.Execute`, where a regular expression pattern extracts only the removed lines from each patch. The final table retains the commit metadata and the cleaned removed-line text, omitting the original patch column. The visualization demonstrates a hybrid ETL pipeline combining GitHub’s REST API, Power Query’s M language, and base R string processing.

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Power Query script to get msmdsrv.exe port number via R

This example shows how to retrieve the port number of the msmdsrv.exe process by combining R scripts with Power Query’s M language. It first runs a system command via `system2` to capture the task list and netstat output, then parses the results using `read.fwf` with fixed-width columns. The M code invokes the R script through `R.Execute`, formats the resulting tables with `Table.TransformColumns` and `Table.PromoteHeaders`, and finally joins the process ID from the task list with the matching netstat entry to extract the local address port. The visualization relies on text-based data processing rather than graphical libraries.

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Writing 10M lines of a query result to a table in Power Query via R

This example demonstrates how to write 10 million rows of a query result to a CSV file from Power Query by invoking an R script. It shows the integration of Power Query’s table generation with R’s file-export capabilities, specifically using the `R.Execute` function to run `write.csv` on a dataset. The Power Query code creates a list of numbers from 1 to 10,000,000 via `{1..10000000}`, converts it to a table with `Table.FromList`, and passes that table as the `dataset` parameter to the R script. The R script then writes the data to `d:\Downloads\out.csv`.

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Power Query regex example from http://www.thebiccountant.com/2018/04/25/regex-in-power-bi-and-power-query-in-excel-with-java-script/

This example demonstrates how to extract numeric patterns from a mixed-language string using regular expressions within Power Query. It defines a custom function that invokes JavaScript’s `RegExp` and `String.match` via the `Web.Page` HTML/JavaScript execution environment, passing a target string and a regex pattern. The specific call extracts all digit sequences from “hello012中国1235” and returns them as a list. This approach highlights Power Query’s ability to leverage external scripting for text processing, bridging M’s native functions with JavaScript’s regex engine for advanced pattern matching.

R.Execute example without the use of R.Execute() in Power Query [behind the scenes this gets executed bin\x64\Rscript.exe" --encoding=UTF-8 RScriptWrapper.R"]

This example demonstrates how to execute an R script from within Power Query without explicitly invoking the `R.Execute` function, instead relying on the `AdoDotNet.DataSource` with the `R.Provider` to run a native query. The code passes a string containing an `output` variable that captures the result of `system2("whoami", stdout=TRUE)`, which retrieves the current username. The resulting table is then converted from binary R data using `RData.FromBinary`. This approach showcases the underlying use of `Rscript.exe` via the provider, highlighting an alternative integration path for R workflows in Power Query.

R flavoured Kmeans algorithm in Power Query

This example demonstrates the R-flavored K-means clustering algorithm executed directly within Power Query, using the iris dataset’s petal width and length columns to assign cluster memberships. The visualization shows the resulting iris data points colored by their assigned cluster, with the clustering computed in R via the `kmeans` function and seeded for reproducibility. The Power Query M code invokes the R engine through the `R.Execute` API, passing a script that runs K-means with three centers and twenty random starts, then returns the augmented table containing the cluster labels. The underlying data originates from the built-in iris dataset, and the rendering relies on Power Query’s native table output, not a separate charting library.

Using R.Execute() in Power Query to export the contents of a table to CSV

This example demonstrates how to use R.Execute() in Power Query to export a table’s contents to a CSV file. The code generates a seed list of numbers from 1 to 1,000,000, converts it into a table, and then passes that dataset to R’s write.csv() function via the R.Execute() API. The R script writes the data to a specified file path. The visualization highlights the integration between Power Query and R, showing how R’s data-export capabilities can be invoked directly within a query pipeline, without additional manual steps.