title: “Final” author: “Steven Calcutt” date: “12/11/2019” output: html_document

Introduction:  The topic of voting rights for felons has become a major issue in the state of Florida. With this passing of Amendment 4 in 2018 which restored ex-felons their voting rights the Florida electorate could begin shifting in 2020. As voting laws across the country continue to shift, it is important for campaigns to understand how to expect these new voters in the electorate might vote come election day. The purpose of this study is to study the state of North Carolina, a state that allows ex-felons to vote as soon as their sentence and any parole or probation period has come to an end. Using this voter file I will be able to see the race, gender, age, and party ID of convicted felons in the state of North Carolina. To control for this group I used the general voter file of every registered voter in the state.  

Two states (Maine and Vermont) allow for convicted felons to vote while incarcerated. Eighteen states automatically restore voting rights upon release from prison. Three states restore voting rights automatically once the felon is released from prison and discharged from parole (probationers can vote). Twenty-one states automatically restore voting rights upon completion of their sentence, including prison, parole, and probation. In seven states, voting rights restoration is dependent type of conviction or, in some cases, the outcome of an individual petition to the state government. In only one state (Kentucky) can voting rights only be restored through an individual petition or application to the governor.

Below are the packages that I used for this project and how I coded the general population in the North Carolina voter file.

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
working.dir <- ("~/Downloads/")
ncvoter_Statewide <- read.delim("~/Downloads/ncvoter_Statewide.txt")
Voters.NC <- paste(working.dir, "/ncvoter_Statewide.txt", sep = "")
ncvoter_Statewide <- read.delim(Voters.NC, sep = "")

Below is how I coded the people convicted of a felony in North Carolina. I sorted out those convicted of a felony in the state and created its own subset voter file.

justFelons <-
  ncvoter_Statewide[ncvoter_Statewide$voter_status_reason_desc == "FELONY CONVICTION",]
table(ncvoter_Statewide$race_code)
## 
##               A       B       I       M       O       U       W 
##       3  101560 1685847   60072   50173  205254  413531 5230705

Above is just an outline of the state of North Carolina’s general population demographics.

ggplot(justFelons, aes(x=race_code)) +
  geom_bar(stat = "count")

It is striking when reviewing the data from North Carolianians who are currently incarcerated. It is heavily dominated by African Americans (8561) compared to whites (6414). While this is only difference of roughly 2,000 people, it is important to remember the previous graph which showed the state is heavily dominated by white people.

ggplot(ncvoter_Statewide, aes(x=gender_code)) +
  geom_bar(stat = "count")

The state of North Carolina has more females (4,003,781) than males (3,447,029) and has 296,313 citizens who do not identify as a gender. This does not come as a surprise, as this falls in line with gender trends across the country.

ggplot(justFelons, aes(x=gender_code)) +
  geom_bar(stat = "count")

It is striking, however, when looking at the gender numbers of those incarcerated in the state of North Carolina. Of those incarcerated: 12,148 are male, 3,665 are female, and 428 do not identify as a gender. Again, much like with the graphs on Race, this does not come as a surprise when you understand the trends of those incarcerated, however, it is very sobering to see it with the data graphed out.

ggplot(ncvoter_Statewide, aes(x=birth_age)) +
  geom_bar(stat = "count")

This graph just shows the basic age breakdown of the state. Like most voter files there is an extreme outlier. This just means the individual has not been properly taken off of the voter file yet.

ggplot(justFelons, aes(x=birth_age)) +
  geom_bar(stat = "count")

This shows the breakdown of felons in the state. It is striking to see that the majority of those incarcerated are between the ages of 25 and 50. Granted, this is not surprising, but it is no less sobering.

ggplot(ncvoter_Statewide, aes(x=party_cd)) +
  geom_bar(stat = "count")

The state of North Carolina is a swing state so it is not surprising to see Democrats (2,920,989), Republicans (2,308,473), and Independents (2,470,201) are all rather close to each other.

ggplot(justFelons, aes(x=party_cd)) +
  geom_bar(stat = "count")