What Influences The Occurrence of Violent Crimes in the United States? (Code)

library(readr)
US_Crimedata <- read_csv("C:/Users/Nusrat/Desktop/SOC 712 - Extra Credit HW/US_Crimedata.csv")
US_ACSdata <- read_csv("C:/Users/Nusrat/Desktop/SOC 712 - Extra Credit HW/US_ACSdata.csv")

library(dplyr)
US_ACS_Crime_merged <- merge(US_Crimedata, US_ACSdata, by = "Geo_FIPS")

US_ACS_Crime_merged$VC <- US_ACS_Crime_merged$SE_T004_001
US_ACS_Crime_merged$Population <- US_ACS_Crime_merged$SE_T001_001
US_ACS_Crime_merged$White <- US_ACS_Crime_merged$SE_A03001_002
US_ACS_Crime_merged$Unemployed <- US_ACS_Crime_merged$SE_A17005_003
US_ACS_Crime_merged$Living_in_Poverty <- US_ACS_Crime_merged$SE_A13003B_002

US_ACS_Crime <- select(US_ACS_Crime_merged, Geo_FIPS, Geo_Name, Population, VC, White, Unemployed, Living_in_Poverty)

library(Zelig)
library(ZeligChoice)
library(survival)
Model1 <- zelig(VC ~ Population + Unemployed + Living_in_Poverty, model = "poisson", data = US_ACS_Crime, cite = F)
summary(Model1)
Model2 <- zelig(VC ~ Population + Unemployed + Living_in_Poverty + White, model = "poisson", data = US_ACS_Crime, cite = F)
summary(Model2)
Model3 <- zelig(VC ~ Population + Unemployed * Living_in_Poverty + White, model = "poisson", data = US_ACS_Crime, cite = F)
summary(Model3)

library(texreg)

## Version:  1.36.23
## Date:     2017-03-03
## Author:   Philip Leifeld (University of Glasgow)
## 
## Please cite the JSS article in your publications -- see citation("texreg").

screenreg(list(Model1, Model2, Model3), doctype = FALSE)

library(ggplot2)
ggplot(US_ACS_Crime, aes(x = Unemployed, y = VC)) +
    geom_smooth(
      col = "mediumblue",
        se = TRUE,
        size = 2) + labs(title = "Violent Crime and Unemployment", y = "Total Violent Crime Reported", x = "Unemployed Population (16 Years and over)")

library(ggplot2)
ggplot(US_ACS_Crime, aes(x = Living_in_Poverty, y = VC)) +
    geom_smooth(
        col = "green4",
        se = TRUE,
        size = 2)  + labs(title = "Violent Crime and Poverty",y = "Total Violent Crime Reported", x = "Population Living under Poverty Threshold")

library(ggplot2)
ggplot(US_ACS_Crime, aes(x = White, y = VC)) +
    geom_smooth(
        col = "red3",
        se = TRUE,
        size = 2)  + labs(title = "Violent Crime and White Population",y = "Total Violent Crime Reported", x = "White Population")

library(readr)
US_Crimedata <- read_csv("C:/Users/Nusrat/Desktop/SOC 712 - Extra Credit HW/US_Crimedata.csv")

library(tidyverse)
library(tmap)
library(tigris)
library(spdep)
library(sf)
library(tmaptools)
options(tigris_class = "sf")
t_county <- counties(cb = TRUE)

US_Crimedata <- US_Crimedata %>% 
  mutate(fips = as.integer(Geo_FIPS))
t_county <- t_county %>% 
  mutate(fips = as.integer(GEOID)) 
comb_data <- t_county %>% 
  left_join(US_Crimedata, by = "fips")
comb_data_sub <- subset(comb_data, STATEFP != "02") %>%
                 subset(STATEFP != "02") %>% 
                 subset(STATEFP != "15") %>% 
                 subset(STATEFP != "60") %>% 
                 subset(STATEFP != "66") %>% 
                 subset(STATEFP != "69") %>% 
                 subset(STATEFP != "72") %>% 
                 subset(STATEFP != "78")

tm_shape(comb_data_sub, projection = 2163) + tm_polygons("SE_T001_001", palette = "YlOrRd", border.col = "grey", border.alpha = .4) + tm_shape(us_states) + tm_borders(lwd = .36, col = "black", alpha = 1)

tm_shape(comb_data_sub, projection = 2163) + tm_polygons("SE_T004_001", palette = "OrRd", border.col = "grey", border.alpha = .4) + tm_shape(us_states) + tm_borders(lwd = .36, col = "black", alpha = 1)

What Influences The Occurrence of Violent Crimes in the United States? (Code)

Nusrat Hossain