Category: Power BI

Philip Seamark explains how the RANKX function works:

One of the first traps to encounter when using this function is the function can be used in calculations for calculated columns as well as calculated measures.   The RANKX function will still do what it is asked.  The trick is how you use the function in each scenario – and more importantly, what filters are going to be implicitly applied to the data the RANKX function actually uses.

This is a helpful post for explaining the ranking function.

Jason Thomas shows a nice method to combine data for the last N months along with a month-by-month breakdown using a single date dimension:

Create 3 measures as shown below, and then add those 3 measures in the report along with a month slicer as shown below. You can change the month in the slicer and verify that the measure values change for the selected month.

Sales (Selected Month) = SUM ( Sales[Sales] )

Sales Last Year = CALCULATE ( SUM ( Sales[Sales] ), SAMEPERIODLASTYEAR ( ‘Date'[Date] ) )

Sales YTD = TOTALYTD ( SUM ( Sales[Sales] ), ‘Date'[Date] )

This is the first time I’ve seen a What If parameter in use.  Very interesting.

Stacia Varga continues her Power BI + hockey series:

My last data acquisition step is to get statistics data for each player. I just need to build a function to dynamically get data by team like I did above using this endpoint as my base structure:

http://statsapi.web.nhl.com/api/v1/teams/54?hydrate=roster(person(stats(splits=yearByYear)))

It turns out there are many different kinds of statistics that I can get in addition to these statistics by season. I’ll probably get them all added into my model eventually, but the process is the same. For a list of other available statistics to use instead of yearByYear, see http://statsapi.web.nhl.com/api/v1/statTypes.

It’s another nice use of Power BI to read from a web-based API.

Stacia Varga shows off some of Power BI’s filtering and data processing capabilities by looking at hockey stats:

Right now, the data is not ideal for analysis. Keeping in mind how I want to use the data, I need to perform some cleansing and transformation tasks. Any time I work with a new data source, I look to see if I need to do any of the following:

• Remove unneeded rows or columns. Power BI stores all my data in memory when I have the PBIX file open. For optimal performance when it comes time to calculate something in a report and to minimize the overhead required for my reports, I need to get rid of anything I don’t need.

• Expand lists or records. Whether I need to perform this step depends on my data source. I’ve noticed it more commonly in JSON data sources whenever there are multiple levels of nesting.

• Rename columns. I prefer column names to be as short, sweet, and user friendly as possible. Short and sweet because the length of the name affects the width of the column in a report, and it drives me crazy when the name is ten miles long, but the value is an inch long—relatively speaking. User friendly is important because a report is pretty much useless if no one understands what a column value represents without consulting a data dictionary.

• Rearrange columns. This step is mostly for me to look at things logically in the query editor. When the model is built, the fields in the model are listed alphabetically.

• Set data types. The model uses data types to determine how to display data or how to use the data in calculations. Therefore, it’s important to get the data types set correctly in the Query Editor.

It’s a fun topic to use for learning about Power BI…says the guy wearing a Blue Jackets shirt right now…

Philip Seamark shows how to visualize the relationships between tables using Power BI:

The network navigator was another good visual, and if you have an R instance installed on your local machine, you can play with some of the custom R visuals.

The catalog views could be used in a similar way to generate power bi visuals showing other object dependencies inside an MS SQL Database.  Additional columns could be added to the base query to be used in tool-tips etc.

If your database tables do not have foreign keys, here is another query that can be used to help guess relationships between tables based on column name and datatype.

Most of these techniques look like they wouldn’t work well on databases with a very large number of tables, but for an average-sized database, it can serve as an avant-garde ERD.

Imke Feldmann shows how to use Power BI to scrape multiple tables from a webpage:

I will present 2 methods here:

1. Append-method: This is the obvious one and is fast for just a few tables.
2. Add-Column-method: A bit more complicated but will be faster for a large number of tables and is also suitable for a dynamic number of tables.

You will also find 2 options at the end of this article:

1. Use custom functions for multi-step table transformations

2. Use dynamic filters to select the desired tables

Read the whole thing.

Eugene Meidinger covers the various deployment options for Power BI:

Even worse, Power BI is rapidly being iterated on. This is great for users, but a challenge for people trying to keep up with the technology. One year ago the following deployment options modes didn’t exist.

1. Sharing individual reports (Jan 2018)
2. “Apps” (May 2017)
3. SharePoint Embedding (Feb 2017)
4. Power BI Premium (May 2017)
5. Power BI Report Server (June 2017)
6. Power BI Embedded V2 (May 2017)

It can be a real challenge to keep up. I think that a lot of the dust has settled when it comes to deployment options. I don’t see them adding a lot of new methods. But I expect there to be many small tweaks as time goes on. In fact I had to make two changes to my slides this morning because they announced changes yesterday!

In contrast, I expect another six to be added to this list in the coming three months.  Because it’s Power BI and the only rule behind Power BI is that there must be more.

Matt Allington tries to solve the converse of an easy problem:

My idea was that I would load school photos and also the reunion photos onto the one page.  The user can then click on a slicer with someone’s name (or any other information about people) and “see” those people highlighted in the photo.  I started thinking that I could use the excellent Synoptic Panel from The Italians for this.  The only problem I could foresee was that Synoptic Panel is designed to provide shading over an image based on what was selected.  I wanted to shade/hide those people that were NOT selected.  Anyhow, I love a challenge.

Read on for Matt’s solution.