covid data per capita

covid-per-capita.r

library(choroplethr)
library(tidyverse)
library(choroplethrMaps)

data(county.regions)
data(df_pop_county)

data <-
  "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv" %>%
  url() %>%
  read.csv(stringsAsFactors = FALSE) %>%
  as_tibble() %>%
  rename(region = fips) %>%
  group_by(region) %>%
  summarise(cases = max(cases), deaths = max(deaths)) %>%
  right_join(county.regions) %>%
  full_join(df_pop_county) %>%
  rename(pop = value) %>%
  replace_na(list(deaths = 0, cases = 0)) %>%
  mutate(full.name = paste(county.name, ", ", state.abb, sep = ""))


data %>%
  mutate(value = 1000000 * cases / pop) %>%
  county_choropleth(
    title = "COVID-19 cases per million (3/28/20 nyt data and 2010 census)",
    legend = "cases per million",
    num_colors = 1,
    state_zoom = c("california", "oregon", "washington")
  )


#
# data %>%
#   filter(deaths > 0) %>%
#   ggplot(aes(x=1000000*deaths/pop, y=1000000*cases/pop, label=full.name, color=pop)) + scale_color_continuous(trans="log") +  scale_x_log10()+ scale_y_log10()+  geom_text(size = I(2)) + xlab("deaths per million")  + ylab("cases per million")
#