~ Siri Shultz ~

Conditional Formatting, Forecasting and Anomaly Detection. 
Using three of Power BI’s five conditional formatting options to extend the design of a matrix visual and use anomaly detection and forecasting to gain a deeper understanding of what’s going on now and in the future in the Great Lakes.
Part of the Power BI Work Out Wednesday Challenges, reference below.

 


Data Source:

NOAA’s Great Lakes Environmental Research Laboratory. Raw data from Data.World. 


Challange Requirements

2021 Week 7 | Power BI: Conditional Formatting

  • Find a Power Query transformation that will change the individual lake columns into rows (you’ll end up with only three columns loaded into your data model)
  • Create a matrix visual that displays ice coverage by lake (on columns) and year (on rows)
  • Include major format options as seen in the sample, such as removing row and column totals
  • Find the “lake” you need to filter out and add a page-level or report-level filter to exclude it
  • Matrix conditional formatting
    • Background Color
      • Build a set of conditional formatting Rules (not the default gradient Color Scale option)
      • Use five different colors for ice coverage consisting of 0-24.99, 25-49.99, 50-74.99, 75-99.99, and 100
    • Font Color
      • Only use black (#000000) or white (#FFFFFF) font colors
      • Build and apply Rules so that the contrast between your background colors and white or black font color passes the accessibility test for WCAG AA at https://contrastchecker.com
    • Icons
      • Add a star next to any value where the maximum ice coverage is 100%
  • Add at least two supporting visuals around your matrix. You only have three fields, so be creative!
  • Answer the following questions:
    • Which lake has frozen completely over most often?
    • Is ice coverage trending *upward* over time for any lake?

Reference: https://www.workout-wednesday.com/2021/02/16/pbi-2021-w07/

 2021 Week 9 | Power BI: Forecasting and Anomaly Detection

  • Find a Power Query transformation that will change the individual lake columns into rows. You’ll end up with only three columns loaded into your data model (this comes straight from Week 7)
  • Add a line chart that displays the average coverage by year, forecasted out to the year 2030.
  • Add a second line chart that also displays average coverage by year showing anomalies at 75% sensitivity, explained by Lake. 
  • Formatting is totally up to you! We’ve been loving the creativity that the #WOW2021 community has been producing – keep it coming!
  • Answer the following questions:
    • What is the projected average ice coverage in 2030?
    • Which lake contributed most to data anomalies?

Reference: https://www.workout-wednesday.com/2021/03/02/pbi-2021-w09/

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