A choropleth map visualizes data by shading geographic regions (countries, states, or counties) according to a measured variable. This technique is ideal for showing regional patterns and spatial distributions, making it easy to identify areas with high or low values at a glance. The color intensity represents the data magnitude, creating an intuitive way to understand geographic variation.
#' anyplot.ai
#' choropleth-basic: Choropleth Map with Regional Coloring
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 85/100 | Created: 2026-09-02
library(ggplot2)
library(dplyr)
library(tibble)
library(scales)
library(ragg)
# --- Theme tokens -------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
# --- Data -----------------------------------------------------------------
# ggplot2 has no native geographic-boundary support (needs sf/ggmap, out of
# scope here), so the map is expressed as a schematic unit-tile mosaic —
# each South American country is a set of grid cells, laid out to preserve
# real relative position/adjacency. geom_tile() then renders it natively.
cell_list <- list(
Venezuela = matrix(c(2, 6, 3, 6, 2, 5), ncol = 2, byrow = TRUE),
Guyana = matrix(c(4, 6), ncol = 2, byrow = TRUE),
Suriname = matrix(c(5, 6), ncol = 2, byrow = TRUE),
Colombia = matrix(c(0, 5, 1, 5, 0, 4, 1, 4, 1, 3), ncol = 2, byrow = TRUE),
Ecuador = matrix(c(0, 3), ncol = 2, byrow = TRUE),
Peru = matrix(c(0, 2, 1, 2, 0, 1), ncol = 2, byrow = TRUE),
Brazil = matrix(c(
2, 4, 3, 4, 4, 4, 5, 4,
2, 3, 3, 3, 4, 3, 5, 3, 6, 3,
2, 2, 3, 2, 4, 2, 5, 2, 6, 2,
2, 1, 3, 1, 4, 1, 5, 1,
3, 0, 4, 0, 5, 0
), ncol = 2, byrow = TRUE),
Bolivia = matrix(c(1, 1, 2, 0, 1, 0), ncol = 2, byrow = TRUE),
Paraguay = matrix(c(3, -1, 4, -1), ncol = 2, byrow = TRUE),
Chile = matrix(c(0, 0, 0, -1, 0, -2, 0, -3, 0, -4, 0, -5, 0, -6), ncol = 2, byrow = TRUE),
Argentina = matrix(c(
1, -1, 2, -1,
1, -2, 2, -2,
1, -3, 2, -3,
1, -4,
1, -5,
1, -6
), ncol = 2, byrow = TRUE),
Uruguay = matrix(c(5, -1), ncol = 2, byrow = TRUE)
)
country_cells <- bind_rows(lapply(names(cell_list), function(country) {
cells <- cell_list[[country]]
tibble(country = country, col = cells[, 1], row = cells[, 2])
}))
country_code <- c(
Venezuela = "VE", Guyana = "GY", Suriname = "SR", Colombia = "CO",
Ecuador = "EC", Peru = "PE", Brazil = "BR", Bolivia = "BO",
Paraguay = "PY", Chile = "CL", Argentina = "AR", Uruguay = "UY"
)
# Renewable share of electricity generation (%) — Guyana is left NA to
# demonstrate missing-data handling.
renewable_share <- tibble(
country = names(country_code),
value = c(65, NA, 45, 70, 75, 60, 85, 30, 100, 48, 30, 94)
)
df <- country_cells %>%
left_join(renewable_share, by = "country")
centroids <- df %>%
group_by(country) %>%
summarize(x = mean(col), y = mean(row), value = first(value), .groups = "drop") %>%
mutate(code = country_code[country])
df_present <- df %>% filter(!is.na(value))
df_missing <- df %>% filter(is.na(value))
# Highest/lowest-share countries are named in the caption below (no on-map
# marker: a per-cell highlight either creates seams across multi-cell
# countries like Bolivia/Argentina or collides with their centroid label).
extreme_countries <- df_present %>%
distinct(country, value) %>%
filter(value == max(value) | value == min(value)) %>%
mutate(code = country_code[country])
highest_codes <- extreme_countries %>%
filter(value == max(value)) %>%
pull(code) %>%
paste(collapse = ", ")
highest_value <- max(extreme_countries$value)
lowest_codes <- extreme_countries %>%
filter(value == min(value)) %>%
pull(code) %>%
paste(collapse = ", ")
lowest_value <- min(extreme_countries$value)
# --- Title (fontsize scales with title length, see plot-generator.md) -----
# No descriptive prefix: the legend title already names the metric, and the
# square canvas leaves less horizontal room than the landscape default.
title_text <- "choropleth-basic · r · ggplot2 · anyplot.ai"
title_len <- nchar(title_text)
title_ratio <- if (title_len > 67) 67 / title_len else 1
title_size <- max(8, round(12 * title_ratio))
# --- Plot -------------------------------------------------------------------
p <- ggplot() +
geom_tile(
data = df_present, aes(x = col, y = row, fill = value),
color = PAGE_BG, linewidth = 0.6, width = 0.94, height = 0.94
) +
geom_tile(
data = df_missing, aes(x = col, y = row),
fill = INK_MUTED, color = PAGE_BG, linewidth = 0.6,
width = 0.94, height = 0.94, alpha = 0.6
) +
geom_text(
data = centroids, aes(x = x, y = y, label = code),
size = 3, color = INK, fontface = "bold"
) +
scale_fill_gradient(
low = "#009E73", high = "#4467A3",
name = "Renewable share\nof electricity",
labels = label_percent(scale = 1),
na.value = INK_MUTED,
guide = guide_colorbar(frame.colour = INK_SOFT, ticks.colour = INK_SOFT)
) +
coord_fixed(ratio = 1) +
labs(
title = title_text,
subtitle = "Schematic tile-map of South America · relative country adjacency preserved, not to scale",
caption = paste0(
"Highest: ", highest_codes, " (", highest_value, "%) · lowest: ",
lowest_codes, " (", lowest_value, "%)\nGray tile: data unavailable (Guyana)"
)
) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(size = title_size, color = INK, hjust = 0.5),
plot.subtitle = element_text(size = 7, color = INK_SOFT, hjust = 0.5),
plot.caption = element_text(size = 7, color = INK_MUTED, hjust = 0.5),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT),
legend.text = element_text(size = 8, color = INK_SOFT),
legend.title = element_text(size = 10, color = INK),
legend.position = "right",
plot.margin = margin(t = 12, r = 8, b = 8, l = 8)
)
# --- Save -------------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 6,
height = 6,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/choropleth-basic/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "choropleth-basic",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/choropleth-basic/r/ggplot2",
"hub": "https://anyplot.ai/choropleth-basic",
"code_json": "https://api.anyplot.ai/specs/choropleth-basic/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/choropleth-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/choropleth-basic/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/choropleth-basic/r/ggplot2/plot-dark.png",
"quality_score": 85.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Choropleth Map with Regional Coloring on anyplot.ai.