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Appending Data Sources Manually in PowerBI

Last week I looked at how I can load into PowerBI an entire library of source data files and let PowerBI determine how to append the resulting files together. This method works great if all the data source files are of the same time and have the same schema/structure. But what if some of the files come from an Access data source, some from CSV files, some from Excel, etc. Furthermore, what if the data column order is different or perhaps if the column headers are different. These are cases in which you may need to import each data source individually. Then edit the query for each source to align the column, column names, data types, etc. before combining the data sets together. Let’s see how that looks.

For simplicity sake, I am going to start from the same folder as last week that holds 10 CSV files each representing a different sales region.

Although I am not going to show images from each data load, I would proceed to load each CSV file into my PowerBI data model one file at a time rather than loading the folder like last time. This method creates a separate table within PowerBI for each data set as shown in the following figure.

I can view the data in each table by selecting the table name in the Fields panel as shown above. The first thing I would want to do is to ‘fix’ any inconsistencies in the data schema in these tables by selecting the table and then clicking on the Edit Queries button in the External Data group of the Data Tools Modeling ribbon. I basically want to build a common table structure across all of the data sources including a consistent column name for each column that appears in multiple tables.

You should note that if I have a data source with a unique column that does not appear in the other tables, I can keep it. Later when I append the individual tables into a single table, the unique column will be brought into the final data model, but the field will be blank for all the other tables that do not have that column.

In my first example here, all of my data schemas from each data source are the same but they would not have to be. So after simply loading the data from each data source, I am ready to start combining the data into a single table for analysis. To do this, I click the Edit Queries button in the External Data group of the Home ribbon and then select one table to start the process.

There is a button on the far right of the Home tab while in Edit Query mode in the Combine group called Append Queries. I can use this button to begin the process of combining the tables.

The Append Queries button opens the dialog shown below that lets me select which table to append. I can only append one table at a time so to append together all 10 tables, I need to do this step 9 times.

As shown in the figure below, each appended query gets the generic name of: Appended Query followed by a number (after the first one which has no number).

This default name is not descriptive enough to help me identify which appended query refers to which data set. If I want to remove one of the data sets from the final table, I would have to click the settings button on the far right of the applied step (the gear icon) to reopen the Append dialog to see which table is being appended. Then if I want to remove that dataset, I could click the ‘X’ to the left of the applied step to delete that one step.

However, a better option is to right click on the default applied step name and select Rename from the popup menu that appears.

This option allows me to select the current applied step name and replace it with a more meaningful name.

The following figure shows a much clearer picture of which data set is being referenced in each step. It also shows that I have finished appending my 9 additional data sets.

Note that any steps applied to the individual data sets are still applied first prior to the data being appended to the final dataset.

Note: One thing that I did not do here but probably should have is to begin by making a duplicate copy of the table that I wanted to begin with so that I could preserve the original table with only its own custom transforms. Then using the duplicated table, I could append the rest of the data sets.

When all the data is appended into a single dataset, I can close and apply this transformation so that any data refreshes can repeat these steps.

I can now go to my Report tab and start to build the visualizations that I want. In the final figure for this session shown below, I create a table of Sales Amount by Sales Territory and included the Sales Territory Group, Region, and Country. I then click on the Sales Amount column to sort the table by this column in ascending order. You can see very quickly that most of the sales occur in Australia and the least sales are in the Central Region of the United States.

Beneath the table, I create another table with the Sales Territory Group and the Sales Amount. PowerBI automatically sums the sales for each Territory Group and displays a chart with only three segments. After creating the table, I change the visualization to the Donut chart to create the appearance shown below.

As with all PowerBI visualizations, the data in the table is linked to the data in the donut chart. If I click on the North America segment of the donut, the table on top refreshes to display only the 6 rows representing sales in North America. Similarly, if I click on the Pacific segment of the donut chart, the table above immediately updates and displays only the one line for Australia.

That’s it for this week. Come back next week for more PowerBI fun.

C’ya.

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Loading and Combining Multiple CSV Files in Power BI

Suppose my job is to collect sales information for my company and I current receive text files of that data from each of our major sales offices around the world. Before I can do any analysis using Power BI, I will need to both load the data from each sales office and then combine the data into a single file. For today, let me assume that the format of the data from each sales office is exactly the same and the order of the fields is also exactly the same. As each sales office sends a copy of their data to me, I store their CSV file in a common folder called CSV_Sales as shown below, eventually getting data from all ten locations.

I am now ready to open Power BI and load my data to begin my analysis. I start by selecting the Get Data option after opening Power BI. This displays the following dialog which lets me specify the type of data I want to load. In the past, I showed several different ways to access individual files from different sources including CSV files. Indeed, I could again load each of the CSV files separately and then ‘somehow’ combine them into a single table for analysis purposes.

However, I notice an option that I had not selected before, Folder.

When I click the folder option, Power BI prompts me for the URL of the folder. If I am not sure of the path, I can click the Browse button and navigate to the folder and Power BI will figure out the path for me. Either way, I click on the OK button to continue.

Power BI then shows me the contents of the folder. For each file in the folder it provides metadata about the file such as its name, extension, date last accessed, date last modified and more. There is also a column at the far left of the grid named Content which in all cases has the word ‘Binary’ in it. This mysterious field in each row actually represents the data in the file. In fact, it is in most cases the only field that I care about.

If I click the Load button, highlighted the previous figure, Power BI loads the folder information into a table as shown in this figure. This is not what I want.

