Category: Power BI

Matt Allington takes us through a new feature in Power BI:

Now back to the point of this post.  As of June 2019, it is possible to pre-filter slicers as well.  It may seem weird, but this previously wasn’t possible – it seemed weird to me, anyway.  This is now fixed and it is possible to use the side filter pane in the same way as other visuals.  I can think of quite a few useful scenarios, including:
– Hiding the dreaded (Blank) in a slicer.  [Actually, please don’t do that, but instead fix your data model].
– Filtering out items not relevant (eg category managers may only want to see their own products)
– Hiding items with no sales

Read on for a few examples of how to use this.

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Teo Lachev shows us how you can unpivot multiple columns in Excel using Power Query:

The user wants to unpivot the data by rotating the three header rows (Scenario Type, Month, and Year) from columns to rows. The issue is that the headers span three rows. If you just select these columns and unpivot, you’ll end up with a mess. And Power Query operates on row at the time so you can’t reference previous rows, such as to concatenate Scenario, Month, and Year. We can do the concatenation in Excel so we have one row with column headers, such as Actuals-Jan-2018, Actuals-Feb-2018, and so on, which we can easily unpivot in Power Query. But if we can’t or don’t want to modify the Excel file, such as to avoid the same steps every time a new file comes in?

Click through for a sample file which shows how you can do this.

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David Eldersveld builds out a Power BI accordion filter:

The Power BI custom accordion relies on Bookmarks and Buttons as key elements. I’ve only created two categories in my accordion. I’ll be honest–it’s probably more work than it’s worth to keep track of different buttons due to positions as well as what’s visible or hidden for each bookmark. The thought of expanding to three categories is a bit daunting. Why is that?

Read on to see why (hint: combinatorial explosion).

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Rob Collie explains how to calculate Value Above Replacement:

Let’s say you’re trying to improve the fuel efficiency of your car, and you have a budget of $100 to make those improvements.  Furthermore, let’s say that you can only spend your budget on three different kinds of improvements:  better tires, better spark plugs, and a better exhaust system.

The simple answer, of course, is to buy the Best of each part – the best Tires, the best Sparkplugs, and the best Exhaust System.

But what if Reality rears its head and rudely insists that the Best Costs More?  What if there are a range of options available in each bucket, with varying price AND effectiveness?  Then what?

Read on for the solution which has not nearly enough baseball.

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Marc Lelijveld shows how you can embed Power BI Dataflow refresh times in your Power BI reports:

But maybe you want to visualize this as part of your report as well. With a really simple piece of Power Query code you can easily generate a date/time at the moment that your dataset is processed. Kasper de Jonge wrote a blog post on that, so I’m not going to elaborate on that. However, when we add this as a separate entity to each dataflow, it results in a last successful refresh date/time for each dataflows.

Since each dataflow will be refreshed on it’s own, likewise as a dataset, the entity with your last date/time will always the last date/time for the whole dataflow, no matter how many entities are in there.

Read on to see how to combine and display these refresh times.

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Melissa Coates explains why you should create reports in Power BI Desktop rather than directly through the Power BI Service:

I always recommend to Power BI authors that report creation & editing should happen in Power BI Desktop and to just ignore the edit capability in the Power BI Service. Usually my reasons are concerned with (a) which version is the latest version and (b) having versioning history, and (c) reducing the risk of someone overwriting someone else’s work because two different reporting editing/publishing options are in use. These are very valid concerns, but I’ve discovered one additional problem that could easily confuse some people: (d) the side effects experienced if you download then re-upload a web-created report. Let me explain…

Read on for that explanation.

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Alberto Ferrari shows how you can replace bidirectional filters with visual filters in Power BI and improve visual quality:

The noticeable thing about the behavior of the slicer is that the two matrices are showing only the brands and colors purchased by Amanda. Yet, the Color slicer is still showing all the colors, even though we know Amanda only purchased three colors: Grey, Silver and White.

The reason is that the matrices, like most Power BI visuals, hide rows if the measure they are showing produces a blank. Because Amanda did not buy any pink product, the value of Sales Amount for Pink results in a blank, therefore the matrix removes the pink color from its result. Prior to the May 2019 release of Power BI, slicers did not display this behavior because slicers did not have a measure to evaluate – they would only show a list of values from a column; Moreover, visual-level filters were not allowed in slicer visuals whereas they were available in other visuals such as charts, tables, and matrices.

Read on to see how to do this.

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Matt Allington shows how you can pivot text data from an Excel spreadsheet using Power Query:

It is very common to need to transform data from one “shape” to another “shape” before it can be used inside Power BI for analysis (although many beginners don’t realise this).   One such example is shown below, where the data in the table on the left hand side needs to be transformed into the table on the right hand side.  As you can see on the left, column A contains the attribute and column B contains the value of the attribute.  Every 4 lines of data is 1 record.  This specific problem is very common problem when your only source of data is from an extract (eg csv) from some other system, particularly older systems where you can’t change the format of the data extract.

This is a clever solution.

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