Create a function library in Power BI using M-Extensions

Having the ability to use own M-function the same way than native functions in Power BI and Excel has been one of my biggest wishes for quite some time. So I was more than amazed to see Frank Tonsen’s comment showing a way to do exactly this in PowerBI, that has been available for almost half a year now: M-Extensions as part of the custom connectors.

Unlike custom connectors who show up in the import-dialogue and provide a custom tailored option for importing data or creating queries, M-Extensions don’t show up explicitly anywhere in Power BI: They just do their M(agic) job to make the functions that you’ve defined in them accessible, as if they were inbuilt native functions: Type their name into the formula bar like this (1):

And enjoy the function description (2: if you’ve specified it in the definition, which is optional):

Simplest example

  1. Your functions: Number.Double and Number.Triple
  2. Combined with the keyword “shared” and separated by “;”
  3. Prefix by “section MyLibrary” gives this text:
section MyLibrary;
shared Number.Double = (Number as number) =>
2 * Number;
shared Number.Triple = (Number as number) =>
3 * Number;

 

How to make M-Extensions work

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Number.Mod rescue pack for Power BI and Power Query

If you use the M-function Number.Mod in Power BI or Power Query and expect the same result like in Excel or DAX, you are probably in good company.

But if the signs of the number and the divisor are not the same, M will differ from Excel and DAX:

Number.Mod in M is different

This is by design, so you can use this this formula instead, if you need matching results:

[Number] – [Divisor] * Number.RoundDown( [Number] / [Divisor] )

Enjoy & stay queryious 🙂

How to open a complex JSON record in Power BI and Power Query

Today I’ll show you a very useful technique how to deal with a JSON record that contains a wild mixture of different elements like this:

If you click on one of the expandable elements, their content will be shown, but you’ll loose all the “surrounding” information (metadata) that is visible now. This is often an issue, regardless if you want to create multiple tables from it to build a star-schema or just need a handful of fields or a denormalized table. But with a little help from M, you’re good to go:

Table.FromRecords( { MyJsonRecord } )

Will returns this:

With this move, every expansion of one of the expandable elements will keep the existing data in place:

Create one big flat table

Simply expand one element after each other to create a denormalized table

Create star schema

For multiple tables, keep this query and reference it to create you (sub-)tables. Always keep the Id-column as the key (!) to combine all the tables in your data model later. (Provided you use this in a function for multiple entities/series)

Best is to play with it, so just past this code into the advanced editor:

 

If your JSON-record has a different structure with “just” header and data in different fields, this technique will be more suitable for you: http://www.thebiccountant.com/2016/04/23/universal-json-opener-for-quandl/

Enjoy & stay queryious 🙂

How to create and use an R-function-library in Power BI

Edit 10-10-2017: There is also a (simpler) way to run a custom function library described here: http://www.thebiccountant.com/2017/10/06/create-a-function-library-in-power-bi-using-m-extensions/ . If you go that route, the only point of interest in the article might be how to create your function library automatically.

Once you’ve discovered the huge potential R gives you to expand your analytical toolbox in Power BI (check some tips & tricks in my previous blogpost if you haven’t already), you might wish to have all your awesome functions conveniently at hand when designing new solutions. And thanks to M, there’s actually nothing easier than that: R-function-library in a record (which works just the same for M-functions 🙂 )

Put your functions into a record (fnr) with the function name as the field name and the function itself as the value: One query to hold them all (and not cluttering your editor pane) and ready to use as if they were native functions:

R-function-library

Use

will export content of my query “Actuals” to csv-file on my desktop.

  1. fnr is the name of the record. You can give it your own name of course, I prefer to keep this as short as possible.
  2. followed in square bracket is the name of the function (record field name)
  3. in ordinary brackets you have the function arguments just like in standard M (record value)

Create record

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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:

Input:

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:

image

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

image

“Documentation” looks like this:

image

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

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How to import from Excel with cell coordinates in Power Query and Power BI

There might be occasions where you want to import data from Excel into Power Query or Power BI using cell coordinates like a range from E3 until G9 for example (“A1 cell reference style”). The function I provide below also caters for the potential pitfalls of this task that Maxim Zelensky has described in his article.

Background

If your worksheet has one leading empty row and column, the import will ignore them and automatically return the range starting from B2. So to fetch the range E3:G9 you have to delete the first row and the first 3 columns. But as Maxim has found out, remaining formats on empty cells will lead to an import of empty rows and columns. So the number of rows and columns to delete will vary and is hard/impossible to predict.