So instead, I click the Edit button on the previous screen which loads the data directly into the Query editor. (Yes, I could just click the Edit Queries button in the Home ribbon to get to the same place, but why go through two steps when one will do.)

However, as I said previously, I don’t need all these other columns that provide information about the data files. I only care about the data inside the files. Therefore, I select the first column, the Content column and from the menu select the submenu under Remove Columns and click the option to Remove Other Columns. This is a faster way of getting rid of columns I do not want rather than selecting each column and then clicking the Remove Columns option.

Once I have only the Content column, I can focus on the button on the right side of the column header. Notice that it is a little different than the buttons on the right side of the other columns. Instead of just a single arrowhead pointing down, this button has two arrows pointing down to a line. This button means that I want to download the actual data from within the binary files into a separate table. Therefore I want to click on it.

As you can see in the following figure, I now have all of the columns from the sales table. Because Power BI only presents a preview of the data in this mode, it is not clear whether this file just contains data from the first table or whether it contains data from all of the data tables in the selected folder.

Because I am still in Edit Query mode, I need to close and apply my transformations to the folder by clicking the Close & Apply button in the Home ribbon.

Now when I return to the Data page of the Power BI Desktop, I can see my data and in the Fields dialog on the right side, I can open the CSV_Sales table definition to see all the fields in the table. But I still don’t truly know if I have the data from all the CSV files or not.

Next I open the Report page and create a simple table that displays several of the columns from the CSV_Sales table. I select the fields: SalesTerritoryGroup, SalesTerritoryCountry, SalesTerritoryRegion, and SalesAmount. I can quickly see now that the table does indeed include all my regional data from all 10 sales regions.

Just for fun, I can build a second table that includes only the fields SalesTerritoryRegion and SalesAmount and then convert the table into a TreeMap chart by clicking the TreeMap visualization. This visualization shows the contribution by sales territory region to the total sales by creating proportional rectangles within a larger rectangle that represents total sales. If I hover over any of the boxes, a popup displays the name of the sales region along with the sales amount for that region.

But wait a minute, I previously said that there were 10 regions, but I only see 7 colored rectangles representing only seven of the regions. What happened to the other three? Well, they are actually there, but they are so small compared to the total sales that they are nothing more than slivers at this scale. In fact, even as I expanded the size of the chart. It was difficult to see these last three sales regions which even added together represent less than 0.1% of the total sales. However, they do exist along the right edge of the chart as you can see as I zoom into the bottom right corner.

Well, I hope you found that interesting. I’ll look at some more Power BI goodies next time.

C’ya.

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Power BI Knows Dates

Back in PowerPivot days, if I wanted to be able to drill down through a report by dates (Year, Quarter, Month, Day), I had to build the hierarchy and typically I had to build custom columns to hold these date parts. I would even have to do some fancy ‘Sort by Column’ changes to get the months of the year to display in the correct order. Power BI takes a lot of that work away and makes it easy to drill down through your data.

Let me start by loading some data from the Adventure Works Data Warehouse database. In the figure below, you can see that I’ve loaded several of the tables from this database, but for today I’m going to focus on just two tables, FactInternetSales and DimDate. The first thing I notice is that these two tables are connected with three relations represented by three lines, one solid and two dotted. This is because DimDate is a role playing dimension since there are multiple dates in the FactInternetSales table that could be associated with the date table to create reports.

By clicking on any of the relationships, I can see which fields are associated with that relationship since the field names will be enclosed in a box. In the figure below, you can see that the active relationship (the solid line) connects the DateKey field in DimDate with the DueDateKey in the FactInternetSales table.

However, I want to use one of the other relationships. To change which relationship is active, I can open the Data Tools Modeling toolbar and select the Manage Relationships button.

You can see that 6 active relationships have been set between the tables and two are currently inactive.

Suppose I want to use the OrderDateKey for my report. It would be nice if I could just click on the relationship that I want and like radio buttons (or option buttons) the previous relationship would be marked inactive and the new relationship would become active. Unfortunately, this just results in an error screen as shown here.

You have to first mark the relationship that you do not want as inactive by clicking on the yellow checkbox to the left of the relationship. Then you can select the relationship you want as shown in the next figure.

Now when I return to my Relationships page, the active connection is now linking the DateKey field in DimDate with the OrderDateKey field in the FactInternetSales table

I did this just to show that I can still work with the tables like I did in PowerPivot, but I really did not need to do any of this. If I open the Reports page and in the right side fields panel select first Sales Amount and then Order Date which is defined as the data type Date/Time, I will get a table that looks something like the following:

If I then switch to a stacked column chart, I will see a chart like the following (after a few formatting changes) that displays the Sales Amount by Year.

If I click on any of the columns, a dialog box appears with some details about that column. However, that is not what I want. I want to be able to drill down on any given year when I click on it.

In a relatively recent enhancement to PowerBI Desktop, several buttons have been added to the header. If I click on the icon third from the right as shown in the next figure, I can toggle the Drill Down feature. Drill down is turned on when the icon is mostly black instead of white.

Now when I click on the third column from the left (the 3rd quarter) the chart automatically updates to display sales by month and it displays the months July, August, and September in the correct order as shown next.

Clicking on the August column of this chart drills down to the next date level which is days in the month as shown here.

At any time, I can move back up the hierarchy by clicking the icon on the far left side of the header which is the Drill Up icon.

In fact, if I click on this icon twice, I can return to the Sales by Quarter chart. Now suppose I want a chart that displays all the months in the year. The button second from the left lets me drill down for all the current column on the current chart to the next level. So if I am displaying quarters, it show me the sales by month.

