final

The graphs and plots below shows the changes pertaining to the boarders of Tennessee U.S House of Representatives in 2022 were massivly affected by the white and minority groups in each district.

I do suspect that the information given does in fact display the racial gerrymandering in 2022 based on the charts and plots given.

## `geom_smooth()` using formula = 'y ~ x'

Code:

# Installing and loading required packages

if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidycensus")) install.packages("tidycensus")
if (!require("sf")) install.packages("sf")
if (!require("mapview")) install.packages("mapview")
if (!require("leaflet")) install.packages("leaflet")
if (!require("leaflet.extras2")) install.packages("leaflet.extras2")

library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(leaflet)
library(leafpop)

options(scipen = 999)

# Transmitting API key

census_api_key("494018a70f114d4f76b10537730ccc9c7dbfe36b")

# Fetching ACS codebooks

ProfileTables <- load_variables(2021, "acs5/profile", cache = TRUE)

# Specifying target variables

VariableList = 
  c(Estimate_ = "DP05_0037P")

# Fetching the 2021 data

mydata <- get_acs(
  geography = "congressional district",
  state = c("TN"),
  variables = VariableList,
  year = 2021,
  survey = "acs1",
  output = "wide",
  geometry = TRUE)

mydata <- rename(mydata, Area = NAME,
                 Estimate = Estimate_E,
                 Range = Estimate_M)

# Mapping the 2021 data

Map2021 <- mapview(
  mydata,
  zcol = "Estimate",
  layer.name = "Pct. White",
  popup = popupTable(
    mydata,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("Area", "Estimate", "Range")
  )
)

Map2021

# Plotting the 2021 data

mygraph2021 <- ggplot(mydata, aes(x = Estimate, y = reorder(Area, Estimate))) + 
  geom_errorbarh(aes(xmin = Estimate - Range, xmax = Estimate + Range)) + 
  geom_point(size = 3, color = "#099d91") + 
  theme_minimal(base_size = 12.5) +
  labs(title = "Estimates by area", 
       subtitle = "Brackets show error margins.", 
       x = "Pct. white in 2021", 
       y = "")

mygraph2021

# Fetching the 2022 data

mydata <- get_acs(
  geography = "congressional district",
  state = c("TN"),
  variables = VariableList,
  year = 2022,
  survey = "acs1",
  output = "wide",
  geometry = TRUE)

mydata <- rename(mydata, Area = NAME,
                 Estimate = Estimate_E,
                 Range = Estimate_M)

# Mapping the 2022 data

Map2022 <- mapview(
  mydata,
  zcol = "Estimate",
  layer.name = "Pct. White",
  popup = popupTable(
    mydata,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("Area", "Estimate", "Range")
  )
)

Map2022

# Plotting the 2022 data

mygraph2022 <- ggplot(mydata, aes(x = Estimate, y = reorder(Area, Estimate))) + 
  geom_errorbarh(aes(xmin = Estimate - Range, xmax = Estimate + Range)) + 
  geom_point(size = 3, color = "#099d91") + 
  theme_minimal(base_size = 12.5) +
  labs(title = "Estimates by area", 
       subtitle = "Brackets show error margins.", 
       x = "Pct. white in 2022", 
       y = "")

mygraph2022

# Reading the data

Davidson2024 <- read.csv("https://github.com/drkblake/Data/raw/refs/heads/main/Davidson2024.csv")

# Plotting the support by minority data

ggplot(Davidson2024, aes(x = Pct_Nonwhite,
                         y = Pct_Harris,
                         color = Winner)) +
  geom_point() +
  geom_smooth(method = "lm",
              se = FALSE) +
  labs(x = "Pct. minority",
       y = "Pct. Harris",
       color = "Winner",
       title = "Support for Harris, by percent minority, 2024") +
  scale_color_brewer(palette = "Dark2")