Using R to unzip and process files into Power BI

There are a lot of blogs and videos out there on how to use R to enhance the visualization layer of Power BI. In this small series, I want to focus on how R can be used to improve the data importing and data cleansing capabilities of Power BI. Previously, I wrote how you could use the R language in Power BI to export data out to a CSV file. You can view that blog by following this link.

Although, I personally am not an expert when it comes to R, I’m learning more each day, it’s one of the topics I enjoy teaching the most in our Advanced Power BI Boot Camp.

Now it’s time for the required Pirate Picture: ARRRRRRRR. By the way, this image is from a tabletop game I like playing called Merchants & Marauders!!

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Getting Started with R in Power BI

To work with R you will need to have R installed. I use Microsoft R open, link provided below. You can optionally download R studio as well. Both of these are free downloads.

Required: Download the Microsoft R Open 

Optional: Download R Studio

Discloser: At the time of this blog post, there is no built-in intellisense in Power BI to help write and debug R. Therefore, I use R Studio to write and test my code! R Studio is not a requirement, it is, in my experience, just very helpful.

UNZ and READ.CSV in R

The problem I want to solve in this blog post is how to unzip a file with Power BI and then process that file. Because I come from an enterprise BI background my first thought is to approach this problem with a tool like SSIS or PowerShell. However, there is an easier way, why else would we be here! Smile

I want to show you how you can use R to unzip and then extract data from a file all in one step. By the way, it’s really, really easy!

Let’s start off by looking at the code, this is all of it!:

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  1. On line one, we are declaring a variable called data, this variable will store the results of our expression.
  2. On line two we are using read.csv to extract the results from the csv file, Customer Sales, and these results are stored in the variable from step one, please note that this process can not occur until after the file has been unzipped.
  3. On lines three and four we are using UNZ to unzip the file Customer Sale.zip and then return the file Customer Sales.csv.

And that’s it!

Wait, wait, wait…. What is line seven? Well, line seven allows me to test the results in R Studio. This is not needed for the importing data into Power BI Desktop. Oh, that reminds me, we need to import the data into Power BI Desktop!

Importing data using R in Power BI Desktop

As you are already aware, there are a lot of data sources available in Power BI, it shouldn’t surprise you that one of those data sources is R. Now let’s walk through how we can quickly import data from an R script into Power BI Desktop.

From the home ribbon in Power BI, click Get Data –> Other –> R Script. See screenshot below:

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This will launch the R Script editor. As I previously mentioned, this script editor is simply a text box. Now we simply paste in our code from R studio, or if you happen to be an R aficionado you can just type your code in this box, as seen below:

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Once we click ok, the Navigator window will open. Since we only declared one variable in our script, only one option will be provided. From here, everything is exactly the same as any other data source.

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Ok, now we’re done! I hope you enjoyed this blog post and I look forward to writing more, until next time.

Using Composite model with Dynamic Security in Power BI

Like many others, I have not been an advocate of using Direct Query, it has long had many concerns diminishing its’ overall usefulness with one of the major key issues being performance. The Microsoft Power BI team has made major strides to mitigate the concerns around performance by adding two new key features: Composite Model and Aggregations. This blog will not discuss aggregations. However, we will briefly discuss the composite model and how we can use this to make Dynamic Security better.

The composite model simply allows users to import data and use Direct Query in the same data model. This has long been a point of contention for Power BI users because combining these two features into one model adds a lot of value. In this blog, I am going to briefly explain how this model helps to improve dynamic row level security.

I was sure I had previously blogged about dynamic row level security but I don’t see a post so I am going to re-direct you elsewhere. If you are interested in learning exactly how to implement dynamic security you can check it out here: Dynamic Security implementation

I am a huge advocate for Dynamic Security. Dynamic security allows you to create a single role in Power BI and then you can manage all your user security in a single table. Take a look at the following table which I am now storing in SQL Server:

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So why would the composite model make dynamic security better?

Great question. Historically, I would always import data into Power BI, including the dynamic security table. The problem with this method is what happens with the security of the data if the model is not being refreshed? For example, I may reassign which users have access to which sales territories in my security table but if data models in the service are not being refreshed then the security is not being refreshed and thus users will have access to data they should not have.