As you can see in the following chart, all the months in the year now appear. But perhaps more surprising is that the months appear in the correct chronological order, something we would have had to do with a Sort by Column option in a standard PowerPivot chart.

I hope you found that ability to drill down through a date hierarchy automatically without having to build that hierarchy first as interesting as I did. I’m at a SQL Saturday today in Tampa presenting an introduction to Power BI to the attendees. Maybe I’ll see you there.

C’ya next time.

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Using Power BI On-line

During the last several months, I’ve taken you through an initial exploration of Power BI Desktop, a program that you can download directly from Microsoft. However, there is another version of Power BI, one that resides in the cloud. Many of its features are similar to Power BI Desktop, but there are also significant differences. Let’s begin the new year by taking a look.

First you have to sign up to use Power BI. Click on the following link to go to the self-service signup page:

https://powerbi.microsoft.com/en-us/documentation/powerbi-service-self-service-signup-for-power-bi/

You should see a link on the page that says: self-service signup process which takes you to the link:

https://powerbi.microsoft.com/en-us/

The screen (shown below) tells you that you can sign up and get started with Power BI for free. That is almost true. There are some notable exceptions. For example, you cannot sign up with an email address provided by consumer email services or telecommunication providers, this includes the fact that you cannot sign up with a Microsoft Hotmail account. Also .gov and .mil addresses are not allowed at this time. I guess that surprised me a bit because government and military intelligence is something that we are in great need of. However, I guess if you want to call it BI, then strictly speaking it is business intelligence only. J

Another reason you may not be able to signup is because your IT department has disabled self-service signup for Power BI even though your organization may have the Office 365 SKU that supports Power BI. Again Microsoft provides instructions for your administrator on how to fix that problem should they so decide to let you use Power BI. Again, I can only assume you must work for an organization that has no interest in business intelligence if they tell you no.

Of course, Microsoft does provide a way for you to personally register for a new Office 365 trial subscription and use that ID to sign up to use Power BI.

You will get an email from PowerBI/Microsoft asking you to confirm your email address.

After you have successfully navigated all the steps to get a Power BI account, you should see an initial power BI screen like the following:

If it is not already there, you can downloaded the financial sample file from: http://go.microsoft.com/fwlink/?linkID=619356. I just don’t remember how I got it originally. To install the data after downloading it, I used the Get Data button at the bottom of the left navigation panel

I can then click the Get button within the Files box to load the CSV file I just downloaded.

This will provide me with a flat file dataset to work with as a sample. The next figure shows what appears to be an entry for both the Database group and the Dashboard group with one entry in each named Financial Sample.

By opening the Dataset Financial Sample, I can choose which fields I want to work with from the Fields list.

I can simply click the check box to the left of the field name or I can click on a field name and drag it over to the working area. For this demo, I will use Sales and Month Name.

While I do have the ability to customize some features such as the formatting of the title text and even to highlight one of the columns as shown below, I quickly realize that starting from a raw data file like this in Power BI, I can not define a custom order for the Month dimension to force the months to appear in chronological order.

Rather than being in chronological order, it appears that the month columns are ordered from the largest to the smallest in sales value. While possible, this is typically not what I would want to display.

I’m going to leave that problem as it is for now and attempt to click on the Dashboard entry for Financial Sample. I am told that I have a report with unsaved changes. When did I work on a report since I never clicked inside the Reports group? Well, it appears that when I define visualizations in the dataset area, I am actually creating a report and must save it if I want to keep it.

When I click Save, Power BI prompts for a report name. For now, I am just going to call it: TestReport.

With the report saved, I now can see the Financial Sample default dashboard.

The first thing I notice at the top of the page is a text box which is asking me to enter a question about the data. This is something new that we have not seen in the Power BI Desktop. The interesting thing is, I can enter a question (or even a statement since the syntax of a question is not as important as the words used in the sentence) that references one or more of the data fields in my dataset. In this case, I can ask a question like: What are the total sales? (Interestingly, I can just type: Total sales and get the same result.) As I type enough of the question for Power BI to guess at what I want, it starts displaying results

I can continue to drill down to get sales by product by just adding the words by product to my question/statement from the above example. Note that Power BI automatically decides to switch from a simple text answer for a single value to a bar chart with the bars representing my product dimension.

I can just as easily request to see total sales by month. Note in this image, the months are displayed in the correct chronological order even though the report I created earlier did not show them in that order by default.

I can even display sales by country as shown below.

By simply changing the visualization, however, I can create a map of sales by country which might be a better way to present geographic data to management.

So I’m going to stop here in this week’s tour of Power BI. I’ve discussed some of the issues you may need to deal with to get access to Power BI and have shown how to load and use a simple flat data file in the form of a CSV file. In future weeks, I will explore more of the features of Power BI and especially how you can use Power BI Desktop or even Excel to create the datasets you need to display in Power BI.

On last point that I will cover in more detail at a later time, but which you might want to be aware of at least for now. One of the advantages of using Power BI to display your data analysis is that you can save your dashboards and make them available to others within your organization as a simple download. Yes, with Power BI Desktop you can save your model and copy the file from your machine to others. However, if you make a change, you must redistribute the changed file each time. Also refreshing the data is a manual process at this time when using Power BI Desktop. We will see in future blogs how these issues are addressed by Power BI to make your life simpler.

C’ya next time I’ll take our Contoso data and show how easy it is to use it from a One Drive file.

