Higher Rates Of Hate Crimes Are Tied To Income Inequality

The raw data behind the story "Higher Rates Of Hate Crimes Are Tied To Income Inequality" https://fivethirtyeight.com/features/higher-rates-of-hate-crimes-are-tied-to-income-inequality/.

hate_crimes

Format

A data frame with 51 rows representing US states and DC and 13 variables:

state: State name
state_abbrev: State abbreviation
median_house_inc: Median household income, 2016
share_unemp_seas: Share of the population that is unemployed (seasonally adjusted), Sept. 2016
share_pop_metro: Share of the population that lives in metropolitan areas, 2015
share_pop_hs: Share of adults 25 and older with a high-school degree, 2009
share_non_citizen: Share of the population that are not U.S. citizens, 2015
share_white_poverty: Share of white residents who are living in poverty, 2015
gini_index: Gini Index, 2015
share_non_white: Share of the population that is not white, 2015
share_vote_trump: Share of 2016 U.S. presidential voters who voted for Donald Trump
hate_crimes_per_100k_splc: Hate crimes per 100,000 population, Southern Poverty Law Center, Nov. 9-18, 2016
avg_hatecrimes_per_100k_fbi: Average annual hate crimes per 100,000 population, FBI, 2010-2015

Source

See https://github.com/fivethirtyeight/data/tree/master/hate-crimes

Examples

library(ggplot2)
ggplot(hate_crimes, aes(x = share_vote_trump, y = hate_crimes_per_100k_splc)) +
  geom_text(aes(label = state_abbrev)) +
  geom_smooth(se = FALSE, method = "lm") +
  labs(x = "Proportion of votes for Donald Trump",
       y = "Hate crimes per 100k during Nov 9-18, 2016 (SPLC)",
       title = "Relationship between Trump support & hate crimes")
#> `geom_smooth()` using formula 'y ~ x'
#> Warning: Removed 4 rows containing non-finite values (stat_smooth).
#> Warning: Removed 4 rows containing missing values (geom_text).