The source for the data i have chosen is the Texas Computerized Criminal History (CCH) and the classifications that are used come from Texas Department of Public Safety (DPS). Although the data-sets come from research conducted at the University of Madison Wisconsin and authored by Michael Light, Jingying He, Jason Robey. The main variables i will be using are: year, category (different crimes), citizen_crime_rate (US citizen crime rate), immigrant_crime_rate (legal immigrant and naturalized citizens crime rate), illegal2_immigrants_crime_rate (undocumented immigrant crime rate), total_crime_rate (total crime rate). The questions i would like to answer through this data is whether or not undocumented immigrants commit more crime (more specifically sexual assault, homicide and drug related offenses). The reason i chose this topic is because of the incessant anti-immigrant posturing from the US conservative camp. Specifically it is this quote that drove me to use this data: “When Mexico sends its people, they’re not sending their best. […] They’re sending people that have lots of problems, and they’re bringing those problems with us. They’re bringing drugs. They’re bringing crime. They’re rapists. And some, I assume, are good people”. The Texas CCH collects its data through police agency reports.
Libraries & Data-Sets
library(haven)library(tidyverse)
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✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
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library(highcharter)
Warning: package 'highcharter' was built under R version 4.3.3
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as.zoo.data.frame zoo
library(gganimate)
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library(viridis)
Loading required package: viridisLite
library(gifski)
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library(wesanderson)
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library(ggpattern)
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library(GGally)
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+.gg ggplot2
subset_data <- big_new[, 3:8]# Use ggpairs with the subsetted dataggpairs(subset_data)
subset_data1 <- big_new[, 5:8]# Use ggpairs with the subsetted dataggpairs(subset_data1)
subset_data2 <- big_new[, 8:10]# Use ggpairs with the subsetted dataggpairs(subset_data2)
cor( big_new$immigrant_crime_rate, big_new$citizen_crime_rate, use ="complete.obs" ) #Provides the correlation Coefficient
[1] -0.2470176
fit1 <-lm( immigrant_crime_rate ~ citizen_crime_rate, data = big_new) #Fits the LR Modelsummary(fit1) # Summary of the model
Call:
lm(formula = immigrant_crime_rate ~ citizen_crime_rate, data = big_new)
Residuals:
1 2 3 4 5 6 7
17.1063 9.9632 -7.7148 -10.0210 0.7200 -9.3941 -0.6596
attr(,"label")
[1] "(firstnm) immi_rate_cit_violent"
attr(,"format.stata")
[1] "%9.0g"
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 269.2654 146.4648 1.838 0.125
citizen_crime_rate -0.3921 0.6878 -0.570 0.593
Residual standard error: 11.32 on 5 degrees of freedom
Multiple R-squared: 0.06102, Adjusted R-squared: -0.1268
F-statistic: 0.3249 on 1 and 5 DF, p-value: 0.5933
Analysis
ARS = 0.2243 The model explains ~22.43% of the total variation in the dependent variable, leaving ~77.57% of the variation unexplained.
