I’m comfortable with the standard "point-and-click" transformations in Power Query, but I’m starting to run into advanced requirements—things like predictive imputation, complex text mining, and advanced statistical normalization.
I know I can use the "Run R Script" step in the Transform tab, but I’m curious about the specific use cases where R really shines compared to standard M code. For example, can I use R to fill in missing values based on a regression model rather than just "Fill Down"? What about feature engineering for machine learning? Are there specific libraries I should be using for these tasks in 2026?
3 answers
If you're doing any kind of Natural Language Processing (NLP), R is far superior to native Power BI tools.
While Power Query is great for structural changes, R is a powerhouse for Statistical Transformations. One of the most common "killer apps" for R in Power BI is Predictive Imputation using the mice (Multivariate Imputation by Chained Equations) library.
Instead of just replacing a null with a zero or the previous value, R can look at other columns (like Age, Income, and Location) to predict what the missing value likely was.
Don't overlook Feature Engineering for Machine Learning.
Exactly, Steven. In my recent 2026 projects, I've been using R to perform One-Hot Encoding for categorical variables and Z-score Normalization for numeric features. While you could do this in DAX, doing it in the R transformation step makes your data model much leaner and faster. Also, libraries like dplyr make "Long to Wide" or "Wide to Long" pivots much more flexible than the standard Power BI Pivot/Unpivot tool, especially when dealing with multiple value columns simultaneously.
I use the stringr and tidytext libraries within Power Query to handle messy product descriptions. You can perform Tokenization, Stemming, and Sentiment Analysis directly during the data load. For example, you can transform a column of customer reviews into a "Sentiment Score" or a "Keyword List" before the data even hits your model. This is much faster than trying to write complex M code or regex for every variation of a word.