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An Aster Plot for the Holidays

Yesterday was Christmas and for those who celebrate Christmas, I hope you had a very happy day. However, I just wanted to ask this. Why do we wait for Christmas to be nice to one another, to wish our fellow man peace on earth and goodwill to all men? And do we really mean it or do we just go back to our old ways the day after. Do we need a holiday to be nice to each other? Why cannot the Christmas spirit spread throughout the year? And why does it have to be tied to a religious holiday. Do we need a religious holiday to tell us when to be nice to others?

On a similar note, New Year’s resolutions are coming up in another week. Could we all possibly resolve to act a little more like Christmas every day of the year? Or is that just as hard as resolving to lose weight?

Anyway, since many of you are on vacation anyway this week, I’m going to do just a short blog on the Aster Plot visualization for Power BI. This visualization, like many of the others I’ve talked about can be found on Microsoft’s Power BI Visualization Gallery page. When you click on the Aster Plot tile, the following diagram displays telling you a little about the visualization which as they state in the description is related to a donut chart. However, unlike a donut chart which defines the size of the donut segment on the value associated with that segment much like a pie chart, an aster plot uses one dimension to define the segments and each segment is equal in the amount of the arc it uses. The value associated with the segment defines the radius of the segment instead. Let’s see how this might work with some of the sales data by product category from Contoso.

I’m not going to go through all the dialogs that appear when you download a new visualization. I’ve done that before. So if you are joining in for the first time, check back through some of my previous blogs for details on that.

After the visualization is downloaded, I can load it into my Visualizations toolset/panel by clicking the ellipsis (3 dots) at the bottom of the visualizations panel.

Power BI will display a warning to caution you about importing custom visualization, so you should always do this on a test machine first to make sure they do not cause any issues. Just because it is on a Microsoft page does not mean that it is safe to use and does not violate any privacy concerns.

Next, I loaded my Contoso data and displayed the Sales Amount by Product Category as shown in the table below. (Again, if you do not know how to do this is Power BI, I’ve covered this several times in the past several months.) I should mention that I specifically sorted the sales data in descending order for this visualization.

Then with my table selected, I can click on my new visualization, the Aster Plot which displays the chart shown below.

Notice that if I hover over any of the segments, a box appears that tells me the product category name along with the corresponding sales amount for that category. One of the things that I don’t particularly care for is that the name of the dimension appears in the center of the chart and apparently behind the arcs so some that some of the name is covered by the chart. I wish I could move the dimension name either to the top of the bottom of the chart at the very least. Of course I could customize the name to make it smaller or to add spaces between the words back on the data page, but for this demonstration, I didn’t feel the need.

You can have other charts on the same page like the one shown here that displays the Sales Amounts by calendar year for Contoso.

Like other visualizations, I can click on any of the Aster plot segments to select that category and the column chart to the right will automatically update to highlight the annual sale for the selected category while still showing in a lighter shade of the column color to total annual sales.

Similarly, I can select a year and the Aster plot will be redrawn to display the relative sizes of each of the categories as shown below.

Well, that is all I’m going to cover for this week because it is time to get back to end-of-year celebrations, visiting relatives, after-Christmas sales, and all those things.

C’ya next year.

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Bubbles – A Power BI Visualization

Bubbles – A Power BI Visualization

This week I am going to take another look at a custom visualization. This one is called Bubbles and is in some ways similar to last week’s look at the Word Cloud visualization. At least it looks similar in that the bubbles can appear as different colors, they are grouped together like the words were in a random pattern, and the size of the bubble, like the size of the text in the Word Cloud is a function of another numeric parameter. This time rather than the frequency of the word as in the Word Cloud, I can select any other numeric measure to define the relative size. In this case, I’ll use sales of the items within a product category and let each bubble represent one of the categories.

I’ll start by going to the Power BI Visualizations page found at: https://app.powerbi.com/visuals and click on the Bubbles tile. This displays the following dialog which tells me a little about the visualization including giving credit to its creator. Since I want to use this visualization, I next click on the Download Visual button.

While I will not show it here, there is an intermediate dialog that display the licensing terms for most visualizations. If you accept the license terms and proceed, your browser should download the visualization to one of your local folders. For me that is the Downloads folder. After it finishes downloading, I will move it to a folder where I keep other Power BI Visualization files.

Next I can open my desktop version of Power BI. In the background, I will load sales and product data from my Contoso sample dataset that I pften use and then switch to the Report page. On this page, I can click on the three ellipses at the bottom of the Visualizations group which is the Import from File button.

I will be shown a dialog which lets me locate the visualization file that I downloaded. Before loading the file, Power BI warns me about the perils of importing custom visualizations because they could contain code that could either circumvent the security on my computer or even access private information in my files. The fact that these files are hosted on a Microsoft site is not a guarantee of safety. However, they are more likely to be safe than other visualizations I may find elsewhere on the Internet.

After I start the import, I receive one more dialog when the process is complete.

The Bubble Visualization tile now appears in the bottom row of the Visualizations section. I’ve been asked whether I can rearrange the visualization tiles, perhaps alphabetically, by type or by some other factor. At this time, I have not discovered a way to do that so if someone has, please let me know. Of course the next update of Power BI may include that functionality so who knows.

Next I drag the fields I want to use into a blank area of the Report page. In this case, I want both the Sales Amount field and the Product Category Name. Initially, these data elements may display as a table or column chart (depending on which field you dragged into the report first). In either case, I can then click on the Bubble visualization and the character (text) field will be used to define the bubbles and the numeric field will be used to define the relative sizes of each bubble. The figure below the column chart version of the initial data added to the report page.

