letsplot.py

""" anyplot.ai
radar-basic: Basic Radar Chart
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 70/100 | Updated: 2026-04-29
"""

import math

import pandas as pd
from lets_plot import (
    LetsPlot,
    aes,
    element_blank,
    element_text,
    geom_line,
    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()

# 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 (scale indicators on first spoke)
value_label_rows = []
for val in grid_values:
    x = val * math.cos(-math.pi / 2) + 8  # Offset right for readability
    y = val * math.sin(-math.pi / 2)
    value_label_rows.append({"label": str(val), "x": x, "y": y})

value_label_df = pd.DataFrame(value_label_rows)

# Build the plot
plot = (
    ggplot()
    # Gridlines (concentric circles)
    + geom_line(
        aes(x="x", y="y", group="radius"), data=grid_df, color="#CCCCCC", size=0.6, alpha=0.6, linetype="dashed"
    )
    # Spokes (radial lines)
    + geom_line(aes(x="x", y="y", group="group"), data=spoke_df, color="#CCCCCC", size=0.6, alpha=0.6)
    # Filled polygons for each series
    + geom_polygon(aes(x="x", y="y", fill="series", group="series"), data=df, alpha=0.25)
    # Lines connecting points
    + geom_line(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)
    + geom_point(aes(x="x", y="y", color="series"), data=df[df["order"] < n], size=6)
    # Python Blue and Python Yellow
    + scale_fill_manual(values=["#306998", "#FFD43B"])
    + scale_color_manual(values=["#306998", "#FFD43B"])
    # Axis limits for square plot
    + scale_x_continuous(limits=(-150, 150))
    + scale_y_continuous(limits=(-150, 150))
    # Title and legend
    + labs(title="radar-basic · letsplot · pyplots.ai", fill="Employee", color="Employee")
    # Square format for symmetric radar chart
    + ggsize(1200, 1200)
    + theme(
        plot_title=element_text(size=24),
        legend_title=element_text(size=18),
        legend_text=element_text(size=16),
        legend_position="right",
        axis_title=element_blank(),
        axis_text=element_blank(),
        axis_ticks=element_blank(),
        axis_line=element_blank(),
        panel_grid=element_blank(),
    )
)

# Add category labels as text
plot = plot + geom_text(aes(x="x", y="y", label="label"), data=label_df, size=16, color="#333333")

# Add grid value labels
plot = plot + geom_text(aes(x="x", y="y", label="label"), data=value_label_df, size=12, color="#666666")

# Save outputs
ggsave(plot, "plot.png", path=".", scale=3)
ggsave(plot, "plot.html", path=".")