“Higher Rates Of Hate Crimes Are Tied To Income Inequality” https://fivethirtyeight.com/features/higher-rates-of-hate-crimes-are-tied-to-income-inequality/
This article describes hate crimes rate in states before and after election. It is being discussed that states with higher income inequality reports more hate crimes compare to other states with less socioeconomic differences. The data, which the article describes had some limitations, however, the idea of both data set used points similar outcomes what suggests that the finds are strong.
library(RCurl)
df <- read.csv("https://raw.githubusercontent.com/hrensimin05/Cuny_DataScience/master/hate_crimes.csv")
summary(df)## state median_household_income share_unemployed_seasonal
## Length:51 Min. :35521 Min. :0.02800
## Class :character 1st Qu.:48657 1st Qu.:0.04200
## Mode :character Median :54916 Median :0.05100
## Mean :55224 Mean :0.04957
## 3rd Qu.:60719 3rd Qu.:0.05750
## Max. :76165 Max. :0.07300
##
## share_population_in_metro_areas share_population_with_high_school_degree
## Min. :0.3100 Min. :0.7990
## 1st Qu.:0.6300 1st Qu.:0.8405
## Median :0.7900 Median :0.8740
## Mean :0.7502 Mean :0.8691
## 3rd Qu.:0.8950 3rd Qu.:0.8980
## Max. :1.0000 Max. :0.9180
##
## share_non_citizen share_white_poverty gini_index share_non_white
## Min. :0.01000 Min. :0.04000 Min. :0.4190 Min. :0.0600
## 1st Qu.:0.03000 1st Qu.:0.07500 1st Qu.:0.4400 1st Qu.:0.1950
## Median :0.04500 Median :0.09000 Median :0.4540 Median :0.2800
## Mean :0.05458 Mean :0.09176 Mean :0.4538 Mean :0.3157
## 3rd Qu.:0.08000 3rd Qu.:0.10000 3rd Qu.:0.4665 3rd Qu.:0.4200
## Max. :0.13000 Max. :0.17000 Max. :0.5320 Max. :0.8100
## NA's :3
## share_voters_voted_trump hate_crimes_per_100k_splc avg_hatecrimes_per_100k_fbi
## Min. :0.040 Min. :0.06745 Min. : 0.2669
## 1st Qu.:0.415 1st Qu.:0.14271 1st Qu.: 1.2931
## Median :0.490 Median :0.22620 Median : 1.9871
## Mean :0.490 Mean :0.30409 Mean : 2.3676
## 3rd Qu.:0.575 3rd Qu.:0.35694 3rd Qu.: 3.1843
## Max. :0.700 Max. :1.52230 Max. :10.9535
## NA's :4 NA's :1# The main dataset includes varieties of statistics and analytics related to every states like poverty and
# medium income of the state
# I will create a subset focused on highchool diploma rate vs median household income and hate crime - simplifying the data set
otherfactors <- subset(df, "share_population_with_high_school_degree" >0.798)
newlist <- c("state","median_household_income","avg_hatecrimes_per_100k_fbi")
newdata <- otherfactors[newlist]
head(newdata)## state median_household_income avg_hatecrimes_per_100k_fbi
## 1 Alabama 42278 1.8064105
## 2 Alaska 67629 1.6567001
## 3 Arizona 49254 3.4139280
## 4 Arkansas 44922 0.8692089
## 5 California 60487 2.3979859
## 6 Colorado 60940 2.8046888attach(newdata)
ndata<- newdata[order(median_household_income),]
head(ndata)## state median_household_income avg_hatecrimes_per_100k_fbi
## 25 Mississippi 35521 0.622746
## 49 West Virginia 39552 2.037054
## 1 Alabama 42278 1.806410
## 19 Louisiana 42406 1.341170
## 18 Kentucky 42786 4.207890
## 43 Tennessee 43716 3.136051tail(ndata)## state median_household_income avg_hatecrimes_per_100k_fbi
## 2 Alaska 67629 1.656700
## 9 District of Columbia 68277 10.953480
## 7 Connecticut 70161 3.772701
## 12 Hawaii 71223 NA
## 30 New Hampshire 73397 2.105989
## 21 Maryland 76165 1.324840In conclusion, as we could asses from the subset that provided data is strong and shows as correlation between households income and hate crime reported by fbi in our example, where lower median income shows higher average hate crime per 100k.