As I have shown before, to change the visualization, you only need to click on the tile in the Visualizations panel to change the currently selected visualization to another. Therefore, clicking on the new Bubble visualization results in the following:

If you hover over any of the bubbles with your mouse, Power BI shows the product category name value and the sales value for that category. Interestingly, because it was not what I expected, hovering anywhere outside the bubble but in the overall background (light grey here) displays the name and value of the largest bubble, not the total sales which is what I expected.

Like many other visualizations, you can click on any of the bubbles and that automatically filters any other visualizations on the page by the selected product category. I could also add a second table on the report page such as Calendar Year from the dimDate table and then define it as a Slicer. Then selecting different years in the slicer changes the Bubble visualization to represent data only for the year(s) selected.

The Bubble visualization has a few formatting options which I can get to by clicking on the pen (pencil) in the Visualization panel. I probably would like to see more options to control the colors of the bubbles which sometimes appear all the same color and sometimes as a few different colors. Perhaps if you too would like to see more control over the formatting of this or any other visualization, you should remember that the opening dialog from the Power BI Visualization download page provides a link to the author where you can send your thoughts and suggestions.

That’s it for this week. Next weekend is Christmas and then the week after is New Years. I will try to find time from celebrating to cover two other visualizations before getting back to some hard core data analysis next year.

C’ya next time.

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Using a Word Cloud to Analyze Responses

Have you ever had to analyze the results of a survey? If your survey consists of multiple choice, true/false or even Likert Scale type questions, the results are fairly easy to tabulate. All you need to do is to count the number of each unique response for each question and then display the data using a table, column/bar chart, or even a pie chart (please only if there are less than or equal to 6 choices).

But what if you have a question like: ‘Describe with a single word your boss’ and you allow them to enter any word truncating the response at the first blank. I suppose you could count the number of times each word is used and then display the results in a table or a column/bar chart. However, because there are potentially dozens of words that could be used, a column/bar chart could get crowded pretty fast.

Perhaps even more difficult to analyze is asking the responder to describe what they find important in a web site especially when you give then a free-form multi-line text field. Now you have to deal with entire sentences like: ‘The most important thing to me is a strong search engine to make it easier to find the information I want.’ How do you parse that type of response into meaningful data? That is what I am going to look at this week.

To begin, I need to capture the survey data in a format that I can import into Power BI. Generally I will have two choices for this, a simple text file of the column(s) from the survey that I want to analyze or perhaps the survey software (like SharePoint) can export the data to an Excel spreadsheet. In either case, I can then import the data into Power BI.

In terms of how I might want to visualize the data in Power BI, column and bar charts are not an option as I mentioned before because there are simply too many words and thus columns to display anything meaningful. However, a common visualization for just this purpose is something called a Word Cloud. Word clouds have been around for some time especially in blog sites to help identify the most common words used in the blog. Fortunately, Power BI has such a visualization in their Visuals Gallery site which can be found at: https://app.powerbi.com/visuals. This site contains visualizations that have been created and submitted by the Power BI community. There are instructions on the site on how to submit custom visualization that you create using the Power BI Developer tool. The Microsoft team will then review the visualization and if it appears stable and relevant, they will publish it to the site. Anyway, there is a visualization on this site called: WordCloud.

To use a visualization from this site, click on it. This displays a dialog as shown in the following figure which includes some information about the visualization, who created it, the license information, support, etc. However note the Download Visualization button

Clicking the download button may include an agreement to the terms of usage of the visualization and will then download a file with the extension .pbiviz. I recommend creating a separate folder on your machine where you can download this and other visualizations from this site. Next I find the box in my Power BI visualizations Report page and locate the button with the 3 dots (ellipsis).

Clicking on this button first displays a warning about importing custom visualizations as shown below.

Next I will use a standard Open dialog box to locate the file that I just downloaded.

Now with my WordCloud visualization available and my data loaded into Power BI, I could simply drag the column that contains my text onto the blank Reports page (or a blank area on the page if I had other visualizations already started, and then with the data selected, choose the WordCloud visualization.

Initially, the display is cluttered with common words like: the, and, an, a, is, had, have, other, my, I, and many other words that we use in everyday conversation. This noise detracts from the real information we want to gather from the word cloud. So we need to find a way to eliminate (or minimize) the occurrence of these words.

How can we do this? The brute force method (and for now the only method I know) is to return to the Data page, select the Edit Queries button on the Home tab and then use the replace values option in the Any Column group of the Transform ribbon to define values to replace.

Now is where I have to be a bit careful. If I simply try to replace “an” with the empty string: “”, I could alter other words like “change” to “chge” or “analysis” to “alysis”. Neither of these words would make sense. Therefore, I decided to always add a blank as a prefix and a suffix to whatever word I wanted to find and eliminate and replace it with a single blank. (Because if I don’t, a phrase like: “search for an employee phone number” would become “search foremployee phone number”.)

The problem is that if the words I am trying to eliminate begin a line or are followed by a period or comma, my replace will not work (unless I also do replaces with periods and commas.)

Anyway, my point is that this can be a time consuming activity of performing a few dozen replace statements and then switching to the Report page to see what the word cloud now includes. Let’s assume that I’ve already done several rounds of replace statements and my word cloud looks like the following:

Another thing I can do to focus on the most important words is to change some of the properties of the visualization. Click on the Pencil in the Visualizations column to display the properties and open the General property group as shown below:

Notice that you can change the maximum number of words displayed, the minimum and maximum font size and whether word breaking is on or off.

First the number of words displayed greatly affects the appearance of the cloud. The following two images show both 200 words and 50 words. (The previous image was 100 words.)

