# api_key <- "5ed58a5745802102fb83d4eec5d1f7326f65ffab"
library(tidycensus)
library(tidyverse)
library(sf)
library(tigris)
library(forcats)
library(viridis)
library(stringr)
options(tigris_use_cache = TRUE)
options(tigris_class = "sf")
census_api_key("5ed58a5745802102fb83d4eec5d1f7326f65ffab")
z00 <- get_decennial(geography = "zcta", variables = "PCT0120001", state = "MN",
geometry = TRUE)
zc <- get_acs(geography = "zip code tabulation area", variables = "B19013_001",
summary_var = "B01001_001", geometry = TRUE)
tarr <- get_acs(geography = "tract", variables = c("B19013_001", "B01001_001"),
state = "TX", county = "Tarrant", geometry = TRUE, output = "wide",
moe_level = 99)
tarr <- get_acs(geography = "tract", variables = "B19013_001",
state = "TX", county = "Tarrant", geometry = TRUE)
racevars <- c("B03002_003", "B03002_004", "B03002_006", "B03001_003")
harris <- get_acs(geography = "tract", variables = racevars, key = api_key,
state = "TX", county = "Harris County", geometry = TRUE,
summary_var = "B01003_001") %>%
mutate(pct = 100 * (estimate / summary_est),
variable = fct_recode(variable,
White = "B03002_003",
Black = "B03002_004",
Asian = "B03002_006",
Hispanic = "B03001_003")
) %>%
st_transform(26915)
ggplot(harris, aes(fill = pct, color = pct)) +
facet_wrap(~variable) +
geom_sf() +
scale_fill_viridis() +
scale_color_viridis()
vt <- get_acs(geography = "county", variables = "B19013_001", state = "VT")
vt %>%
mutate(NAME = str_replace(NAME, " County, Vermont", "")) %>%
ggplot(aes(x = estimate, y = reorder(NAME, estimate))) +
geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) +
geom_point(color = "red", size = 3) +
labs(title = "Household income by county in Vermont",
subtitle = "2011-2015 American Community Survey",
y = "",
x = "ACS estimate (bars represent margin of error)")
# Try with normalizing var
# Decennial Census
black00 <- get_decennial(geography = "tract", variables = "P007003", year = 2000,
key = api_key, state = "MN", county = "Hennepin", geometry = TRUE)
armed <- get_decennial(geography = "tract", variables = "P038002", year = 2000,
state = "MN", county = "Hennepin", geometry = TRUE)
black90 <- get_decennial(geography = "tract", variables = "P0100002", year = 1990,
state = "IL", county = "Cook", geometry = TRUE)
ny <- get_decennial(geography = "county", variables = "P0100002", year = 1990,
state = "NY", geometry = TRUE)
vars <- c("P0100001", "P0100002", "P0100004", "P0080001")
vars10 <- c("P0050003", "P0050004", "P0050006", "P0040003")
race10 <- get_decennial(geography = "tract", variables = vars10, year = 2010,
key = api_key, state = "IL",
geometry = TRUE, summary_var = "P0010001")
il <- get_decennial(geography = "county", variables = vars10, year = 2010,
key = api_key, state = "IL", geometry = TRUE)
ggplot(il, aes(fill = value, color = value)) +
geom_sf() +
facet_wrap(~variable)
tidy <- tarr %>%
mutate(GEOID = paste0(state, county, tract)) %>%
select(GEOID, B19013_001E, B19013_001M) %>%
gather(key = variable, value = value, -GEOID) %>%
separate(variable, into = c("variable", "type"), sep = -2) %>%
mutate(type = ifelse(type == "E", "estimate", "moe")) %>%
spread(type, value)
tr <- tracts("TX", "Tarrant", cb = TRUE, class = "sf")
tidy2 <- left_join(tidy, tr, by = "GEOID")
library(tidycensus)
library(tidyverse)
library(sf)
library(viridis)
get_acs(geography = "tract", variables = "B19013_001E",
key = api_key, state = "IL", county = "Cook",
geometry = TRUE) %>%
rename(hhincome = B19013_001E) %>%
ggplot() +
geom_sf(aes(fill = hhincome,
color = hhincome)) +
coord_sf(crs = 26916) +
scale_fill_viridis() +
scale_color_viridis()
tarr %>%
st_transform(26914) %>%
ggplot() +
geom_sf(aes(fill = B19013_001E)) +
scale_fill_viridis()
df <- census(geography = "tract", variables = c("P0010001", "P0030001"),
key = api_key, state = "OR", county = "Benton")
black00 <- census(geography = "tract", variables = "P007003", year = 2000,
key = api_key, state = "MN", county = "Hennepin")
slovak90 <- census(geography = "tract", variables = "P0350025", year = 1990,
sumfile = "sf3", key = api_key, state = "NY", county = "Queens")
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