COVID-19 Cases (San Diego County, by ZIP Code) - Updated April 4, 2020

README.md

COVID-19 Cases (San Diego County, by ZIP Code) - Updated April 4, 2020

Sources:

• San Diego County
• NBC San Diego

County officials stressed the presence of cases, or lack thereof, should not affect how people operate during the coronavirus pandemic.

Staying at home, practicing social distancing and washing your hands are just some practices to maintain, regardless of the number of cases reported in your community.

The number of people actually infected with the disease is likely much higher than reported numbers because not everyone needs to be tested, including in some cases, the close contacts of positive patients who are usually presumed to have the disease.

append_population.r

library(readr)
library(tidycensus)
library(dplyr)
library(stringr)

cases <- read_csv("https://gist.githubusercontent.com/vikjam/f91acfb1b825c0345fc7f4a7c1f6c75e/raw/a58fc598ca58e36c7d2960cf1c59d3f314a2b527/cases_by_zip.csv")

acs_population <- get_acs(geography = "zcta", 
                          variables = c(population = "B01001_001"), 
                          year = 2018) %>%
                  filter(str_sub(GEOID, 1, 2) %in% c("91", "92")) %>%
                  select(GEOID, estimate) %>%
                  rename(zip = GEOID, population = estimate)

cases_pop <- inner_join(cases, acs_population, by = "zip")

cases_by_zip.csv

visualize.r

library(ggplot2)
library(hrbrthemes)

p_cases_vs_pop <- ggplot(cases_pop, aes(x = population, y = confirmed_cases)) + 
                  geom_point() +
                  labs(x="Population", y="Confirmed cases",
                       title="Population versus COVID-19 Cases, by ZIP Code",
                       subtitle="Restricted to San Diego County",
                       caption="Updated April 4, 2020") + 
                  theme_ipsum_rc()