Obviously changing the minimum and maximum font size will help visually give me an idea of which words occurred the most often. The fourth option is the control that breaks the column into individual words. Otherwise, the entire phrase in the column is used which may be good for short predefined responses, but not so good for my case here.

You should have also noted that each time I change one of the properties, the word cloud regenerates. Another somewhat less obvious thing that occurs is that special characters are converted to blanks when the words are parsed. Therefore, a word like: “site’s URL” gets parsed into three words: “site”, “s” and “URL” which explains why the single letters “s” and “t” often appear even in a 50 word cloud. Of course, it might be possible to go back and replace “‘s ” with ” ” or “‘t ” with ” “. Again, I need to be very creative and very careful to come up with a final word cloud.

Anyway, I hope you found that interesting. In future weeks I will look at additional Power BI features and visualization. C’ya then.

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Power BI – Measures and Slicers

Sorry about missing last week but it was a holiday here in the states where parents encourage their kids to go up to strangers and ask for candy. Also people of all ages, not just kids, get dressed up in costumes that range from the cute Elsa princesses to the DBA zombies and the slutty managers. Yeah, some of them were really scary especially since it wasn’t always a costume.

Anyway, last time I talked about creating custom columns and added the column TotalProfit to the FactSales table of my model. (If you missed that discussion, go back to my blog from two weeks ago.) Column expressions are easy to create because they largely resemble row context expressions that you might add to an Excel spreadsheet. In fact, most of the syntax and functions are exactly the same as in Excel.

But we can also add custom measures. The difference between a column and a measure is that most columns can be used as dimensions in a pivot or matrix table especially the columns that contain alphanumeric data. Typically the columns that have numeric data appear as aggregated values, summed, averaged, minimized or maximized depending on the goal and the dimensions used. In fact, the TotalProfit column, while calculated for each row in the FactSales table, is typically displayed as a summed value such as in the following image taken from where we left off last time.

In this matrix table, the TotalProfit is first calculated for each row in the FactSales table that will be included in the final matrix table, but the individual calculated values are then summed by one of five channels that exist for sales. Thus with my sample data, the calculation for TotalProfit occurs a little over 3 million times, but these calculations are made only when the column is created, not for each visualization. Then those values are summed by channel for the above visualization. As this happens on my Surface computer in less than a second, that is still pretty impressive. However, there is another way we can calculate total profit for this table.

We can create a measure to calculate total profit. A measure is calculated each time it is used in a table. In a table like the one above, a total profit measure would only be calculated four times, once for each channel, but each calculation would include three sums over potentially hundreds of thousands of rows and these calculations occur when the visualization is created and in each visualization the data might be needed. To create a measure, find and click on the New Measure button in the calculations group of the Modeling ribbon.

In the expression box, I can see the start of the expression. Note that unlike PowerPivot, the measure name is followed by just the equal sign rather than a colon and equal sign. The rest of the expression is similar to what I used before except that I have to aggregate (in this case sum) each of the values in the expression (you cannot sum an expression). In order to distinguish this expression from the prior one, I include an underscore between ‘total’ and ‘profit’.

After defining the measure, it appears in the field list on the right. While it is still selected, I go to the formatting section of the ribbon and adjust the data format to $ English (United States) which gives me comma separators and two decimal places by default. Note that I can and should do this for any other table field that I will use in a visualization.

Now I can add this measure to my matric table from above replacing the TotalProfit calculated column with the Total_Profit measure. In order to calculate the total profit for channel sales, Power BI has to sum the SalesAmount column for all channel sales and subtract from it the sum of channel total costs and the sum of channel discount amounts.

Your first thought might be to ask how is this better than calculating the total profit for each row in the FactSales table and then simply summing the included rows into a single value. Well like a lot of things in the real world, it depends. Typically a calculated column increases data load times each time you load the model because only the expression is saved, not the individual values (or at least so I’ve been led to believe), but for many visualizations that use most if not all of the data, the impact on calculating the values for the visualization are no worse and possibly even less than using a calculated measure. On the other hand, for a calculated measure that only appears in a few visualizations or when the visualization has been filtered to include a smaller subset of the total data, a calculated measure can improve both data load times and dashboard display times.

But perhaps more importantly, not all expressions can be written as either a calculated column or a measure interchangeably as I have done here. For example, if I wanted to calculate the percent that each channel sales represents from the total sales, I would have to use a measure because there is no way to aggregate percentages over multiple rows. I want you to think about that and I may return with a detailed example in a future week. In the meantime, I want to explore one additional feature of this model.

Those of you who have followed me through my travels in using PowerPivot remember the concept of using slicers to allow the user to analyze different segments of the data by clicking on dimension values. For example, using the above table, I might want to see the profit numbers for different product categories. In PowerPivot, I could create a slicer and if I had more than one visualization on a page, I could associate each chart or table with that slicer.

In PowerBi, I can also define slicers for the reports on the page. To do so, I click in a blank area and select the field from the dimension table that I want to use as the slicer/filter. In this example, I will use the ProductName field from the dimProductCategory table. This dimension only has eight values. The table is relatively short and appears initially as shown below.

To convert this table list into a slicer, I need to select the Slicer visualization as shown in the following figure while the above table has focus. This tells PowerBI to use the table as a slicer rather than just displaying the values of the field. (Yes, I could add a filter to the visualization directly, but a slicer can automatically apply to multiple visualizations on the page.)

After being defined as a slicer, each value of ProductCategoryName has a selection box to its immediate left and two additional entries have been added to the top of the list to select all values or to select only those records that have a blank for the product category.

