A horizontal bar chart displaying feature importances from machine learning models, with features sorted by importance value and bars colored by a gradient to emphasize relative contribution. This visualization is essential for model interpretability, helping data scientists and stakeholders understand which features drive model predictions. The horizontal orientation allows for readable feature names of varying lengths.
#' anyplot.ai
#' bar-feature-importance: Feature Importance Bar Chart
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 89/100 | Created: 2026-09-02
library(ggplot2)
library(dplyr)
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 sequential colormap (single-polarity: brand green -> blue)
IMPRINT_SEQ_LOW <- "#009E73"
IMPRINT_SEQ_HIGH <- "#4467A3"
# --- Data -----------------------------------------------------------------
# Feature importances from a gradient-boosting model predicting loan default
features <- c(
"Credit Score", "Debt-to-Income Ratio", "Annual Income",
"Loan Amount", "Employment Length", "Payment History",
"Credit Utilization", "Number of Open Accounts", "Loan Purpose",
"Home Ownership", "Interest Rate", "Delinquencies (2yr)",
"Account Age", "Revolving Balance", "Inquiries (6mo)"
)
n <- length(features)
importance_raw <- sort(rexp(n, rate = 3), decreasing = TRUE)
importance <- round(importance_raw / sum(importance_raw), 4)
std <- round(importance * runif(n, 0.10, 0.30), 4)
df <- tibble::tibble(feature = features, importance = importance, std = std) |>
arrange(importance) |>
mutate(feature = factor(feature, levels = feature))
# --- Plot -------------------------------------------------------------------
title_text <- "bar-feature-importance · r · ggplot2 · anyplot.ai"
p <- ggplot(df, aes(x = importance, y = feature, fill = importance)) +
geom_col(width = 0.68) +
geom_errorbar(
aes(xmin = importance - std, xmax = importance + std),
width = 0.28, color = INK_SOFT, linewidth = 0.5
) +
geom_text(
aes(x = importance + std, label = sprintf("%.3f", importance)),
hjust = -0.25, size = 3.0, color = INK, family = "sans"
) +
scale_fill_gradient(low = IMPRINT_SEQ_LOW, high = IMPRINT_SEQ_HIGH, guide = "none") +
scale_x_continuous(
labels = scales::number_format(accuracy = 0.01),
expand = expansion(mult = c(0, 0.18))
) +
labs(
title = title_text,
x = "Feature Importance",
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.x = element_line(color = INK_MUTED, linewidth = 0.2),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(color = INK, size = 10, margin = margin(t = 8)),
axis.title.y = element_blank(),
axis.text.x = element_text(color = INK_SOFT, size = 8),
axis.text.y = element_text(color = INK_SOFT, size = 9),
axis.ticks = element_blank(),
plot.title = element_text(color = INK, size = 12, margin = margin(b = 12)),
plot.margin = margin(t = 14, r = 20, b = 10, l = 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/bar-feature-importance/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": "bar-feature-importance",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/bar-feature-importance/r/ggplot2",
"hub": "https://anyplot.ai/bar-feature-importance",
"code_json": "https://api.anyplot.ai/specs/bar-feature-importance/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/bar-feature-importance",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bar-feature-importance/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bar-feature-importance/r/ggplot2/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Feature Importance Bar Chart on anyplot.ai.