State-level data — state

Information about each state collected from both the official US Census website and from various other sources.

Usage

state_stats

Format

A data frame with 51 observations on the following 23 variables.

state: State name.
abbr: State abbreviation (e.g. "MN").
fips: FIPS code.
pop2010: Population in 2010.
pop2000: Population in 2000.
homeownership: Home ownership rate.
multiunit: Percent of living units that are in multi-unit structures.
income: Average income per capita.
med_income: Median household income.
poverty: Poverty rate.
fed_spend: Federal spending per capita.
land_area: Land area.
smoke: Percent of population that smokes.
murder: Murders per 100,000 people.
robbery: Robberies per 100,000.
agg_assault: Aggravated assaults per 100,000.
larceny: Larcenies per 100,000.
motor_theft: Vehicle theft per 100,000.
soc_sec: Percent of individuals collecting social security.
nuclear: Percent of power coming from nuclear sources.
coal: Percent of power coming from coal sources.
tr_deaths: Traffic deaths per 100,000.
tr_deaths_no_alc: Traffic deaths per 100,000 where alcohol was not a factor.
unempl: Unemployment rate (February 2012, preliminary).

Source

Census Quick Facts (no longer available as of 2020), InfoChimps (also no longer available as of 2020), National Highway Traffic Safety Administration (tr_deaths, tr_deaths_no_alc), Bureau of Labor Statistics (unempl).

Examples


library(ggplot2)
library(dplyr)
library(maps)

states_selected <- state_stats |>
  mutate(region = tolower(state)) |>
  select(region, unempl, murder, nuclear)

states_map <- map_data("state") |>
  inner_join(states_selected)
#> Joining with `by = join_by(region)`

# Unemployment map
ggplot(states_map, aes(map_id = region)) +
  geom_map(aes(fill = unempl), map = states_map) +
  expand_limits(x = states_map$long, y = states_map$lat) +
  scale_fill_viridis_c() +
  labs(x = "", y = "", fill = "Unemployment\n(%)")


# Murder rate map
states_map |>
  filter(region != "district of columbia") |>
  ggplot(aes(map_id = region)) +
  geom_map(aes(fill = murder), map = states_map) +
  expand_limits(x = states_map$long, y = states_map$lat) +
  scale_fill_viridis_c() +
  labs(x = "", y = "", fill = "Murders\nper 100k")


# Nuclear energy map
ggplot(states_map, aes(map_id = region)) +
  geom_map(aes(fill = nuclear), map = states_map) +
  expand_limits(x = states_map$long, y = states_map$lat) +
  scale_fill_viridis_c() +
  labs(x = "", y = "", fill = "Nuclear energy\n(%)")