If I click on any one of the categories, the data in the other visualizations on the same page automatically filters out all records from other categories as shown below.

I can also select multiple categories by clicking on several of the checkboxes to include in the matrix sales from all of the selected categories. Note that in a case like this, the measure I defined actually will perform fewer calculations because the sum function only acts on the filtered records of the slicer, not on all records in the channel.

I can return to displaying all categories either by clicking the Select All option, by clicking on each of the individual category names, or by using the Erase icon to the right of the table name.

C’ya next time.

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Deriving Columns from What You Know in Power BI

Before I get started with the topic of the day, I want to remind you that Power BI is still being updated by Microsoft. In fact, there was an update just last week on October 20th that appears to have added some missing features that I mentioned before. So if you haven’t updated Power BI recently, be sure to do that before reading the rest of this blog.

A few weeks ago when I was looking at defining relations between tables in the Relationship dialog I was disappointed in the fact that when I display my tables in what I would have previously called a Dialog view in PowerPivot, I could not use drag and drop to define my relations. Well, my disappointment is over. The latest update, among other things, adds this capability.

Since last Wednesday, October 21, was ‘Back to the Future’ day, I want to go back to last week’s blog just after I added the DimProductCategory table. This time however, rather than using the Management Relationships dialog, I am just going to the Relationships view and drag the field ProductCategoryLabel to ProductCategoryKey as shown in the following image.

Now I must say that I am use to just identifying the fields from my two tables that I want to connect to form the relationship and expect PowerPivot to figure out which is the one and which is the many table. Unfortunately, Power Bi still is not quite this smart. Because when I attempt to define this relationship from the one site to the many side, the relationship definition fails as shown in the following figure.

Maybe the next update will automatically reverse the direction of the relationship for me. However, for now I can click the OK button in the message box shown above and then redefine the relationship correctly from the DimProductSubcategory to the DimProductCategory table.

Now the relation is created and I am ready to continue. By the way, when I click on the relationship, Power BI highlights the two tables as well as the relationship line. It also encloses the connecting fields in a box to make it easy to identify the relationship fields.

If instead of left clicking on the relationship, suppose I right click on the relationship. Now I get an expected option menu which in this case consists of only a single option, Delete. Clicking this option will of course delete the relationship.


On the other hand, double clicking on the relationship opens the Edit Relationship dialog shown below. I can use this dialog to view the relationship or a sampling of the data, or make changes to the relationship.

That’s great. But like an infomercial, there’s more! In the Relationship view (Diagram view) I can also now right click on a field and delete a field, hide a field from the Report view or rename the field.

As with PowerPivot, if I delete a field, it is gone for good. If I realize later that I need the field, I would need to delete the table and reload it to get the missing field. Of course there are other consequences to doing this like needing to redefine the relationships and possibly rebuilding reports (visualizations) that used that table. Because at this time, I cannot visually define the fields to include or exclude during my original data load from my data sources, it is important that I know my data and delete any fields that I know that I will not need before I begin creating new columns, measures, or reports. In PowerPivot, we called these columns Useless columns.

I can also hide some columns from the Report view. For example, I can often hide columns used to define relationships between tables because end users typically do not include these columns in reports. Hidden columns are referred to as Technical columns because they are required in the data model and cannot be deleted without destroying relationships or perhaps calculations of other fields. I never want to show more columns to a user than they know what to do with.

Finally, renaming columns can be very beneficial. Often column names in databases have cryptic or abbreviated names. End users may not be comfortable with these shorten names. Use the Rename feature to make names user-friendly and descriptive.

Not only can I delete, hide, or rename columns in a table, but by right clicking on the table header, I can perform these same actions on an entire table. For example, I may not like dimension tables that begin with the letters ‘Dim’ like many DBAs prefer but end users may have no idea why the table is Dim. Similar to columns, I don’t delete tables from my model unless I am positive that I do not need them. Hiding tables only makes sense if I want to use only one or two fields from a table. In this case, I may need a column from another table for a calculation, but I would never display those columns directly in reports.

Returning to the data view of my tables, I can also right click on any of the column headers and delete, hide or rename the column. There are also several other options ranging from sorting, to creating new columns or new measures.

I can also right click on the column names in the fields list along the right side of the Data view. The dropdown list of options shown below is similar to options in the context menu above. So I have several different ways to manage columns

Let’s try something new, a new column in fact. In my FactSales table, I can find several columns like SalesAmount, TotalCost, and several others, but there is not Total Profit column. I can easily calculate that value from other values in the table. To begin, I need to create a new column in my data model. That means clicking the New Column button in the Modeling ribbon.

New columns are created at the right end of the table. By default, the column name is cleverly called Column. Of course I can change that by entering a new name. Then after an equal sign which indicates that an expression will be used to define the column, I can begin entering the column definition using DAX expressions. Yes, DAX is still alive and well. If you need a review of using DAX, I’ve covered multiple DAX topics over the last couple of years of this blog.

As in PowerPivot, I can select a column from the current table by just typing the left square bracket. This action opens a dropdown of all column names in the current table listed alphabetically. I can scroll down through the list and select a column by double clicking on its name. I can also type a few characters of the column name to narrow down my list as shown below.

My full expression to calculate TotalProfit is shown below

When I click the Enter key, the entered expression is used to calculate the values for all the table rows.

Before moving off the column, I might want to define custom formatting for the values. The formatting definition here is carried forward to all reports generated with the data. In the above example, I might want to only display the dollar amounts to two decimal places. (Actually, because of the size of aggregated data, I might later decide to format the values with no decimal places.)

