Chapter 31 problems with extraction - 223 divisions not named county
Alaska - Borough (13), Census Area (11), 4 City and Borough Puerto Rico - Municipio, (78) Louisiana - Parish, (64) Connecticut - Planning Region (9) Some in Virginia - city, 40 lowercase, 2 “City County” Maryland: Baltimore city Missouri: St. Louis city Nevada: Carson City District of Columbia - District of Columbia, District of Columbia
us_counties <- get_acs(
geography = "county",
variables = "B01003_001", # Total population variable
state = NULL, # all states
year = 2023,
survey = "acs5",
geometry = TRUE, # Include geographic boundaries
cache_table = TRUE
) |>
shift_geometry() |> #shifting AK, HI, PR to fit US map better
# will need to change to case_when
mutate(state = str_remove(NAME, "^.+County,")) |>
mutate(county = str_remove(NAME, " County,.+$")) %>%
select (GEOID, state, county, geometry)## Getting data from the 2019-2023 5-year ACSNow we will read in the national health data file, and merge it with the US counties data. Note that this file includes total rows for the each state and the District of Columbia (with an empty [NA] county name).
national_health_data <- read_excel(here("data/national_health_data.xlsx"),
col_names = TRUE, skip = 1, sheet = 2) |>
janitor::clean_names()## New names:
## • `Unreliable` -> `Unreliable...4`
## • `95% CI - Low` -> `95% CI - Low...7`
## • `95% CI - High` -> `95% CI - High...8`
## • `National Z-Score` -> `National Z-Score...9`
## • `95% CI - Low` -> `95% CI - Low...39`
## • `95% CI - High` -> `95% CI - High...40`
## • `National Z-Score` -> `National Z-Score...41`
## • `Unreliable` -> `Unreliable...42`
## • `95% CI - Low` -> `95% CI - Low...44`
## • `95% CI - High` -> `95% CI - High...45`
## • `National Z-Score` -> `National Z-Score...46`
## • `95% CI - Low` -> `95% CI - Low...69`
## • `95% CI - High` -> `95% CI - High...70`
## • `National Z-Score` -> `National Z-Score...71`
## • `95% CI - Low` -> `95% CI - Low...73`
## • `95% CI - High` -> `95% CI - High...74`
## • `National Z-Score` -> `National Z-Score...75`
## • `National Z-Score` -> `National Z-Score...77`
## • `National Z-Score` -> `National Z-Score...84`
## • `National Z-Score` -> `National Z-Score...86`
## • `National Z-Score` -> `National Z-Score...90`
## • `National Z-Score` -> `National Z-Score...94`
## • `National Z-Score` -> `National Z-Score...98`
## • `National Z-Score` -> `National Z-Score...100`
## • `National Z-Score` -> `National Z-Score...107`
## • `95% CI - Low` -> `95% CI - Low...115`
## • `95% CI - High` -> `95% CI - High...116`
## • `National Z-Score` -> `National Z-Score...117`
## • `95% CI - Low` -> `95% CI - Low...119`
## • `95% CI - High` -> `95% CI - High...120`
## • `National Z-Score` -> `National Z-Score...130`
## • `95% CI - Low` -> `95% CI - Low...132`
## • `95% CI - High` -> `95% CI - High...133`
## • `National Z-Score` -> `National Z-Score...134`
## • `95% CI - Low` -> `95% CI - Low...152`
## • `95% CI - High` -> `95% CI - High...153`
## • `National Z-Score` -> `National Z-Score...154`
## • `National Z-Score` -> `National Z-Score...156`
## • `National Z-Score` -> `National Z-Score...158`
## • `95% CI - Low` -> `95% CI - Low...161`
## • `95% CI - High` -> `95% CI - High...162`
## • `National Z-Score` -> `National Z-Score...163`
## • `National Z-Score` -> `National Z-Score...165`
## • `Population` -> `Population...167`
## • `95% CI - Low` -> `95% CI - Low...169`
## • `95% CI - High` -> `95% CI - High...170`
## • `National Z-Score` -> `National Z-Score...171`
## • `Population` -> `Population...173`
## • `95% CI - Low` -> `95% CI - Low...175`
## • `95% CI - High` -> `95% CI - High...176`
## • `National Z-Score` -> `National Z-Score...177`
## • `National Z-Score` -> `National Z-Score...181`
## • `National Z-Score` -> `National Z-Score...185`
## • `95% CI - Low` -> `95% CI - Low...187`
## • `95% CI - High` -> `95% CI - High...188`
## • `National Z-Score` -> `National Z-Score...189`
## • `95% CI - Low` -> `95% CI - Low...197`
## • `95% CI - High` -> `95% CI - High...198`
## • `National Z-Score` -> `National Z-Score...199`
## • `National Z-Score` -> `National Z-Score...223`
## • `National Z-Score` -> `National Z-Score...225`