Homework 5

Map Visualization with ggplot

Author

Byeong-Hak Choe

Published

May 3, 2026

Modified

May 3, 2026

πŸ“Œ Directions

  • Submit one Quarto document (.qmd) to Brightspace:

    • danl-310-hw5-LASTNAME-FIRSTNAME.qmd
      (e.g., danl-310-hw5-choe-byeonghak.qmd)

  • Due: April 27, 2026, 11:59 P.M. (ET)


βœ… Setup

library(tidyverse)
library(sf)
library(tigris)
library(scales)
library(ggthemes)
library(hrbrthemes)
library(rmarkdown)



Question 1

The following data is for Question 1:

election_panel <- read_csv(
  'https://bcdanl.github.io/data/election_panel_2000_2024.csv')
  • Replicate the following map using socviz::county_map.
    • Do not use coord_map(projection = "albers", lat0 = 39, lat1 = 45).
    • Use the following colors:
      • β€œ#FFFFFF”, β€œ#211f82”, β€œgrey80”, β€œ#81ABD9”


county_map <- socviz::county_map
county_map <- county_map |> 
  mutate(id = as.integer(id))

election_panel <- election_panel |> 
  mutate(id = as.integer(id))

county_full <- county_map |> 
  left_join(election_panel, 
            by = "id")

county_full <- county_full |> 
  arrange(year, county_fips, order)

# Function to create na_map for each year
na_map <- function(yr){ 
  county_full_na <- county_full |> 
    filter(is.na(year)) |>  # Part of Alaska
    select(-year) |> 
    mutate( year = yr)
}

county_full_NAmap <- county_full

# Row-binding na_map(yr) to county_full_NAmap for each year
for (yr in as.numeric( levels( factor( county_full$year ) ) ) ){
  county_full_NAmap <- county_full_NAmap |> 
    rbind( na_map(yr) )
}
# Also, try it with 
# (1) data = county_full
# (2) data = county_full |> filter(!is.na(year))
# (3) data = county_full_NAmap

p1 <- ggplot(data = county_full_NAmap |> filter(!is.na(year)),
             mapping = aes(x = long, y = lat, group = group, 
                           fill = pct_DEMOCRAT )) + 
  geom_polygon(color = "grey60", 
               linewidth = 0.025) 

q <- quantile(county_full$pct_DEMOCRAT, 
              probs = c(0, 0.25, 0.5, 0.75, 1), 
              na.rm = TRUE)


p2 <- p1 + 
  scale_fill_gradient( 
    low = '#FFFFFF',  # transparent white
    high = '#211f82',  # from party_colors for DEM
    na.value = "grey80",
    breaks = q,
    labels = c(paste0(round(q[1], 1), "\n(Min)"),
               paste0(round(q[2], 1), "\n(25th)"),
               paste0(round(q[3], 1), "\n(Median)"),
               paste0(round(q[4], 1), "\n(75th)"),
               paste0(round(q[5], 1), "\n(Max)")
    )
  ) 

p2 + labs(fill = "Percent\nDemocrat",
          title = "U.S. Presidential Elections, 2000-2024",
          subtitle = "Votes toward Democratic Presidential Candidates") +
  facet_wrap(.~ year, ncol = 3) +
  guides(fill = guide_colourbar(direction = "horizontal", barwidth = 20,
                                title.hjust = -1, title.vjust = 1)) +
  theme_map() + 
  theme(plot.margin = unit( c(1, 1, 4, 0.5), "cm"),
        plot.title = element_text(size = rel(2),
                                  hjust = .5,
                                  face = 'bold'),
        plot.subtitle = element_text(size = rel(1.5),
                                     hjust = .5,
                                     face = 'italic'),
        legend.title = element_text(face = 'italic',
                                    margin = margin(r = 10)),
        legend.position = c(0.5, -.15),
        legend.box.margin = margin(-200,0,0,0),
        strip.background = element_rect(fill = "#2e74c0", 
                                        color = "black", size = .1),
        strip.text = element_text(face = 'bold',
                                  size = rel(1.5))
  )



Question 2

The following data is for Question 2:

ny_income <- read_csv("https://bcdanl.github.io/data/NY_pinc_wide.csv")

p_title <- "Personal Income by County in New York"
p_caption <- "Source: U.S. Bureau of Economic Analysis (https://www.bea.gov/data/income-saving/personal-income-by-county)"
  • Replicate the following NY map using ny_counties_sf, geom_sf(), and scale_fill_viridis_c(trans = "log10"):
ny_counties_sf <- counties(state = "NY", year = 2024, cb = TRUE) |>
  st_as_sf()

This code downloads the county boundary map for New York State and stores it as an sf object, which is a spatial data format commonly used in R for mapping and geographic analysis.

  • counties(state = "NY", year = 2024, cb = TRUE)
    • This uses the counties() function from the tigris package.
    • state = "NY" tells R to download county boundaries for New York.
    • year = 2024 requests the 2024 version of the boundary file.
    • cb = TRUE asks for the generalized cartographic boundary file, which is simplified and lighter than the full detailed shapefile. This is usually better for plotting maps.
  • st_as_sf()
    • This converts the result into an sf object.
    • An sf object is a spatial data frame, meaning it stores both:
      • regular tabular variables, and
      • geometry information for map shapes.


options(tigris_use_cache = TRUE)

# Read the income data
ny_income <- readr::read_csv("https://bcdanl.github.io/data/NY_pinc_wide.csv")

# Keep county rows only and clean county names
ny_income_long <- ny_income |>
  filter(fips != 36000) |>
  pivot_longer(cols = pincp1969:pincp2024,
               values_to = "income",
               names_to = "year") |> 
  mutate(year = str_replace(year, "pincp", ""),
         year = as.integer(year)) |> 
  filter(year >= 2019) |> 
  mutate(fips = as.character(fips))

# Download NY county geometries
ny_counties_sf <- counties(state = "NY", year = 2024, cb = TRUE) |>
  st_as_sf() |>
  mutate(
    fips = GEOID,
    county_name_map = NAME
  )

# Join map data with income data
ny_map <- ny_counties_sf |>
  left_join(ny_income_long, by = "fips")


ny_map |> 
  ggplot() +
  geom_sf(aes(fill = income), color = "white", linewidth = 0.2) +
  facet_wrap(~year) +
  scale_fill_viridis_c(
    trans = "log10",
    breaks = seq(50000, 200000, 25000),
    labels = dollar_format(),
    option = "H",
    na.value = "grey90",
    name = "Income\n(log-10 scale)"
  ) +
  labs(
    title = "Personal Income by County in New York",
    caption = "Source: U.S. Bureau of Economic Analysis (https://www.bea.gov/data/income-saving/personal-income-by-county)"
  ) +
  guides(
    fill = guide_colorbar(barheight = 40)
  ) +
  hrbrthemes::theme_ipsum(base_size = 13) +
  theme(
    panel.grid.major = element_blank(),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    legend.box.margin = margin(b = 70)
  )

βœ… End of Homework 5

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