1.1 GIS data download and preparation
library(tidyverse)
library(leaflet)
library(rmapshaper)
library(tidycensus)
library(stargazer)
library(scales)
library(sf)
library(pander)
options(tigris_use_cache = TRUE)
## turn off scientific notation
options(scipen = 5)## Download GIS data for maps
## geometry = TRUE --> GIS shapefile data to create maps
## B01001_001: total population
## NOTE: When you download the county data for the regressions, use options: geometry = FALSE,keep_geo_vars = FALSE
# county-level data for population
countyGIS <- get_acs(geography = "county",
variables = "B01001_001",
geometry = TRUE,
keep_geo_vars = TRUE)
# State data (for displaying state borders on map)
stateGIS <- get_acs(geography = "state",
variables = "B01001_001",
geometry = TRUE,
keep_geo_vars = FALSE)
## Simplify GIS data to make file sizes smaller. This essentially removes some details along coastlines and very-not-straight borders.
stateGIS <- ms_simplify(stateGIS, keep = 0.01)
countyGIS <- ms_simplify(countyGIS, keep = 0.01)
countyGIS <- countyGIS %>%
select(FIPS = GEOID,
stFIPS = STATEFP,
coFIPS = COUNTYFP,
coNAME = NAME.x,
pop = estimate,
geometry)
## For maps, drop the following:
## Puerto Rico (ST FIPS 72) (no election data)
## Alaska (ST FIPS 02) (voting data isn't reported by county...we could also map the legislative districts, but we're not going to since we'd rather have smaller maps without those extra details)
## Hawaii (ST FIPS 15) (so our map can zoom in on continental 48 states)
countyGIS <- countyGIS %>% filter(stFIPS != "72" & stFIPS != "02")
stateGIS <- stateGIS %>% filter(GEOID != "72" & GEOID != "02" & GEOID != "15")
## join 2-character state abbreviation and create name = county, ST for labeling maps
fipsToSTcode <- fips_codes %>% select(stFIPS = state_code, stNAME = state) %>% unique()
countyGIS <- inner_join(countyGIS,fipsToSTcode,by="stFIPS")
countyGIS <- countyGIS %>% mutate(name = paste0(coNAME,", ", stNAME))
## NOTE: If you don't use keep_geo_vars = TRUE, you don't get separate STATEFP and COUNTYFP, but you can use mutate() and create stFIPS = substr(GEOID,1,2) and coFIPS = substr(GEOID,3,5)Category 1 variable 1
white <- get_acs(geography = "county",
variables = "B01001A_001",
geometry = FALSE,
keep_geo_vars = TRUE)
white_filtered <- white %>%
select(FIPS = GEOID,
pop_white = estimate,
moe_white = moe)
countyGIS <- countyGIS %>%
inner_join(white_filtered, by = "FIPS") %>%
mutate(pct_white = 100 * pop_white / pop) Category 1 variable 2
asian <- get_acs(geography = "county",
variables = "B01001D_001",
geometry = FALSE,
keep_geo_vars = TRUE)
asian_filtered <- asian %>%
select(FIPS = GEOID,
pop_asian = estimate,
moe_asian = moe)
countyGIS <- countyGIS %>%
inner_join(asian_filtered, by = "FIPS") %>%
mutate(pct_asian = 100 * pop_asian / pop) Category 2 variable 1
white_age <- get_acs(geography = "county",
variables = c("B01001A_007", "B01001A_008", "B01001A_009", "B01001A_010", "B01001A_011", "B01001A_012", "B01001A_013", "B01001A_014", "B01001A_015", "B01001A_016", "B01001A_022", "B01001A_023", "B01001A_024", "B01001A_025", "B01001A_026", "B01001A_027", "B01001A_028", "B01001A_029", "B01001A_030", "B01001A_031"),
geometry = FALSE,
keep_geo_vars = TRUE, output = "wide")
whiteage_filtered <- white_age %>%
mutate(estimate = c(B01001A_007E + B01001A_008E + B01001A_009E + B01001A_010E + B01001A_011E + B01001A_012E + B01001A_013E + B01001A_014E + B01001A_015E + B01001A_016E + B01001A_022E + B01001A_023E + B01001A_024E + B01001A_025E + B01001A_026E + B01001A_027E + B01001A_028E + B01001A_029E + B01001A_030E + B01001A_031E)) %>%
