View source: R/get_genpop_covid.R
| get_genpop_covid | R Documentation |
Pulls data from the NYT github page on COVID cases and deaths for a county and then merges alongside a population number from the 2019 Census population estimates for that county to use for rate calculation.
get_genpop_covid(county, state = NULL)
county |
either a 5 digit fips code (safer) or a county name |
state |
a state name or abbreviation if a county name was provided |
a data frame with the following columns: Date, County, State, FIPS, General.Confirmed, General.Deaths, General.Population
## Not run:
# get data from Orange county, North Carolina
get_genpop_covid(county = "Orange", state = "NC")
# get data from los angeles by fips code
get_genpop_covid(county = "06037")
get CA hist data
ca_df <- read_scrape_data(TRUE, state = "California")
# look only at SATF
satf_df <- ca_df %>%
filter(Name == "SUBSTANCE ABUSE TREATMENT FACILITY")
# get the corresponding county data
county_df <- get_genpop_covid(first(satf_df$County.FIPS))
# make the plot comparing prison vs general population
county_df %>%
# get rid of potential conflicting columns
select(Date, tidyr::starts_with("General")) %>%
right_join(satf_df) %>%
mutate(`Prisoner\nPopulation` = Residents.Confirmed / Population.Feb20) %>%
mutate(`King County\nPopulation` =
General.Confirmed / General.Population2019) %>%
select(Date, `Prisoner\nPopulation`, `King County\nPopulation`) %>%
tidyr::pivot_longer(-Date) %>%
mutate(name = forcats::fct_rev(name)) %>%
ggplot(aes(x = Date, y = value, color = name, fill = name)) +
geom_area(alpha=.5, size = 1.5, position = position_dodge()) +
theme_behindbars() +
scale_color_bbdiscrete() +
labs(y = "Proportion\nInfected", color = "", fill = "") +
ylim(c(0,.65)) +
ggtitle(
"Substance Abuse Treatment Facility Outbreak",
"Comparing COVID Outbreaks in Prison and the Surrounding Area"
)
## End(Not run)
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