For today’s theme I’ve gone with some subtle colors. Enjoy!
As always, my themes are free to use, but a linkback is appreciated!
“dataColors”: [“#737c5e”, “#9a977b”, “#CCC0A0”, “#B3AB8E”, “#e5d4b3”, “#d2b393”, “#bf926e”],
If you need to rename a field in a single visualization, you can simply drag the field into the visualization and then click on rename.
But what if you want to rename a field in several visualizations but not all of them. There is a work around that can get you to this. You need to create a new measure with a simple formula:
[New Name] = [Measure that you want to rename]
This can then be applied to any visualization that you need to use the alternative name.
In an exciting bit of news today, Microsoft announced the ability to remove the gray box that appears around visualizations. This can be done in Power BI desktop.
Any changes made in the desktop version will be transferred to the published dashboard.
You can read more about the update here.
There are several tricks for improving the response time of your Power BI pages. On of the easiest methods to implement is to limit your table visualizations to a certain number of rows.
By doing this, the visualization renders quicker and it helps your users focus on the most important data points.
There is an option to sum up all the other rows beyond the top N in a category called other, but that will also slow down your performance.
It requires using the Rankx function, which, as admitted by Microsoft’s own blog is the slowest function available in Power BI.
It’s important to evaluate the trade off between performance and functionality when designing your dashboards.
Though the data I usually work with has very little to do with physical locations, I decided to play around with the maps visualization in Power BI this evening. Since the data set I’ve been playing with includes both latitude and longitude information, it was the perfect opportunity to try it out.
To begin with, I selected the map visualization which gave me several options for input fields. I dragged the latitude and longitude fields from the data into the appropriate boxes and was rewarded with a map of Chicago.
However, the map is really crowded when all the data is displayed. Just for fun, I added gender to the legend field and the visualization became slightly more overwhelming.
While wanting to avoid grouping the data, I added some page level filters which limited the rides to female riders in February in thunderstorms. This left me with a much smaller amount of rides and after zooming in, I started to see the details in the maps.
Though this is still a bit cluttered, it’s easy to see where distinctions in the data could start to be made.
It’s a fun feature to play.
One of the best features of Power BI is the ability to add drilldowns on data. This allows the user to dive deeper into certain categories or data points based on the criteria presented in previous reports. For example, using the Divvy bike sharing data which contains several types of data, I created a pie chart based on day of the week and start station.
Then, right clicking on any of the pieces of the pie allows the user to select see records. This automatic drilldown allows the user to see a table showing all of the records contain.
To create custom drilldowns, you can add a second page to the Power BI dashboard. On the newly created page, put the field you want to use to drill through to the last option on the options pane shown below.
Recently, I created this pie chart based on distinct counts in Power BI. This was interesting simply because I didn’t think that there would be an difference in my data based on the day of the week, but there it was. The difference is slight, but surprising nonetheless.
To create a pie chart with distinct counts, I started by selecting the pie chart visualization. I think added my day field to the “legend” input and added from station to the values option.
To switch from a total value on the from_station, I pressed the small downward arrow and selected “Count (Distinct) as shown in the image below.
My output was the pie chart showing the distinct value counts below.
For today’s theme, I revisited a sunset palette filled with oranges and pinks.
As always, my themes are free to use but a link back is appreciated.
“dataColors”: [“#FAA275”, “#FF8C61”, “#E77B73”, “#CE6A85”, “#B35E7E”, “#985277”, “#5C374C”],
Microsoft announced yesterday an important update to the Power BI data hosting schema. You can now select where your day is hosted whether required for your internal reasons or outside regulatory requirements.
You can read more about it on the Microsoft Power BI blog here.
Note that this feature is only available in Power BI premium.
In my opinion, Power BI has an odd quirk in which you are unable to format a Count or Count Distinct. Often when I’m working with large datasets, I have counts greater than 1000 and I would like to use the “,” separator. Oddly this isn’t allowed as you can see the grayed out option here.
Through some trial and error I have found that there is a simple solution as shown below. Create a a new measure using a DAX statement. The measure shown below uses the formula Measure = COUNTX(data,data[day]) which can be formatted. Problem solved!