COVID Cases over Time

1. Setting Up Libraries

   I will mainly be using tidyr and dplyr here to do the analysis.

2. Analysis Questions

   Thanks to Moiya Josephs for the analysis questions:
   1- Find the state with the highest and lowest deaths.
   2- Compare the death rate both before and after the vaccine was released.

3. Pull in the Data from Github

   To answer the questions above I will be doing a lot of sorting and group_by + filtering.

my_git_url <- getURL("https://raw.githubusercontent.com/aelsaeyed/Data607/main/Project2/COVID/United_States_COVID-19_Cases_and_Deaths_by_State_over_Time.csv")
covid_cases_raw <- read.csv(text = my_git_url)
head(covid_cases_raw, 10)

4. Fixing up and Subsetting Columns

   Some of the columns I need to be numerical are in character format, so I will take care of that first. I will also separate the submission_date column into 3 columns, one each for month, day, and year. The commas in the numerical columns had to be removed first.

cases_cleaned = covid_cases_raw %>% 
  separate(ï..submission_date, into = c("month", "day", "year"), sep="/") %>%  #separate the date values
  mutate(`new_death`= gsub(",", "", new_death) ) %>% #remove commas
  mutate(`new_death`= as.integer(new_death) ) %>%    #cast to int
  mutate(`tot_death`= gsub(",", "", tot_death) ) %>% #remove commas
  mutate(`tot_death`= as.integer(tot_death) ) %>%    #cast to int
  mutate(`year`= as.integer(year)) %>%    #cast the date values as int as well
  mutate(`month`= as.integer(month)) %>% 
  mutate(`day`= as.integer(day)) %>% 
  arrange(desc(year), desc(month), desc(day)) %>% #arrange by dates descending
  select(3,1,2,4,10,13) #Select only the columns I need
  
head(cases_cleaned, 10)

5. Grouping By State

   To answer the first question I will be grouping by states and summing the deaths.

sum_dth = cases_cleaned %>% 
  group_by(state) %>% 
  summarise(sum_tot_death= sum(tot_death)) 
  

state_max_deaths <- sum_dth[which.max(sum_dth$sum_tot_death),] 
state_min_deaths <- sum_dth[which.min(sum_dth$sum_tot_death),] 

state_min_deaths

state_max_deaths

6. Further Investigation

   It looks like a few states reported 0 deaths, so I wanted to see what the numbers looked like on that side of the spectrum. Below I simply display the sum of deaths per state but sorted in ascending order.

From what I looked up, PW is Palau, a “freely associated state”, which recorded 150 deaths.

head(sum_dth %>% arrange(sum_tot_death), 10)

7. The Impact of the Vaccine

   For this I will use the official release date of the vaccine in the United States for people over 16 after approval by the FDA, which is August 23, 2021. (https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine). I will order the data by date and draw a line graph of the new deaths per day. It would appear that around the same time the vaccine was released, the number of new death dropped followed by another resurgence.

chrono_dth <- cases_cleaned %>% 
  filter(new_death>0) %>% 
  group_by(year, month, day) %>%
  summarise(sum_new_dth = sum(new_death)) %>% 
  mutate(`date` = as.Date(paste(year,month,day,sep="-"))) %>% 
  filter(year > 2020)

## `summarise()` has grouped output by 'year', 'month'. You can override using the
## `.groups` argument.

head(chrono_dth, 10)

dates_vline <- as.Date(c("2021-08-23"))                 # Define positions of vline
dates_vline <- which(chrono_dth$date %in% dates_vline)

ggplot(data = chrono_dth, aes(x = date, y = sum_new_dth)) +
  geom_line(color="blue") +
  geom_point() +
  geom_vline(xintercept = as.numeric(chrono_dth$date[dates_vline]), color="red", linetype="dotted", size = 1)