Use tidyverse (ggplot2, dplyr) to explore the COVID-19 data for Louisiana.
We will work with two datasets. The first dataset is from Louisiana Department of Health where you can get three excel files for:
Cases and Testing Data by Parish by Day
Cases and Testing Data by Census Tract by Week
Cases by Age/Gender by Week
The second dataset is from the COVID Tracking Project where you can download two CSV files:
Daily case count data “louisiana-history.csv”
Daily case data by race “CRDT Data - CRDT.csv”
Summarize following objects using appropriate graphs:
Daily test counts by Parish
Daily Negative test counts by Parish
Daily Positive test counts by Parish
Weekly total case count by Age group
Weekly case count by Age group and Region
Weekly total case count by Gender
Weekly case count by Gender and Region
Weekly case count by Gender and Parish
Daily case count by Race group of State LA
Daily death count by Race group of State LA
Link the daily case count data from the two sources. Summarize your findings.
Recreate the 4 plots (New tests, New cases, Current hospitalizations, and New deaths) of total daily case counts with 7-day average of Full range at the COVID tracking project website
Compile a tibble that contains cumulative percent positivity of tests by Parish.
Plot the cumulative percent positivity of tests of Parish on a map for the weeks containing dates (one plot for each week):
March 22, 2020 (stay at home order starts)
May 15, 2020 (stay at home order lifted, phase 1 starts)
June 5, 2020 (phase 2 starts)
July 13, 2020 (Mask mandate)
January 1, 2021 (New Year’s eve within holiday season)
January 10, 2021 (Peak of new cases across US)
Plot new cases by lab collection date with a 7-day average line of New Orleans and Baton Rouge.
Explain your findings.