COVID Provisional Deaths Investigation

library(RSocrata)
library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.3.0      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

d <- ProCOVIDCty %>% select(!footnote) %>% pivot_longer(cols = 13:19, names_to = "race", values_to = "values") %>%

pivot_wider(names_from  = indicator, values_from = "values")

d$`Distribution of all-cause deaths (%)` <- d$`Distribution of all-cause deaths (%)` %>% as.numeric()

d$`Distribution of COVID-19 deaths (%)` <- d$`Distribution of COVID-19 deaths (%)` %>% as.numeric()

d$`Distribution of population (%)` <- d$`Distribution of population (%)` %>% as.numeric()

d$burden  =  d$`Distribution of COVID-19 deaths (%)`/ d$`Distribution of population (%)` 


data <- split(d, with(d, c(race)), drop = TRUE)

d$fips <- d$fipscode

library(usmap)

data$non_hispanic_white$burden <- scale(data$non_hispanic_white$burden)
data$non_hispanic_black$burden <- scale(data$non_hispanic_black$burden)
data$hispanic$burden <- scale(data$hispanic$burden)


plot_usmap(data = data$non_hispanic_white, regions = "counties", values = "burden")

plot_usmap(data = data$non_hispanic_black, regions = "counties", values = "burden")

plot_usmap(data = data$hispanic, regions = "counties", values = "burden")