12.5 Application: Voter Identification Laws | Data Science for Political Science

12.5 Application: Voter Identification Laws

According to the NCSL, 35 states have laws requesting or requiring voters to show some form of identification at the polls.

The presence of voter ID laws and the strictness of these laws has accelerated over the past decade.
We will look at the geography of these laws to see if there are regional or other political patterns to these

NCSL

12.5.1 Using the `%in%` function

Detecting if something is contained within a vector: The function %in% asks: is this contained in the vector? Yes/No

"new jersey" %in% c("new jersey", "california", "nebraska")

## [1] TRUE

"florida" %in% c("new jersey", "california", "nebraska")

## [1] FALSE

(! "florida" %in% c("new jersey", "california", "nebraska")) # not in

## [1] TRUE

We will augment our map data with a new variable that classifies states according to their voter ID laws.

usmap <- map_data("state")

head(usmap)

##        long      lat group order  region subregion
## 1 -87.46201 30.38968     1     1 alabama      <NA>
## 2 -87.48493 30.37249     1     2 alabama      <NA>
## 3 -87.52503 30.37249     1     3 alabama      <NA>
## 4 -87.53076 30.33239     1     4 alabama      <NA>
## 5 -87.57087 30.32665     1     5 alabama      <NA>
## 6 -87.58806 30.32665     1     6 alabama      <NA>

## create variable as NA
usmap$photoidlaws <- NA

## Fill in vaariable with state categories
usmap$photoidlaws[usmap$region %in% c("arkansas","georgia", "indiana", "kansas","mississippi", "tennessee", "wisconsin")] <- "Strict Photo ID"

usmap$photoidlaws[usmap$region %in% c("alabama", "florida", "idaho", "louisiana", 
                                      "michigan", "montana", "rhode island", "south carolina", 
                                      "south dakota", "texas")] <- "Non-Strict Photo ID"

usmap$photoidlaws[usmap$region %in% c("arizona", "north dakota", 
                                      "ohio", "wyoming")]<- "Strict Non-Photo ID"

usmap$photoidlaws[usmap$region %in% c("alaska", "colorado", "connecticut", 
                                      "delaware", "hawaii", "iowa", "kentucky", "missouri", 
                                      "new hampshire", "oklahoma", "utah", "virginia", 
                                      "washington", "west virginia")] <- "Non-Strict Non-Photo ID"

usmap$photoidlaws[usmap$region %in% c("oregon", "nevada","california", 
                                      "new mexico", "nebraska", "minnesota", 
                                      "illinois", "north carolina", "pennsylvania", 
                                      "new york", "new jersey", "massachusetts",
                                      "vermont", "maryland", "north carolina", 
                                      "district of columbia", 
                                      "maine")] <- "No Document Required"

## Make it a factor categorical variable
usmap$photoidlaws <- as.factor(usmap$photoidlaws)

Let’s create a map of the U.S. We will then annotate the map with information about voter identification laws.

ggplot()+
  geom_polygon(data=usmap, aes(x=long, y=lat, group=group, 
                               fill=photoidlaws),
               colour="black")+
  ## palette lets you pick a color scheme without specifics
  scale_fill_brewer(palette="Greens", name="Photo ID Laws")+

  theme_void() +
  ggtitle("Map of U.S. Voter ID Laws")+
  coord_quickmap()