Cor-Co:-0.2470176 A correlation coefficient of -0.2470176 suggests a weak negative correlation
P-Val: 0.5933 there is not enough evidence to reject the null hypothesis
\[Y_{i}=\beta_{0}+\beta_{1}X_{i}\] Where \(Y_{i}\) is the Immigrant Crime Rate of the \(i^{th}\) observation, \(\beta_{0}\) is the intercept, \(\beta_{1}\) is the slope, and \(X_{i}\) is the Citizen Crime Rate of the \(i^{th}\) observation. \[Y_{i}=269.265+(-0.3921)X_{i}\]
Visualizations
P1
df_new$immigration_type <-factor(df_new$immigration_type, levels =c("citizen_crime_rate", "immigrant_crime_rate", "illegal2_immigrants_crime_rate"))p.1<-ggplot(data = df_new, aes(x = immigration_type,y = number_of,fill = immigration_type),color ="black") +geom_col(lwd =2,width =1) +geom_text(aes(label = number_of),position =position_stack(vjust =0), hjust =-0.2,color ="black", size =3, family ="mono") +coord_flip() +facet_wrap( ~ category, ncol =2, dir ="v") +# Facet by the "category" variabletheme_linedraw() +labs( title ="Unveiling Crime Trends: Natives vs. Undocumented \n Year: 2018", # Labels titlecaption ="Source: Texas Computerized Criminal History (CCH)" ) +theme(axis.text =element_blank(), # Remove axis textaxis.title =element_blank(), # Remove axis labelsaxis.ticks =element_blank(), # Remove axis ticks aspect.ratio =0.285, # Made the overall size of the visualization smallerpanel.spacing.x =unit(8, "lines"),legend.position =c( .5, .53), # Changes legend positionlegend.direction ="vertical" ,legend.background =element_rect( color ="black", fill ="#24868eff" ), # legend backgroundlegend.box.background =element_rect( color ="#24868eff" ),legend.title =element_text( color ="white", face ="bold", family ="mono"),legend.text =element_text( family ="mono", face ="bold", size =10),plot.background =element_rect( fill ="#E5E4E2"),plot.title =element_text( family ="mono", # Changes font family of Titlesize =17, # Changes size of Titleface ="bold", # Boldens Titlehjust =0.5), # Centers the Title to the Plotsplot.caption =element_text( hjust =0.55, # Centers the caption to the plotsface ="italic" ), # Italicizes the captionpanel.background =element_rect(fill ="white"), # Change panel background colorstrip.background =element_rect( color ="black", fill ="#24868eff",linetype="solid"), # Colored the name platesstrip.text =element_text( family ="mono", # Changes font family of Panel Titlessize =12, # Changes size of Panel Titlesface ="bold")) +# Boldens Panel Titles) + scale_fill_manual(values =wes_palette("Zissou1", n =4),name ="Crime Rates \nPer 100,000",labels =c("US Citizens", "Legal \nImmigrants", "Undocumented \nImmigrants"))
Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.
p.1
This Visualization depicts crime rates (per 100,000) for 6 crimes major crimes. The crime rates are categorized by US citizens, legal immigrant, undocumented immigrant. From the graph we can discern that having a US citizenship makes you more likely to commit crime within the US. This factoid goes against popular right wing narratives about immigrant crime.
P3
colors <-c( "steelblue", "black", "red", "orange", "blue" ) #color choices for pointsmy_own_theme <-hc_theme(colors =c("red", "green", "blue"),chart =list(backgroundColor =NULL,divBackgroundImage ="https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExcTFwMDl6eXlqbWhuNmMwaW5nMTkxZGxqc3pueWd4Z3R3N29zNjF1ZyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/l1J3Ll1FWR0iqoeUo/giphy.gif" ),title =list(style =list(color ="#333333",fontFamily ="Lato" ) ),subtitle =list(style =list(color ="#666666",fontFamily ="Lato" ) ),legend =list(itemStyle =list(fontFamily ="Lato",color ="black" ),itemHoverStyle =list(color ="gray" ) ))highchart() |>hc_add_series(data = big_new$citizen_crime_rate, #adds US-Life Expectancy to chartname ="US Citizen Crime Rate",type ="area",yAxis =0) |>hc_add_series(data = big_new$total_crime_rate, #adds Vietnam-Life Expectancy to chartname ="Total Crime Rate",type ="area",yAxis =0) |>hc_add_series(data = big_new$immigrant_crime_rate, #adds Vietnam-Population to chartname ="Legal Immigrat Crime Rate",type ="area",yAxis =0) |>hc_add_series(data = big_new$illegal2_immigrants_crime_rate, #adds US-Population to chartname ="Undocumented Crime Rate",type ="area",yAxis =0) |>hc_xAxis(categories = big_new$year, # adds x-axistickInterval =1) |>hc_xAxis(title =list(text="Year")) |># adds x-axis titlehc_yAxis(title =list(text ="Crime Rates per 100,000"),min =50,max =300 ) |>hc_title(text ="Violent Crime Rate Disparities: A Disturbing Trend ") |># adds titlehc_subtitle(text ="Rates are per 100,000") |>hc_colors(colors) |>#adds colorshc_chart(style =list(fontFamily ="Georgia", # boldens and changes font to GeorgiafontWeight ="bold")) |>hc_legend(verticalAlign ="top", # places legend on top of chart and changes its orientation to horizontallayout ="horizontal") |>hc_add_theme(my_own_theme) # changes theme to gray scale
This graph compares Violent crime rates between US citizens, legal immigrant, undocumented immigrant and total. It seems that US citizens commit more violent crime than legal/naturalized immigrants and undocumented immigrants. The GIF in the background is suppost to represent the U.S border.