Method

The range that PowerQuery or PowerBI will import is stored in the Excel-file already in the sheet-data and the xml looks like this (“Sample3” from Maxims data):

The imported range is E1 till J12, as the first rows contain formatting instructions, and will therefore be imported as well. In the 3rd row E3 shows up with the first value, which is surrounded by “<v>”.

This is how it looks like in the Xml.Table in the query editor:

Task is to calculate the number of rows and columns delete, considering the individual offset that is caused by the formatted empty cells.

Code

So I’ve cooked together these ingredients in a pretty massive code that you can download here: fnImportFromExcelCellCoordinates.txt

How to use the function

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Should we pipe M?

“Just because you could doesn’t mean you should”… So I’m asking the Power Query and M fans & experts here if we “should” pipe M:

Background: With M you can nest your expressions like in Excel to group commands that belong together. But this has some disadvantages like:

  • Reading:
    • the execution order of the functions doesn’t match the reading order
    • the function name and the arguments are torn apart
  • Writing:
    • if you write an additional function around the existing expression which then fails, it is very laborious to manually delete all the code to go back to the previous state (especially, if you have trailing function arguments)
    • if you later recon that you need an intermediate step of the nested expression and need to split up the statement, the same problems occur

So instead of this code:

we could write it like so:

This code works in M if you have a record (“M”) in your queries that contains Kim Burgess’ cool code and an additional record (“M”) that he has kindly helped me with:

All that still folds!

Honestly, it doesn’t look pretty in my eyes (yet), but it works and eliminates the disadvantages mentioned above. With some help of Expression.Evaluate, we could further clean it up to match the magrittr-style for example, but I’ve been warned to use this function, so not sure what to prefer at the end.

Please let me know your thoughts & stay queryious 😉

File to download: ShouldWePipeM.zip

New M-function: Table.TransformColumnTypesToFirstRowsTypes for PowerBI and PowerQuery

The following function automatically transforms all columns to the types that have been detected in the cells of its first row. Provided they come as: Number, date or text (but you can add additional type conversions if you need them.).

It also has some rough edges: If the first value is empty, the column will be converted to text. Also, it contains the (improved) logic from this article:  So if a date is written in a way that it could also be a number, then it will be converted as a number. To minimize the room for errors here, I’ve converted the values to text first, but this is still something to watch out for. But in very many cases it will just do what you have long been looking for:

Use cases:

  • You don’t want to use the automatic but static/hard coded type-conversion in the 2nd step (because you know you’re table is going to have more columns in the future and you want to cater for proper type-conversion of them as well)
  • You’ve lost your column types due to some other command (like Table.ReplaceValues)

M-Code

Code to download: TableColumnTypesToFirstRowsTypes.txt

 

Enjoy & stay queryious 😉

How to expand a column that cannot be expanded in Power BI and Power Query in Excel

Especially when working with JSON-data, you might end up with a column that has elements of mixed types in it. The expand column – arrows will be missing, but some elements still need to be expanded, like here:

But there is an easy way to fix it:

Transform to expandable column

Table.TransformColumns(Source, {{“Column1”, each if Value.Is(_, type list) then _ else {_} }} )

It transforms the “Column1” from table “Source” by checking, if the content of the each row ( _ ) is of type list and if yes, keep that value ( _ ) and if not, transform it to a list (by framing it into curly brackets {_} )

Syntax for tables

Table.TransformColumns(Source, {{“Column1”, each if Value.Is(_, type table) then _ else #table({“Column1”}, {{_}} ) }} )

Syntax for records

Table.TransformColumns(Source, {{“Column1”, each if Value.Is(_, type record) then _ else [a=_] }} )

File for Subscribers to download: HTExpandColumnThatCannotBeExpanded.zip

This technique should be applied to columns where the expandable elements all have the same structure. If that’s not the case, you should use this technique instead.

Enjoy & stay queryious 😉

Table.TransformColumns – alternative in PowerBI and PowerQuery in Excel

This article shows a trick for a little problem that annoyed me for quite some time: How to get Table.TransformColumns transforming the values of a column with a reference to a value (from the same row) of another column?

1 Replace text by a value from another column

So instead of adding a new column where the “*” is replaced by the value from column “WhildcardValue”, I just want to perform the replacement-operation in the original “Text”-column, so that I don’t have to rename and delete the other columns later:

So far, I always ended up fighting with Table.TransformColumns-function and got quite frustrated because I couldn’t find a way to reference the (row-) value of the other column. As it turns out, I was fighting the wrong target here, because Table.ReplaceValue is actually the saviour for this challenge:

1.1 Check column “Text” -> Transform -> Replace Values

So in (4) we just filled in a dummy-value, which we’re going to replace with a reference to the desired column: “each [WildcardValue]” like this:

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