How to create a Load History or Load Log in Power Query or Power BI

If you want to collect your regularly loaded data without overwriting it or create a load log that writes the load activites with a timestamp into a table, you need to create a query that adds new lines to it’s own latest version.

In both cases you add a column that returns the current day and time using DateTime.LocalNow()


Load Log the PowerBI-way

Then using Power BI it’s fairly easy: Use the R-extension to perform an append query to the external txt or csv-file like described in this post (incremental load):

write.table(trim(dataset), file=”your filepath & filename.txt”, sep = “\t”, row.names = FALSE, append = TRUE, column.names=FALSE)

Load Log the Power Query-way

In Excel’s Power Query it’s a bit trickier, because you cannot export your queries and therefore need to create the consolidated table in your workbook itself. And it must be a self-referencing query or to be more precise: a self-sourcing query: A query that takes its latest output as its new input, then appends the new data and overwrites the old result with the new one.

How do we deal with this? When we write the query we don’t have the result yet to be referenced?

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Use R to export data from Power BI

Edit 03 Aug 2016: With the July 2016 release you can now run your R-scripts directly in the query-editor (so no row-limit any more!). No need to go via the visuals. But this will limit you to export datasets from the query-editor, so no DAX.

Edit 22 Jan 2016: Currently this method is limited to 150k rows! It will drop all others without warning!

Edit 25 Jan 2016: 150k rows is the limit on all R-scripts at the moment (link). A warning sign will be shown on the image. Will create a new blogpost if this changes – so stay tuned (by following this blog, Twitter or LinkedIn)

With the December release, Microsoft enabled Power BI’s content to be fetched from the R-feature. So instead of plotting this data to the screen, could we just export it to a file?

Yes we can:

write.table(trim(dataset), file=”your filepath & filename.txt”, sep = “\t”, row.names = FALSE);

write.table has a lot of options, which are explained here. I’ve only used a few here: Exactly defining the location where the output-file shall be stored (file=), setting tab as delimiter (sep=”\t”) and skipping row names, which would be an index in our example here.

In addition to that, I had to get rid of trailing whitespaces that somehow sneaked into the table (trim(dataset)). Therefore the gdata-package is needed. So the full R-code looks like this:

write.table(trim(dataset), file=”your filepath & filename.txt”, sep = “\t”, row.names = FALSE)

Here you have to make sure to turn the slashes in the filepath: So if your path looks like this: C:\Users\Imke\Desktop\Rfile.txt you need to write it like this: C:/Users/Imke/Desktop/Rfile.txt

NEW: You find a function for this export here: , it swaps the slashed automatically.

Let’s take this as a start that will probably fulfill many users need to quickly export data from Power BI to simple text files. Further use cases I see are:

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Power Query & Power BI are ideal learning paths from Excel to R

Reading Jen Stirrup’s great article about the learning path for SQL Server 2016 and R I ended up learning my first R pieces from this wonderful post that she has referenced. There Tony Ojeda beautifully describes how basic Excel-concepts are translated into R.

Well – I cannot speak of so much personal experience here – but these R-codes came easy as nothing before to me. Apart from Tony’s first class didactics, could this be due to what I’ve learned in Power Query over the last year?

I’ve added the comparison of Excel and R by the M-code from Power Query – putting it in the middle:


Do you find the similarities as striking as I do?

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