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COVID Report

COVID 
Cases & Deaths by Socioeconomic Vulnerability - Wisconsin

Census tract level reporting

of Wisconsin's COVID data in 

relation to socioeconomics.

created by Selena Frey

Problem: How does an individual state plan for its next major health crisis?


Hypothesis: By looking into its historical COVID data.


 

WDH.png

Step 1: Extract raw data

2). State CSV.png
3). GeoIDMap

Step 2: Load Data into SQL

1). CSV import.png

Step 3: Clean & Transform in SQL

edw.jpg
6) edw.covidstudy.png
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Step 4: Pull Table into Power BI

7). Power BI Connect.png
8). Load Power BI.png

Alternate Approach to Step 4

Using a custom SQL Query, 
as a Power BI data source.

9). SQL Query.png

SELECT
      County_Name
     ,Average_SV = AVG(Socioeconomic_Vulnerability)
     ,Cases = SUM(POS_CUM_CONF)
     ,Deaths = SUM(DTH_CUM_CP)
     ,Rank_SV = ROW_NUMBER()OVER(ORDER BY AVG(Socioeconomic_Vulnerability))
     ,Rank_SV_Desc = ROW_NUMBER()OVER(ORDER BY AVG(Socioeconomic_Vulnerability)DESC)
     ,Rank_Cases = ROW_NUMBER()OVER(ORDER BY SUM(POS_CUM_CONF))
     ,Rank_Cases_Desc = ROW_NUMBER()OVER(ORDER BY SUM(POS_CUM_CONF)DESC) 
FROM edw.COVID_Study
GROUP BY County_Name

Step 5: Create Visuals in Power BI

Cards

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Conditional Formatting

conditional formatting.png

Scatter plot

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Column Charts

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Power BI vs. SQL
Relationships

pbi.png
sql.png
10). Relationships.png
13). Relationship2.png
11). Unique.png

Checking relationships in SQL
 

12). Null.png

Power BI vs. SQL
Calculated Columns

14). Calculated column.png
edw.jpg

SQL in back of Power BI
 

Power BI vs. SQL
Calculated Measures

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