A radar chart (also known as spider or web chart) displays multivariate data on axes starting from a common center point, with values connected to form a polygon. Each axis represents a different variable, making it ideal for comparing multiple quantitative variables at once or visualizing strengths and weaknesses across categories.
""" anyplot.ai
radar-basic: Basic Radar Chart
Library: letsplot 4.11.0 | Python 3.13.14
Quality: 89/100 | Updated: 2026-07-24
"""
import math
import os
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_rect,
element_text,
geom_path,
geom_point,
geom_polygon,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
scale_color_manual,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
)
LetsPlot.setup_html()
# Theme-adaptive chrome (Imprint palette data colors stay constant across themes)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
IMPRINT_PALETTE = ["#009E73", "#C475FD"]
# Data - Employee performance metrics (6 categories, 2 employees)
categories = ["Technical", "Communication", "Leadership", "Creativity", "Teamwork", "Problem Solving"]
values_alice = [85, 70, 60, 90, 75, 80]
values_bob = [70, 85, 75, 65, 90, 70]
n = len(categories)
# Create angles for each category (evenly spaced, starting from top)
angles = [i * 2 * math.pi / n for i in range(n)]
# Build dataframe with cartesian coordinates for each series
data_rows = []
for i, (cat, val_a, val_b, angle) in enumerate(zip(categories, values_alice, values_bob, angles, strict=True)):
# Convert polar to cartesian (0 degrees at top, clockwise)
x_a = val_a * math.cos(angle - math.pi / 2)
y_a = val_a * math.sin(angle - math.pi / 2)
x_b = val_b * math.cos(angle - math.pi / 2)
y_b = val_b * math.sin(angle - math.pi / 2)
data_rows.append({"category": cat, "value": val_a, "x": x_a, "y": y_a, "series": "Alice", "order": i})
data_rows.append({"category": cat, "value": val_b, "x": x_b, "y": y_b, "series": "Bob", "order": i})
# Close the polygon by repeating first point
x_a = values_alice[0] * math.cos(angles[0] - math.pi / 2)
y_a = values_alice[0] * math.sin(angles[0] - math.pi / 2)
x_b = values_bob[0] * math.cos(angles[0] - math.pi / 2)
y_b = values_bob[0] * math.sin(angles[0] - math.pi / 2)
data_rows.append(
{"category": categories[0], "value": values_alice[0], "x": x_a, "y": y_a, "series": "Alice", "order": n}
)
data_rows.append({"category": categories[0], "value": values_bob[0], "x": x_b, "y": y_b, "series": "Bob", "order": n})
df = pd.DataFrame(data_rows)
# Create gridlines data (circles at 20, 40, 60, 80, 100)
grid_rows = []
grid_values = [20, 40, 60, 80, 100]
grid_angles = [i * 2 * math.pi / 72 for i in range(73)] # 73 points for smooth circles
for radius in grid_values:
for angle in grid_angles:
x = radius * math.cos(angle - math.pi / 2)
y = radius * math.sin(angle - math.pi / 2)
grid_rows.append({"x": x, "y": y, "radius": radius})
grid_df = pd.DataFrame(grid_rows)
# Create axis lines (spokes from center to edge)
spoke_rows = []
for i, angle in enumerate(angles):
x = 105 * math.cos(angle - math.pi / 2)
y = 105 * math.sin(angle - math.pi / 2)
spoke_rows.append({"x": 0, "y": 0, "group": i})
spoke_rows.append({"x": x, "y": y, "group": i})
spoke_df = pd.DataFrame(spoke_rows)
# Create axis labels (category names at outer edge)
label_rows = []
for cat, angle in zip(categories, angles, strict=True):
x = 120 * math.cos(angle - math.pi / 2)
y = 120 * math.sin(angle - math.pi / 2)
label_rows.append({"label": cat, "x": x, "y": y})
label_df = pd.DataFrame(label_rows)
# Create grid value labels - placed on the angular bisector between the first
# two category spokes (a gap with no data) rather than on a data-bearing spoke,
# so they never overlap a series' marker/line
value_label_angle = (angles[0] + angles[1]) / 2 - math.pi / 2
value_label_rows = []
for val in grid_values:
x = val * math.cos(value_label_angle)
y = val * math.sin(value_label_angle)
value_label_rows.append({"label": str(val), "x": x, "y": y})
value_label_df = pd.DataFrame(value_label_rows)
# Descriptive prefix clarifies this instance is an employee comparison, giving
# the reader an immediate frame for the two complementary skill profiles
title = "Employee Skills Comparison · radar-basic · python · letsplot · anyplot.ai"
title_fontsize = round(16 * (60 / len(title) if len(title) > 60 else 1.0))
title_fontsize = max(title_fontsize, 11)
# Build the plot
plot = (
ggplot()
# Gridlines (concentric circles) - geom_path preserves point order (geom_line
# sorts by x, which breaks a circle traced by angle into a star pattern)
+ geom_path(aes(x="x", y="y", group="radius"), data=grid_df, color=INK_SOFT, size=0.6, alpha=0.3)
# Spokes (radial lines)
+ geom_path(aes(x="x", y="y", group="group"), data=spoke_df, color=INK_SOFT, size=0.6, alpha=0.3)
# Filled polygons for each series
+ geom_polygon(aes(x="x", y="y", fill="series", group="series"), data=df, alpha=0.25)
# Ink-color outline behind each line, peeking out a touch on either side, so the
# lower-contrast lavender series still reads clearly against the page background
+ geom_path(aes(x="x", y="y", group="series"), data=df, color=INK, size=2.8, alpha=0.5)
# Lines connecting points, in category order (geom_path, not geom_line)
+ geom_path(aes(x="x", y="y", color="series", group="series"), data=df, size=2)
# Points at each vertex (exclude the closing point to avoid double dot) - shape 21
# gives a filled marker with an ink-color border stroke for the same contrast boost
+ geom_point(aes(x="x", y="y", fill="series"), data=df[df["order"] < n], shape=21, color=INK, size=6, stroke=1.2)
# Imprint palette - brand green first, lavender second
+ scale_fill_manual(values=IMPRINT_PALETTE)
+ scale_color_manual(values=IMPRINT_PALETTE)
# Axis limits for square plot
+ scale_x_continuous(limits=(-150, 150))
+ scale_y_continuous(limits=(-150, 150))
# Title and legend
+ labs(title=title, fill="Employee", color="Employee")
# Square format for symmetric radar chart
+ ggsize(600, 600)
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
plot_title=element_text(size=title_fontsize, color=INK),
legend_title=element_text(size=12, color=INK),
legend_text=element_text(size=10, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_position="right",
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
panel_grid=element_blank(),
panel_border=element_blank(),
)
)
# Add category labels as text (theme-adaptive ink)
plot = plot + geom_text(aes(x="x", y="y", label="label"), data=label_df, size=4.2, color=INK)
# Add grid value labels (theme-adaptive soft ink)
plot = plot + geom_text(aes(x="x", y="y", label="label"), data=value_label_df, size=3.5, color=INK_SOFT)
# Save outputs (PNG scaled 4x to 2400x2400, plus interactive HTML)
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Basic Radar Chart on anyplot.ai.