Homework 2 Challenge 1

Let us revisit the CDC Covid-19 Case Surveillance Data. There are well over 3 million entries of individual, de-identified patient data. Since this is a large file, I suggest you use vroom to load it and you keep cache=TRUE in the chunk options.

# file contains 11 variables and 3.66m rows and is well over 380Mb. 
# It will take time to download

# URL link to CDC to download data
url <- "https://data.cdc.gov/api/views/vbim-akqf/rows.csv?accessType=DOWNLOAD"


#Admission to ICU
covid_data <- vroom::vroom(url)%>% # If vroom::vroom(url) doesn't work, use read_csv(url)
  clean_names()

covid_data_clean <- covid_data %>% 
 filter(death_yn %in% c("Yes", "No"), 
        sex %in% c("Male", "Female"),
        icu_yn %in% c("Yes", "No"),
        age_group != "Unknown") %>% 
  
  select(death_yn,age_group, sex, icu_yn) %>% 
  
  group_by(age_group, sex, icu_yn) %>% 
  
  summarise(death = sum(death_yn == "Yes"), total= n()) %>% 
  
  mutate(death_rate = death/total)
  
  
ICU_deaths_plot <- ggplot(covid_data_clean,
                               mapping = aes(x = age_group, 
                                y = death_rate))+
  geom_col(fill = "#FF9582")+
 facet_grid(cols = vars(sex),
             rows = vars(factor(icu_yn, ordered = TRUE, 
                                 levels = c("Yes","No"),
                                 labels = c("Admitted to ICU",
                                            "Not ICU")))) + 
  theme_bw()+
  scale_y_continuous(labels = scales::percent)+
  coord_flip()+
  geom_text(size=3, aes(label = round(100*death_rate,digits = 1),
                y = death_rate + 0.05))+
  labs(title = "Covid death % by age group, sex and whether patient was admitted to ICU", 
       x ="", 
       y="", 
       caption = "Source: CDC")
  
ICU_deaths_plot

#save the picture
ggsave("ICU_yesno.jpg",plot=ICU_deaths_plot, width = 15,height = 10)

#place the picture in code
knitr::include_graphics(here::here("ICU_yesno.jpg"))

#effect of comorbidity

covid_data_clean_comorb <- covid_data %>% 
 filter(death_yn %in% c("Yes", "No"), 
        sex %in% c("Male", "Female"),
        medcond_yn %in% c("Yes", "No"),
        age_group != "Unknown") %>% 
  
  select(death_yn,age_group, sex, medcond_yn) %>% 
  
  group_by(age_group, sex, medcond_yn) %>% 
  
  summarise(death = sum(death_yn == "Yes"), total= n()) %>% 
  
  mutate(death_rate = death/total)
  
  
    
co_morbid_deaths_plot <- ggplot(covid_data_clean_comorb,
                               mapping = aes(x = age_group, 
                                y = death_rate))+
  geom_col(fill = "#6B7CA4")+
 facet_grid(cols = vars(sex),
             rows = vars(factor(medcond_yn, ordered = TRUE, 
                                 levels = c("Yes","No"),
                                 labels = c("With comorbidities",
                                            "Without comorbidities")))) + 
  theme_bw()+
  scale_y_continuous(labels = scales::percent)+
  coord_flip()+
  geom_text(size=3, aes(label = round(100*death_rate,digits = 1),
                y = death_rate + 0.05))+
  labs(title = "Covid death % by age group, sex and presence of co-morbidities", 
       x ="", 
       y="", 
       caption = "Source: CDC")

co_morbid_deaths_plot

#save the picture
ggsave("co_morbidities.jpg",plot=co_morbid_deaths_plot, width = 15,height = 10)

#place the picture in code
knitr::include_graphics(here::here("co_morbidities.jpg"))