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## Dorling Cartogram example with US Census data
## Requires you sign up for a free Census API key
## https://api.census.gov/data/key_signup.html
##
## Required packages
library(tidyverse)
library(tidycensus)
library(sf)
library(cartogram)
library(colorspace)
## Setup
options(tigris_use_cache = TRUE)
## Do this
census_api_key("YOUR API KEY HERE")
## or, to install in your .Rprofile follow the instructions at
## https://walker-data.com/tidycensus/reference/census_api_key.html
pop_names <- tribble(
~varname, ~clean,
"B01003_001", "pop",
"B01001B_001", "black",
"B01001A_001", "white",
"B01001H_001", "nh_white",
"B01001I_001", "hispanic",
"B01001D_001", "asian"
)
## Get the data
fips_pop <- get_acs(geography = "county",
variables = pop_names$varname,
cache_table = TRUE) |>
left_join(pop_names, join_by(variable == varname)) |>
mutate(variable = clean) |>
select(-clean, -moe) |>
pivot_wider(names_from = variable, values_from = estimate) |>
rename(fips = GEOID, name = NAME) |>
mutate(prop_pop = pop/sum(pop),
prop_black = black/pop,
prop_hisp = hispanic/pop,
prop_white = white/pop,
prop_nhwhite = nh_white/pop,
prop_asian = asian/pop)
fips_map <- get_acs(geography = "county",
variables = "B01001_001",
geometry = TRUE,
shift_geo = FALSE,
cache_table = TRUE) |>
select(GEOID, NAME, geometry) |>
rename(fips = GEOID, name = NAME)
pop_cat_labels <- c("<5", as.character(seq(10, 95, 5)), "100")
counties_sf <- fips_map |>
left_join(fips_pop, by = c("fips", "name")) |>
mutate(black_disc = cut(prop_black*100,
breaks = seq(0, 100, 5),
labels = pop_cat_labels,
ordered_result = TRUE),
hisp_disc = cut(prop_hisp*100,
breaks = seq(0, 100, 5),
labels = pop_cat_labels,
ordered_result = TRUE),
nhwhite_disc = cut(prop_nhwhite*100,
breaks = seq(0, 100, 5),
labels = pop_cat_labels,
ordered_result = TRUE),
asian_disc = cut(prop_asian*100,
breaks = seq(0, 100, 5),
labels = pop_cat_labels,
ordered_result = TRUE)) |>
sf::st_transform(crs = 2163)
## Now we have
counties_sf
## Create the circle-packed version
## Be patient
county_dorling <- cartogram_dorling(x = counties_sf,
weight = "prop_pop",
k = 0.2, itermax = 100)
## Now draw the maps
## Black
out_black <- county_dorling |>
filter(!str_detect(name, "Alaska|Hawaii|Puerto|Guam")) |>
ggplot(aes(fill = black_disc)) +
geom_sf(color = "grey30", size = 0.1) +
coord_sf(crs = 2163, datum = NA) +
scale_fill_discrete_sequential(palette = "YlOrBr",
na.translate=FALSE) +
guides(fill = guide_legend(title.position = "top",
label.position = "bottom",
nrow = 1)) +
labs(
subtitle = "Bubble size corresponds to County Population",
caption = "Graph: @kjhealy. Source: Census Bureau / American Community Survey",
fill = "Percent Black by County") +
theme(legend.position = "top",
legend.spacing.x = unit(0, "cm"),
legend.title = element_text(size = rel(1.5), face = "bold"),
legend.text = element_text(size = rel(0.7)),
plot.title = element_text(size = rel(1.4), hjust = 0.15))
ggsave("figures/dorling-bl.png", out_black, height = 10, width = 12)
## Hispanic
out_hispanic <- county_dorling |>
filter(!str_detect(name, "Alaska|Hawaii|Puerto|Guam")) |>
ggplot(aes(fill = hisp_disc)) +
geom_sf(color = "grey30", size = 0.1) +
coord_sf(crs = 2163, datum = NA) +
scale_fill_discrete_sequential(palette = "SunsetDark", na.translate=FALSE) +
guides(fill = guide_legend(title.position = "top",
label.position = "bottom",
nrow = 1,
)) +
labs(fill = "Percent Hispanic by County",
subtitle = "Bubble size corresponds to County Population",
caption = "Graph: @kjhealy. Source: Census Bureau / American Community Survey") +
theme(legend.position = "top",
legend.spacing.x = unit(0, "cm"),
legend.title = element_text(size = rel(1.5), face = "bold"),
legend.text = element_text(size = rel(0.7)),
plot.title = element_text(size = rel(1.4), hjust = 0.15))
ggsave("figures/dorling-hs.png", out_hispanic, height = 10, width = 12)
## NH White
out_white <- county_dorling |>
filter(!str_detect(name, "Alaska|Hawaii|Puerto|Guam")) |>
ggplot(aes(fill = nhwhite_disc)) +
geom_sf(color = "grey30", size = 0.1) +
coord_sf(crs = 2163, datum = NA) +
scale_fill_discrete_sequential(palette = "BluYl", na.translate=FALSE) +
guides(fill = guide_legend(title.position = "top",
label.position = "bottom",
nrow = 1,
)) +
labs(fill = "Percent Non-Hispanic White by County",
subtitle = "Bubble size corresponds to County Population",
caption = "Graph: @kjhealy. Source: Census Bureau / American Community Survey") +
theme(legend.position = "top",
legend.spacing.x = unit(0, "cm"),
legend.title = element_text(size = rel(1.5), face = "bold"),
legend.text = element_text(size = rel(0.7)),
plot.title = element_text(size = rel(1.4), hjust = 0.15))
ggsave("figures/dorling-nhw.png", out_white, height = 10, width = 12)
## Asian
out_asian <- county_dorling |>
filter(!str_detect(name, "Alaska|Hawaii|Puerto|Guam")) |>
ggplot(aes(fill = asian_disc)) +
geom_sf(color = "grey30", size = 0.1) +
coord_sf(crs = 2163, datum = NA) +
scale_fill_discrete_sequential(palette = "Purple-Ora", na.translate=FALSE) +
guides(fill = guide_legend(title.position = "top",
label.position = "bottom",
nrow = 1,
)) +
labs(fill = "Percent Asian by County",
subtitle = "Bubble size corresponds to County Population",
caption = "Graph: @kjhealy. Source: Census Bureau / American Community Survey") +
theme(legend.position = "top",
legend.spacing.x = unit(0, "cm"),
legend.title = element_text(size = rel(1.5), face = "bold"),
legend.text = element_text(size = rel(0.7)),
plot.title = element_text(size = rel(1.4), hjust = 0.15))
ggsave("figures/dorling-asian.png", out_asian, height = 10, width = 12)
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