mutate(moe = B01001A_007M + B01001A_008M + B01001A_009M + B01001A_010M + B01001A_011M + B01001A_012M + B01001A_013M + B01001A_014M + B01001A_015M + B01001A_016M + B01001A_022M + B01001A_023M + B01001A_024M + B01001A_025M + B01001A_026M + B01001A_027M + B01001A_028M + B01001A_029M + B01001A_030M + B01001A_031M) %>% select(FIPS = GEOID,
pop_whiteage = estimate,
moe_whiteage = moe)
countyGIS <- countyGIS %>%
inner_join(whiteage_filtered, by = "FIPS") %>%
mutate(pct_whiteage = 100 * pop_whiteage / pop) Category 2 variable 2
asian_age <- get_acs(geography = "county",
variables = c("B01001D_007", "B01001D_008", "B01001D_009", "B01001D_010", "B01001D_011", "B01001D_012", "B01001D_013", "B01001D_014", "B01001D_015", "B01001D_016", "B01001D_022", "B01001D_023", "B01001D_024", "B01001D_025", "B01001D_026", "B01001D_027", "B01001D_028", "B01001D_029", "B01001D_030", "B01001D_031"),
geometry = FALSE,
keep_geo_vars = TRUE, output = "wide")
asianage_filtered <- asian_age %>%
mutate(estimate = c(B01001D_007E + B01001D_008E + B01001D_009E + B01001D_010E + B01001D_011E + B01001D_012E + B01001D_013E + B01001D_014E + B01001D_015E + B01001D_016E + B01001D_022E + B01001D_023E + B01001D_024E + B01001D_025E + B01001D_026E + B01001D_027E + B01001D_028E + B01001D_029E + B01001D_030E + B01001D_031E)) %>%
mutate(moe = B01001D_007M + B01001D_008M + B01001D_009M + B01001D_010M + B01001D_011M + B01001D_012M + B01001D_013M + B01001D_014M + B01001D_015M + B01001D_016M + B01001D_022M + B01001D_023M + B01001D_024M + B01001D_025M + B01001D_026M + B01001D_027M + B01001D_028M + B01001D_029M + B01001D_030M + B01001D_031M) %>% select(FIPS = GEOID,
pop_asianage = estimate,
moe_asianage = moe)
countyGIS <- countyGIS %>%
inner_join(asianage_filtered, by = "FIPS") %>%
mutate(pct_asianage = 100 * pop_asianage / pop)Category 3 variable 1
us_citizen_us_born <- get_acs(geography = "county",
variables = "B05001_002",
geometry = FALSE,
keep_geo_vars = TRUE)
us_citizen_us_born_filtered <- us_citizen_us_born %>%
select(FIPS = GEOID,
pop_us_born = estimate,
moe_us_born = moe)
countyGIS <- countyGIS %>%
inner_join(us_citizen_us_born_filtered, by = "FIPS") %>%
mutate(pct_us_born = 100 * pop_us_born / pop) Category 3 variable 2
us_citizen_naturalized <- get_acs(geography = "county",
variables = "B05001_005",
geometry = FALSE,
keep_geo_vars = TRUE)
us_citizen_naturalized_filtered <- us_citizen_naturalized %>%
select(FIPS = GEOID,
pop_us_naturalized = estimate,
moe_us_naturalized = moe)
countyGIS <- countyGIS %>%
inner_join(us_citizen_naturalized_filtered, by = "FIPS") %>%
mutate(pct_us_naturalized = 100 * pop_us_naturalized / pop) Category 4 variable 1
employment_male <- get_acs(geography = "county",
variables = "B23001_002",
geometry = FALSE,
keep_geo_vars = TRUE)
employment_male_filtered <- employment_male %>%
select(FIPS = GEOID,
pop_employment_male = estimate,
moe_employment_male = moe)
countyGIS <- countyGIS %>%
inner_join(employment_male_filtered, by = "FIPS") %>%
mutate(pct_employment_male = 100 * pop_employment_male / pop)Category 4 variable 2
employment_female <- get_acs(geography = "county",
variables = "B23001_088",
geometry = FALSE,
keep_geo_vars = TRUE)
employment_female_filtered <- employment_female %>%
select(FIPS = GEOID,
pop_employment_female = estimate,
moe_employment_female = moe)
countyGIS <- countyGIS %>%
inner_join(employment_female_filtered, by = "FIPS") %>%
mutate(pct_employment_female = 100 * pop_employment_female / pop)