Suppose that I want to display some of this data using a standard table with Channel names for the rows and a few select columns from the table. Notice that the values displayed here obey the formatting definition set on the Data page.

That’s it for this week. Next time I cover creating New Measures and why you might want to do so.

C’ya.

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Creating Relationships Manually in Power BI

Last time we loaded a BI model with data from several different data sources, but the data sources were carefully selected so that the columns that would relate one table to another table would already have the same name and data type before attempting to upload the data to Power BI Desktop. Because of this assumption, I was able to let Power BI auto detect the relations between the tables. Unfortunately, in the real world, that does not always happen. So this time I will show you how to create relationships manually and how you may need to massage the data a little first before defining those relationships.

I am going to use the Contoso data again and will begin with the following tables already loaded into the model from my SQL Server instance: FactSales, DimChannel, DimDate, DimProduct, and DimProductSubCategory. To complete my model, I need the Product Category data which I am going to load from an Access table in this case.

Since we covered how to load data from SQL Server last time, I am going to pick this discussion up after I have loaded the SQL data and am about to load the Product Category data. The image below shows that I have already click the Get Data button and have selected Access database from the list of possible data sources and have defined where my Access database could be found. As you can see in this figure, Power BI only finds a single table in my Access database named DimProductCategory. When I select this table, I see a preview of the data.

In the table preview (on the right), I see that there are only two columns named ProductCategoryLabel and ProductCategoryName. You might be able to guess from the way the data in the column is formatted that the ProductCategoryLabel data has been defined as text, not numbers because it is left justified.

After clicking the Load button to import this data into my data model, I switch to the relationships view and see the table diagram shown below.

I see that there is no relationship defined with the DimProductCategory table because there are no connecting lines leading into or from this table. Therefore, I first want to select the Manage Relationships button. I can see the four relationships that tie together the other five tables, but I am missing a relationship. I may first try to click the auto detect button which I talked about last time to see if Power BI can find the missing relationship

Unfortunately, Power BI very quickly responds back that it cannot. The problem is that while the DimProductSubcategory table has a field named ProductSubCategoryKey, the new table, DimProductCategory, does not have a field with that name.

So I might try to create the relationship manually by clicking the New button on the Manage Relationships screen (after closing the Autodetect message screen of course).

On this screen I have to specify the names of the tables and the linking columns to define the relationship. I can begin with either table on the top. After I select the table, I see a grid of the available fields along with a couple of rows of data. To select the field that I want to use in the relationship, I merely need to click the column header to select the field.

I then specify the name of the linking table along with selecting the column that I want to link to.

There are some advanced options which I am going to skip over for now and simply click the OK button. Power BI then attempts to create the relationship. But wait a minute, you might say, the column names are different and the data types of these two fields are different. That is true. I am not surprised that I can link two tables on fields with a different name, but different data types? In fact, I expected Power BI to reject this relationship because of the different data types, but it did not. It created the relationship as shown in the following image

To find out if this relationship is really valid, I next go to the report desktop and select the ProductCatgoryName field from the DimProductCategory table and the ProductSubcategoryName field from the DimProductSubcategory table. You can see from the figure below that the data makes sense. The general category Audio should include subcategories like Headphones, Radios, and Speakers, but not Camcorders, Cameras, or Cell Phones. Somehow, Power BI was able to transform automatically one of the data types to the other (I am thinking it converted the string field to an integer) and then formed the relationship.

Yes, I could prove that this relationship is correct another way by creating a simple table that lists ProductCategoryLabel from the DimProductCategory table and ProductCategoryKey from the DimProductSubcategory table. You can clearly see that the relationship links these two tables correctly.

Surprised by this, I wanted to try something else that may not be as easy for Power BI to automatically convert. I took another table, the Stores table, and modified the Excel version of the table to change StoresKey to prefix the store number with the letter ‘S’. I then loaded that table into my model as shown below.

Next I went to the Manage Relationships dialog as before and attempted to add a relationship between the FactSales table and the Stores table as shown in the following figure.

Again Power BI did not complain about creating the relationship. However, when I went to the reports page this time and attempted to display a report of sales amount (from FactSales) against the StoreID from the Stores table, I got the result shown below which indicates a total sales for each year, but no StoreID value at all. (I used the Matrix visualization here with StoreID for the rows and YearLabel for the columns and SalesAmount for the value.) So clearly this relationship did not work.

Therefore, I need to edit one table or the other to ‘fix’ this relationship. I choose to edit the Stores table. Remember from last time, to edit a table, I click the Edit Queries button to open a new window where I can edit the tables.

In this case, the solution is to remove the ‘S’ from the front of each of the StoreID values. I can do this with the Split Column transformation and specifically to split the column based on the number of characters from the left rather than splitting the column based on any specific character or character string like I did in an earlier blog example.

This action results in two columns, the first, StoreID.1, contains only the first character of the StoreID field, the ‘S’. The second field, StoreID.2, contains the numeric portion of the store ID. As you can see in the formatting of this column in the following figure, Power BI also treats StoreID.2 as a numeric value.

I then removed the StoreID.1 column as something I will not need. I also renamed StoreID.2 to just StoreID.

I then clicked Close and Apply.

Now I can create a new relationship between FactSales and Stores using StoreKey and StoreID respectively as shown below. Note that I did not change the field names to match. If I had, I could probably use the auto-detect feature to find and create the relationship for me.

This time when I attempt to create the same report on sales by store and year, I get reasonable results as shown in the table below.

Well, I hope you found that interesting. Next time I plan to probe a little deeper on creating calculated columns within a query.

Till next time, c’ya!