A forest plot displays effect sizes with confidence intervals from multiple studies in a meta-analysis. Each study is represented as a point estimate with horizontal whiskers showing the confidence interval, and a diamond at the bottom shows the pooled estimate. The plot includes a vertical reference line at the null effect (typically 0 or 1), making it easy to assess statistical significance and heterogeneity across studies.
#' anyplot.ai
#' forest-basic: Meta-Analysis Forest Plot
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 90/100 | Created: 2026-09-05
library(ggplot2)
library(dplyr)
library(scales)
library(ragg)
set.seed(42)
# --- Theme tokens (see prompts/default-style-guide.md "Theme-adaptive Chrome")
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"
# Imprint palette — position 1 is ALWAYS the brand green
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314")
BRAND <- IMPRINT_PALETTE[1]
# --- Data: RCTs of a novel antihypertensive vs. placebo on stroke risk -------
# Effect measure is an odds ratio (log-normal, inverse-variance weighted).
surname <- c("Bergstrom", "Chen", "Diaz", "Okafor", "Kowalski",
"Nguyen", "Patel", "Silva", "Haddad", "Kim")
year <- sort(sample(2010:2023, length(surname)))
study <- paste0(surname, " (", year, ")")
sample_size <- round(runif(length(surname), 90, 640))
true_log_or <- log(0.72)
study_se <- 1.6 / sqrt(sample_size)
log_or <- rnorm(length(surname), mean = true_log_or, sd = 0.18)
studies <- tibble(
study = study,
effect_size = exp(log_or),
ci_lower = exp(log_or - 1.96 * study_se),
ci_upper = exp(log_or + 1.96 * study_se),
inv_var = 1 / study_se^2
) %>%
mutate(weight_pct = 100 * inv_var / sum(inv_var))
# Pooled estimate — inverse-variance fixed-effect model
pooled_log_or <- sum(log_or * studies$inv_var) / sum(studies$inv_var)
pooled_se <- sqrt(1 / sum(studies$inv_var))
pooled_effect <- exp(pooled_log_or)
pooled_lower <- exp(pooled_log_or - 1.96 * pooled_se)
pooled_upper <- exp(pooled_log_or + 1.96 * pooled_se)
# Row positions: studies stacked top-down in chronological order, pooled
# estimate isolated near the bottom with a gap for the diamond + rule.
n_studies <- nrow(studies)
studies$y_pos <- rev(seq_len(n_studies))
pooled_y <- 0
whisker_cap <- 0.15
diamond_half_h <- 0.32
diamond_df <- tibble(
x = c(pooled_lower, pooled_effect, pooled_upper, pooled_effect),
y = c(pooled_y, pooled_y + diamond_half_h, pooled_y, pooled_y - diamond_half_h)
)
# --- Plot ---------------------------------------------------------------
p <- ggplot(studies, aes(x = effect_size, y = y_pos)) +
geom_vline(xintercept = 1, linetype = "dashed", linewidth = 0.5, color = INK_SOFT) +
geom_hline(yintercept = 0.55, linewidth = 0.3, color = INK_SOFT) +
geom_segment(aes(x = ci_lower, xend = ci_upper, yend = y_pos),
color = BRAND, linewidth = 0.9) +
geom_segment(aes(x = ci_lower, xend = ci_lower,
y = y_pos - whisker_cap, yend = y_pos + whisker_cap),
color = BRAND, linewidth = 0.9) +
geom_segment(aes(x = ci_upper, xend = ci_upper,
y = y_pos - whisker_cap, yend = y_pos + whisker_cap),
color = BRAND, linewidth = 0.9) +
geom_point(aes(size = weight_pct), shape = 15, color = BRAND) +
geom_segment(data = tibble(x = pooled_lower, xend = pooled_upper),
aes(x = x, xend = xend, y = pooled_y, yend = pooled_y),
inherit.aes = FALSE, color = BRAND, linewidth = 0.9, alpha = 0.35) +
geom_polygon(data = diamond_df, aes(x = x, y = y),
inherit.aes = FALSE, fill = BRAND, color = INK) +
scale_x_log10(breaks = c(0.25, 0.5, 1, 2, 4),
labels = scales::label_number(accuracy = 0.01)) +
scale_y_continuous(
breaks = c(pooled_y, studies$y_pos),
labels = c("Pooled estimate", studies$study),
expand = expansion(add = c(0.9, 0.9))
) +
scale_size_continuous(range = c(3, 9), name = "Weight (%)") +
labs(
title = "forest-basic · r · ggplot2 · anyplot.ai",
x = "Odds ratio (stroke, log scale)",
y = NULL
) +
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.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_line(color = INK, linewidth = 0.2),
axis.line.x = element_line(color = INK_SOFT, linewidth = 0.4),
axis.ticks = element_blank(),
axis.title.x = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
plot.title = element_text(color = INK, size = 12),
legend.position = "right",
legend.background = element_blank(),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.title = element_text(color = INK, size = 10)
)
# --- Save -----------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/forest-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": "forest-basic",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/forest-basic/r/ggplot2",
"hub": "https://anyplot.ai/forest-basic",
"code_json": "https://api.anyplot.ai/specs/forest-basic/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/forest-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/forest-basic/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/forest-basic/r/ggplot2/plot-dark.png",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Meta-Analysis Forest Plot on anyplot.ai.