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I know there are 3 ways of using R with Power BI: import, data transformation in Power Query, and custom R visuals
From my understanding it seems that using custom R visual is the only one that has a big advantage over Power BI native visuals and expands possibilities. This might be a limited view as I haven't played around with data import & transformation using R too much.
Anyone who's worked with R with Power BI, especially in importing data and transformations? I would love to hear what everyone thinks R does better than DAX/M and why it should be leveraged.
Thanks!
Solved! Go to Solution.
@Anonymous ,
Do you mean the advantage of using R script in power query, right? Reasons are as below:
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.
Community Support Team _ Jimmy Tao
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
@Anonymous ,
Do you mean the advantage of using R script in power query, right? Reasons are as below:
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.
Community Support Team _ Jimmy Tao
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
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