P2
p.2<-ggplot( data = Allmis_new, # Loaded union.unemp.2 into ggplotmapping =aes( x = year, # Applied year to the X-axisy = number_of , # Applied unemployment percentage to the Y-axisfill = category )) +# Applied Sex to fill in the areageom_area ( color =wes_palette( "GrandBudapest2", 1 ) ) +# Adds the area plot layerfacet_wrap( ~factor( immigration_type, levels =c( "citizen_crime_rate", "immigrant_crime_rate", "illegal2_immigrants_crime_rate", "total_crime_rate"))) +# levels allows the orderinglabs( title ="Misdemeaners V.S Felonies", # Labels titlex ="Year (2012-18)", # Labels x axisy ="Unemployment (Percent)", # Labels y axiscaption ="Source: Organisation for Economic Co-operation and Development (OECD)" ) +# Adds Sourcetheme_linedraw() +# sets the theme for the graphs, it is the reason they are darktheme(aspect.ratio =0.8, # Made the overall size of the visualization smalleraxis.title.x =element_text( size=14 ), # Changes size of X-axis Labelaxis.title.y =element_text( size=14 ), # Changes size of Y-axis Labelaxis.text =element_text( size =9 ), # changes axes text sizeslegend.background =element_blank(), # Makes the background of the legend box blanklegend.box.background =element_rect( color ="black" ),legend.position =c( .768, 0.305 ), # Changes legend positionlegend.title =element_text( size =8.5, face ="bold" ), # Changes the legend title text sizelegend.text=element_text( size =8.5 ),# Changes the legend text sizeplot.title =element_text( size =17, # Changes size of Titleface ="bold", # Boldens Titlehjust =0.5, ), # Centers the Title to the Plotsplot.caption =element_text( hjust =0.5, # Centers the caption to the plotsface ="italic" ), # Italicizes the captionpanel.spacing =unit( 1, "lines" ), # Spreads out the facets plotsstrip.background =element_rect( color ="black", fill =wes_palette( "Chevalier1" ), linetype="solid" )) # Colored the name plates above each graph with Wes Anderson color palletp.2
ggplot(data = df_new, aes(x = immigration_type, y = number_of, fill = immigration_type)) +geom_col() +coord_flip() +facet_grid(rows =vars(category)) +theme(axis.title.x=element_blank(),axis.text.x=element_blank(),axis.ticks.x=element_blank()) +theme_void()
Final Thoughts
Although it had been clear to me, prior to making these visualizations, that the popular right wing sentiment that immigrants are more than likely to commit more crime, use and sell more drugs, commit sexual crimes and homicides than the average red blooded american are all false red herrings brought about to galvanize the masses. These visualizations clearly show how US citizens, per-capita, commit more crime than undocumented and naturalized immigrants respectively. It also seems as though the more you assimilate into American culture the more likely you may be to commit said acts. This is to say that crime may be a systemic issue in the US and not one of state and non-state actors. In an article APnews by Will Weissert & Jill Colvin, the authors examines how former President Donald Trump’s rhetoric on immigration, characterized by fear-mongering and portraying migrants as criminals, has resonated with certain segments of the population. It goes on to discuss the use of alarming language and imagery to convey the perception of immigrants as a threat to American society. This work reflects my similar sentiments on the visualizations i have created. Overall, i feel i could have experimented a lot more with the visualization aspects and how i represented the data. I also feel, in retrospect, that i very much enjoy creating geo-spacial visualizations. Nevertheless, i very much enjoyed this project and learned some things along the way, most notably how to incorporate any GIF or image behind my visualizations in high charter.
