prb-demo.R

library(tidycensus)
library(tidyverse)

# Get the data
income_data <- get_acs(
  geography = "county",
  variables = "B19013_001",
  state = "ME",
  year = 2020,
  geometry = TRUE
)

# Let's take a look at the data structure
income_data

# A first plot
ggplot(income_data, aes(x = estimate, y = NAME)) +
  geom_point()

# Let's step through some improvements:
library(scales)

ggplot(income_data, aes(x = estimate, y = reorder(NAME, estimate))) +
  geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe)) +
  geom_point(size = 3, color = "darkgreen") +
  theme_minimal(base_size = 12.5) +
  labs(title = "Median household income",
       subtitle = "Counties in Maine",
       x = "2016-2020 ACS estimate",
       y = "") +
  scale_x_continuous(labels = label_dollar()) +
  scale_y_discrete(labels = ~word(.x, sep = " County"))

# Want a map? Change the geom!
ggplot(income_data, aes(fill = estimate)) +
  geom_sf() +
  theme_void(base_size = 12.5) +
  labs(fill = "2016-2020\nACS estimate") +
  scale_fill_distiller(palette = "Greens", direction = 1,
                       labels = label_dollar())


# What if I don't know much about Maine?
# R can bring charts and technologies together!
library(ggiraph)
library(patchwork)

income_map <- ggplot(income_data, aes(fill = estimate)) +
  geom_sf_interactive(aes(data_id = GEOID)) +
  scale_fill_distiller(palette = "Greens",
                       direction = 1,
                       guide = "none") +
  theme_void()

income_plot <- ggplot(income_data, aes(x = estimate,
                                       y = reorder(NAME, estimate),
                                       fill = estimate)) +
  geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe)) +
  geom_point_interactive(color = "black", size = 4, shape = 21,
                         aes(data_id = GEOID)) +
  scale_fill_distiller(palette = "Greens", direction = 1,
                       labels = label_dollar()) +
  scale_x_continuous(labels = label_dollar()) +
  scale_y_discrete(labels = ~word(.x, sep = " County")) +
  labs(title = "Household income by county in Maine",
       subtitle = "2016-2020 American Community Survey",
       y = "",
       x = "ACS estimate (bars represent margin of error)",
       fill = "ACS estimate") +
  theme_minimal(base_size = 14)

girafe(ggobj = income_map + income_plot, width_svg = 10, height_svg = 5) %>%
  girafe_options(opts_hover(css = "fill:cyan;"))