Catallaxy Services | @feaselkl |
|
Curated SQL | ||
We Speak Linux |
Power BI is an interactive data visualization tool. It comes with a number of built-in visualizations, including:
These out-of-the-box visuals work well, but won't cover every scenario.
When what comes out of the box isn't enough, Power BI also offers two major places for expansion:
Today's talk will focus on finding and using custom Power BI visuals. Our theme of choice is baseball statistics.
Custom visuals are available at the Power BI custom visuals gallery (https://app.powerbi.com/visuals/).
Click on an interesting-looking custom visual and you have the option to download the visual, as well as a Power BI Desktop file which shows off the visual.
To import a visual, open Power BI Desktop and click the ellipsis (...) button on the Visualizations menu and select the "Import a custom visual" option.
Navigate to where you downloaded the custom visual and select it.
After a moment, the import should succeed.
You will then see the new visual as an option on your Visualizations panel.
The aquarium is a toy visual, but a fun one. We select the data elements to represent fish and fish sizes.
We will use the SeriesPost.csv file from the Lahman database, calling it Championships.
Add a new measure called Count. Count = COUNTA(Championships[Winner])
Sparklines are useful, terse diagrams. They are line charts without numeric values and act to show trends rather than focusing on individual data points. Sparklines are time series measures.
Lahman database Teams.csv and TeamsFranchises.csv files. Teams is year-by-year stats; TeamsFranchises is basic franchise information. We want to set up a data relationship in Power BI for these two files.
To keep from getting overwhelmed, let's look at NL East teams from 1994 on.
The box and whisker chart plots the distribution of elements, showing us helpful figures like median, mean, first and third quartiles, and outliers.
Lahman database Batting.csv and Master.csv, and locally created PlayerPositions.csv files.
Slugging percentage is defined as Total Bases / At-Bats. Total Bases = (1 * 1B) + (2 * 2B) + (3 * 3B) + (4 * HR). We have Hits rather than singles, so our modified calculation is:
SLG = 1000.0 * (Batting[H] + Batting[2B] + 2 * Batting[3B] + 3 * Batting[HR]) / Batting[AB]
SLG is usually calculated as a range from 0 to 1 (or more); we multiply by 1000 to make it a range from 0 to 1000.
The tornado visual shows a class-by-class breakdown of two groups. We often see this in sporting events or other direct competitions.
We use the same data sources and calculations as in the Box and Whisker experiment.
Another custom visual is the Smart Filter. This is an easy way of showing filters on-screen.
The gap analysis visual gives a direct comparison between two elements. This is similar to the Tornado visual, but gap analysis focuses more on differences.
We use the same data sources as in the Box and Whisker and Tornado experiments. We do not need the slugging percentage calculation, however.
Radar charts (AKA spider charts) give a 2D representation of several variables.
We use the locally created BattingRatings.csv file. This data comes from OOTP 16.
We have one new calculation, FullName. FullName = BattingRatings[FirstName] & " " & BattingRatings[LastName]
There are a number of custom visuals available within Power BI. You can also create your own custom visuals.
To learn more, go here: http://CSmore.info/on/powerbiviz
And for help, contact me: feasel@catallaxyservices.com | @feaselkl