A radial dendrogram renders hierarchical clustering in a circular layout where the root node sits at the center and branches extend outward, with leaf nodes arranged around the circumference. This layout is a space-efficient alternative to linear dendrograms for large hierarchies, making it well-suited for datasets with hundreds of leaves. Branch lengths are proportional to distance or dissimilarity, preserving the quantitative interpretation of cluster merges.
#' anyplot.ai
#' dendrogram-radial: Radial Dendrogram
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 89/100 | Created: 2026-09-05
library(ggplot2)
library(dplyr)
library(tidyr)
library(tibble)
library(ragg)
set.seed(42)
# --- Theme tokens ------------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
# Imprint palette (see prompts/default-style-guide.md "Categorical Palette")
IMPRINT_PALETTE <- c(
"#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314"
)
# --- Data: synthetic gene expression profiles across 4 latent groups --------
n_genes <- 32
n_samples <- 8
n_groups <- 4
group_id <- rep(1:n_groups, length.out = n_genes)
group_means <- matrix(rnorm(n_groups * n_samples, mean = 0, sd = 4), nrow = n_groups)
expr <- group_means[group_id, ] + matrix(rnorm(n_genes * n_samples, mean = 0, sd = 1.3), nrow = n_genes)
rownames(expr) <- sprintf("Gene_%02d", seq_len(n_genes))
hc <- hclust(dist(expr), method = "average")
leaf_cluster <- cutree(hc, k = n_groups)
n <- n_genes
max_height <- max(hc$height)
# --- Walk the merge tree: assign circumferential position + branch purity ---
leaf_pos <- match(seq_len(n), hc$order)
node_x <- numeric(n - 1)
node_cluster <- rep(NA_integer_, n - 1)
get_x <- function(idx) if (idx < 0) leaf_pos[-idx] else node_x[idx]
get_height <- function(idx) if (idx < 0) 0 else hc$height[idx]
get_cluster <- function(idx) if (idx < 0) leaf_cluster[[-idx]] else node_cluster[idx]
radial_rows <- vector("list", 2 * (n - 1))
arc_rows <- vector("list", n - 1)
for (k in seq_len(n - 1)) {
left <- hc$merge[k, 1]
right <- hc$merge[k, 2]
x_left <- get_x(left)
x_right <- get_x(right)
node_x[k] <- (x_left + x_right) / 2
h_left <- get_height(left)
h_right <- get_height(right)
h_here <- hc$height[k]
c_left <- get_cluster(left)
c_right <- get_cluster(right)
node_cluster[k] <- if (!is.na(c_left) && !is.na(c_right) && c_left == c_right) c_left else NA_integer_
radial_rows[[2 * k - 1]] <- tibble(
x = x_left, r_start = max_height - h_left, r_end = max_height - h_here, cluster = c_left
)
radial_rows[[2 * k]] <- tibble(
x = x_right, r_start = max_height - h_right, r_end = max_height - h_here, cluster = c_right
)
n_pts <- max(10, round(abs(x_right - x_left) * 3))
arc_rows[[k]] <- tibble(
x = seq(x_left, x_right, length.out = n_pts), r = max_height - h_here,
cluster = node_cluster[k], seg = k
)
}
label_with_cluster <- function(df) mutate(df, cluster_label = ifelse(is.na(cluster), "mixed", as.character(cluster)))
radial_df <- label_with_cluster(bind_rows(radial_rows))
arc_df <- label_with_cluster(bind_rows(arc_rows))
# --- Leaf labels, rotated tangentially around the circumference -------------
leaf_labels <- tibble(x = leaf_pos, label = rownames(expr)) %>%
mutate(
r = max_height * 1.06,
angle = 90 - 360 * (x - 0.5) / n,
hjust = ifelse(angle < -90, 1, 0),
angle = ifelse(angle < -90, angle + 180, angle)
)
# --- Concentric distance-scale rings (quantitative reading of merge height) --
ring_heights <- pretty(c(0, max_height), n = 4)
ring_heights <- ring_heights[ring_heights > 0 & ring_heights < max_height]
ring_df <- crossing(h = ring_heights, x = seq(0.5, n + 0.5, length.out = 200)) %>%
mutate(r = max_height - h)
ring_labels <- tibble(h = ring_heights) %>%
mutate(x = 0.85, r = max_height - h, label = sprintf("%.0f", h))
# --- Colors -------------------------------------------------------------------
cluster_levels <- c(as.character(seq_len(n_groups)), "mixed")
CLUSTER_COLORS <- setNames(c(IMPRINT_PALETTE[seq_len(n_groups)], INK_MUTED), cluster_levels)
CLUSTER_LABELS <- setNames(c(sprintf("Cluster %d", seq_len(n_groups)), "Mixed branch"), cluster_levels)
# --- Plot ---------------------------------------------------------------------
p <- ggplot() +
geom_path(
data = ring_df, aes(x = x, y = r, group = h),
color = INK, alpha = 0.18, linewidth = 0.3
) +
geom_text(
data = ring_labels, aes(x = x, y = r, label = label),
color = INK_MUTED, size = 2.6, hjust = 1, vjust = -0.4
) +
geom_segment(
data = radial_df,
aes(x = x, xend = x, y = r_start, yend = r_end, color = cluster_label),
linewidth = 0.7
) +
geom_path(
data = arc_df,
aes(x = x, y = r, group = seg, color = cluster_label),
linewidth = 0.7
) +
geom_text(
data = leaf_labels,
aes(x = x, y = r, label = label, angle = angle, hjust = hjust),
color = INK_SOFT, size = 2.7
) +
scale_color_manual(values = CLUSTER_COLORS, labels = CLUSTER_LABELS, name = NULL) +
scale_x_continuous(limits = c(0.5, n + 0.5), expand = c(0, 0)) +
scale_y_continuous(limits = c(0, max_height * 1.22), expand = c(0, 0)) +
coord_polar(theta = "x", start = 0) +
labs(
title = "dendrogram-radial · r · ggplot2 · anyplot.ai",
subtitle = "Concentric rings mark merge distance (height); root at center"
) +
theme_void(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
plot.title = element_text(color = INK, size = 12, hjust = 0.5, margin = margin(b = 4)),
plot.subtitle = element_text(color = INK_MUTED, size = 8, hjust = 0.5, margin = margin(b = 10)),
legend.position = "bottom",
legend.text = element_text(color = INK_SOFT, size = 8),
plot.margin = margin(14, 14, 14, 14)
)
# --- 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/dendrogram-radial/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": "dendrogram-radial",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/dendrogram-radial/r/ggplot2",
"hub": "https://anyplot.ai/dendrogram-radial",
"code_json": "https://api.anyplot.ai/specs/dendrogram-radial/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/dendrogram-radial",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/dendrogram-radial/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/dendrogram-radial/r/ggplot2/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Radial Dendrogram on anyplot.ai.