Now, with the composite model I can still import all my reporting data but then I can set up a direct query connection to my dynamic security table. This means that all of Power BI models using this table will always be up to date!

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Hopefully you can find many more useful scenarios for the composite model! Thanks for reading my blog!

Power BI Resources

This blog post is a quick post to outline some of the resources around Power BI that I have found most useful, I will update this list accordingly.

Power BI Learning Tour

Brand new to Power BI? If so, this Microsoft led training is a great place to start:

https://docs.microsoft.com/en-us/power-bi/guided-learning/

Custom Visuals – Series by Devin Knight

Devin Knight has, at the time of this blog, recorded 117 videos on his blog dedicated to demonstrating different custom visuals created by the community. If you are not already aware of this blog series, take a look at the link below!:

https://devinknightsql.com/category/power-bi-custom-visuals/

PowerBI.tips – Custom Theme Generator

My personal favorite from powerbi.tips is the custom theme generator. They also have pre-built layouts, tutorials and many other valuable resources.

https://powerbi.tips/

DAX Guide

The DAX Guide is a great place to browse and explore available DAX functions.

https://dax.guide/

Have something that just has to be on this list? Let me know!

HASONEVALUE vs ISFILTERED in DAX

I use the function HASONEVALUE quite often, many times it is in the context of solving problems with the total row. I wrote a blog on how to handle totals using HASONEVALUE here:

Unfortunately, HASONEVALUE is not always the tool for the job. For this scenario I am returning Homes Sold Month-to-Date. The final solution should look like the following screenshot:

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As you can see in the previous image we want to return the Homes Sold MTD if we are at the day level or the month level, but if we are at a level higher than the month level then we simply want to return a blank value. It doesn’t make sense in this scenario to show MTD homes sold at the quarter or year level. If we use HASONEVALUE(‘date’[Date]) here, then no value is returned at the Month level. Here is an example:

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As you can see in this example, a blank value is returned for the month of January. As previously mentioned the desired behavior is to return a value for the month of January.

IsFiltered

IsFiltered: Returns TRUE when columnName is being filtered directly. Else, False.

Let’s take a look at what IsFiltered returns if we are checking to see if the date is filtered:

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ISFILTERED returns False for the month and Year as expected. Now, we can simply chain multiple IsFiltered functions together to return our desired results. In the following screenshot I added an or condition to check if the month was also filtered. As you can see, the results now return TRUE at the day and month level.

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Now we can apply some conditional logic using the IF function based on the results of the IsFiltered expression in example above. If the day or month IsFiltered then return the MTD homes sold, else return BLANK(). See below:

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Thanks for checking out my blog!

DAX LASTDATE vs. LASTNONBLANK

In my previous blog post I discussed how the time intelligence function CLOSINGBALANCEMONTH worked great, except when it didn’t. If you remember, the problem was the function CLOSINGBALANCEMONTH could not handle situations where the data had gaps, or blanks on specific dates.

If you missed the previous blog post, you can find it here:

In this post,  I want to build a measure that returns the closing price for the date in the current context. My initial attempt at this measure is going to suffer from the same issue we faced when working with the CLOSINGBALANCEMONTH function in the last blog. Let’s start by using CALCULATE and LASTDATE.

Working with LASTDATE

Here is your MSDN definition of LASTDATE: “returns the last date in the current context for the specified column of dates”.

This function is great because it works in the current context so it makes the measures you author in DAX very dynamic. For example:

  • If the current context is month, LASTDATE returns the last day of the month.
  • If the current context is Quarter, LASTDATE returns the last day of the quarter.
  • If the current context is day, the day in the current context is returned.

This means that the LASTDATE function automatically works for each level in your date hierarchy, this is why we like working with DAX, because of this type of functionality.

Let’s take a look at a simple example of LASTDATE:

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This simple calculated measures returns the following:

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As you can see in this screenshot, LASTDATE is returning the last date of the current context, in this visual we have the month and the year.

Next, I will create a new measure that returns the Closing Price of the current time period. The following measure returns the Closing Price of the stock for the last day of the month:

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If we take a look at the results in our table visual, we will see that our measure is returning blanks for certain months. The reason this is occurring is because the stock market isn’t open every day of the year, therefore, if there is no closing price for the last day of the month then a blank value is returned.

