census data quickly
2025-08-10
Census Data
dp_vars_expanded <- c(
# DP02 - Social Characteristics
"pct_bachelor_degree_plus_adults25plus" = "DP02_0068P",
"pct_high_school_graduate_adults25plus" = "DP02_0067P",
"pct_english_only_home_pop5plus" = "DP02_0113P",
"pct_spanish_home_pop5plus" = "DP02_0116P",
"pct_broadband_subscription_households" = "DP02_0154P",
# DP03 - Economic Characteristics
"per_capita_income" = "DP03_0088",
"median_household_income" = "DP03_0062",
"pct_below_poverty_all_people" = "DP03_0128P",
"pct_unemployed_labor_force" = "DP03_0009P",
"pct_no_vehicle_households" = "DP03_0057P",
"pct_gov_employment_employed16plus" = "DP03_0042P",
"pct_snap_benefits_households" = "DP03_0074P",
# DP04 - Housing Characteristics
"pct_owner_occupied_housing_units" = "DP04_0046P",
# DP05 - Demographics
"pct_under_5_total_pop" = "DP05_0005P",
"pct_under_18_total_pop" = "DP05_0019P",
"pct_over_65_total_pop" = "DP05_0024P",
# Race/Ethnicity
"pct_hispanic_any_race_total_pop" = "DP05_0076P",
"pct_white_non_hispanic_total_pop" = "DP05_0082P",
"pct_black_non_hispanic_total_pop" = "DP05_0083P",
"pct_asian_non_hispanic_total_pop" = "DP05_0085P",
"pct_native_american_non_hispanic_total_pop" = "DP05_0084P"
)# Pull data for all three geographies
nm_state <- tidycensus::get_acs(
geography = "state",
variables = dp_vars_expanded,
year = 2023,
survey = "acs5",
state = "NM"
)
nm_senate <- tidycensus::get_acs(
geography = "state legislative district (upper chamber)",
variables = dp_vars_expanded,
year = 2023,
survey = "acs5",
state = "NM"
)
nm_house <- tidycensus::get_acs(
geography = "state legislative district (lower chamber)",
variables = dp_vars_expanded,
year = 2023,
survey = "acs5",
state = "NM"
)
# Optional: Combine all into one dataset with geography type
all_nm_data <- bind_rows(
nm_state |> mutate(geo_type = "State"),
nm_senate |> mutate(geo_type = "Senate District"),
nm_house |> mutate(geo_type = "House District")
)nm_house2 <- tidycensus::get_acs(
geography = "state legislative district (lower chamber)",
variables = dp_vars_expanded,
year = 2023,
survey = "acs5",
state = "NM",
geometry = TRUE
)## | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 16% | |============ | 17% | |============= | 18% | |============== | 19% | |============== | 21% | |=============== | 22% | |================= | 25% | |================== | 26% | |======================= | 32% | |==================================== | 52% | |===================================== | 53% | |====================================== | 54% | |======================================= | 56% | |======================================== | 57% | |=========================================== | 61% | |============================================ | 62% | |============================================ | 63% | |============================================= | 65% | |============================================== | 66% | |=============================================== | 67% | |=================================================== | 72% | |=================================================== | 74% | |======================================================= | 79% | |========================================================== | 83% | |============================================================= | 88% | |================================================================= | 93% | |===================================================================== | 98% | |======================================================================| 100%# Choose a good projection for NM — Albers Equal Area (NAD83)
# EPSG: 5070 works well for US-wide data, but NM-specific CRS is EPSG: 6423
nm_house2 <- sf::st_transform(nm_house2, crs = 6423)
# Simple choropleth example — median household income
ggplot(
nm_house2 |>
subset(variable == "pct_spanish_home_pop5plus") |>
sf::st_transform(32113)
) +
geom_sf(aes(fill = estimate), color = "grey60", linewidth = 0.2) +
scale_fill_distiller(palette = "Greens", direction = 1, na.value = "grey85") +
theme_minimal(base_size = 12) +
labs(
title = "Spanish Spoken at Home (Population 5+)",
subtitle = "New Mexico House Districts — ACS 2019–2023",
fill = "%"
)
library(tigris)
options(tigris_use_cache = TRUE)
# Spanish spoken at home, reprojected
abq_data <- nm_house2 |>
subset(variable == "pct_spanish_home_pop5plus") |>
sf::st_transform(32113)
# Bernalillo County for zoom extent
bernalillo <- tigris::counties(state = "NM", year = 2023) |>
subset(NAME == "Bernalillo") |>
sf::st_transform(32113)
abq_bbox <- sf::st_bbox(bernalillo)
# Statewide primary roads, clipped to NM
nm_boundary <- tigris::states(year = 2023) |>
subset(STUSPS == "NM") |>
sf::st_transform(32113)
major_roads_nm <- tigris::primary_roads(year = 2023) |>
sf::st_transform(32113) |>
sf::st_intersection(nm_boundary)
# Centroids for labeling
label_points <- sf::st_centroid(abq_data)# Plot ABQ zoom with labels
ggplot() +
geom_sf(data = abq_data, aes(fill = estimate), color = "grey60", linewidth = 0.2) +
geom_sf(data = major_roads_nm, color = "grey30", linewidth = 0.5, alpha = 0.9) +
geom_sf_text(
data = label_points,
aes(label = sub(".*District (\\d+).*", "\\1", NAME)),
size = 2,
color = "black",
fontface = "bold"
) +
scale_fill_distiller(palette = "Greens", direction = 1, na.value = "grey85") +
coord_sf(
xlim = c(abq_bbox["xmin"], abq_bbox["xmax"]),
ylim = c(abq_bbox["ymin"], abq_bbox["ymax"]),
expand = FALSE
) +
theme_minimal(base_size = 12) +
labs(
title = "Spanish Spoken at Home (Population 5+)",
subtitle = "Albuquerque Area — ACS 2019–2023",
fill = "%",
caption = "Labels show House District numbers"
)