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LASTNONBLANK function in DAX

I discussed the LASTNONBLANK function in my previous blog post, so I won’t get too detailed here but here is the definition and syntax from MSDN:

Definition: Returns the last value in the column, filtered by the current context, where the expression is not blank.

Syntax: LASTNONBLANK(<column>, <expression>)

LASTNONBLANK will return the last date in the current context that wasn’t blank, that is the perfect function for this scenario.

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Let’s take a look at the results of LASTNONBLANK compared to LASTDATE. In the highlighted sections below, notice that for each area where the close price is blank the results of the LASTDATE function and LASTNONBLANK function differ. As previously discussed, the stock market was closed on the last date of the month and therefore the close price does not return a value.

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Now it’s time to modify the Close Price measure so that it returns the last close price for the current context:

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Here are the final results:

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Thanks for reading, enjoy!

How to add Data Labels to maps in Power BI

Recently, I was asked if the values for maps could be shown on the map and of course the first thing I thought was to just turn on data labels. Well, if you’re reading this then you already know there isn’t currently a way to add data labels. Now, I say currently because the Power BI team is always making changes and it could one day be there.

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First, this was not my idea. When I realized there was not a data label option I knew someone in the community would have figured out a work around, and I was right!

Thanks to the awesome people who contribute at community.powerbi.com the answer was only a “google kungfu” search away. If you would like to see the original forum post where Sean “Super Contributor” helped out another Power BI user then check out the following URL:

https://community.powerbi.com/t5/Desktop/Data-Labels-on-Maps/td-p/79118

This blog is going to take a look at the following  three items:

  • Why this workaround works
  • How to set this up and configure it
  • Concerns and issues with this method

Why this workaround works

The map visual in Power BI will allow you to display the value of whatever is allocated to the Location field of the map. Generally you would simply put the country, state, city,  or some other valid location in this field. The limitation is that this field can only be populated with a calculated column. Therefore, Sean suggested combining the value you wanted to display into a calculated column field. Well, that allows you to now display the results, but it doesn’t map the data. The next step is you will need to have the latitude and longitude for each geographical location that needs to be mapped. The latitude and longitude coordinates will be added to the map visual to determine location and the location field will be used to display the data label.

Setup and configuration of Data Labels on Maps in Power BI!

Now, let’s dig in and build this out.

First, make sure your geography table has the required latitude and longitude columns. My geography table goes down to the zip code level of detail but I only brought in the Latitude and Longitude at the State level since I knew this is how I wanted to view the data in my map visual.

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Next, it’s time to build the calculated column that will help you display the data.

  • Create a new calculated column on the Geography table.
  • The following DAX expression returns Total Sales by State combined with the state name.
  • I also added the FORMAT function to add some formatting to the values.
  • The part highlighted in the red box returns the Total Sales by State.

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Now that the you have your latitude and longitudes and the calculated column has been built, it’s time to add this to a map! In the below screenshot I highlight where each of your fields should be assigned.

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Finally, it’s time to display our Location on the map!

  • Under formatting options turn on Category Labels
  • Feel free to change the properties for color, text size, show background and transparency.

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Concerns and Issues

First of all, you must store your values in a calculated column in order to display them.

  • This means that your values will be static, they won’t change as slicers change.
  • You need to know exactly what and how your end users will use the data, which is nearly impossible.
  • This column will take up resources in your data model.

Secondly, you must have the latitude and longitude for every location you want to put on your map.

  • It may be difficult to obtain a quality list of latitudes and longitudes for each address.
  • These additional columns in your data model will have a high level of uniqueness and will definitely consume valuable memory resources.

Enjoy!

Quick Tips–Updating Parameters from the PBI Service

In this quick tip I want to share how you can update Parameters in Power BI from the service. Previously, this was not an option and Parameters could only be updated from Power BI Desktop.

Updating Parameters in Power BI

To update parameters, navigate to the Datasets section in the Power BI Service. Next, click on the schedule refresh icon. Updating parameters is done from the schedule refresh window.

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Click on Parameters from the schedule refresh window. Once expanded you will see available parameters, simply update the parameters and you’re done!

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Thanks for reading!