4  socviz examples

Caution

The examples in this chapter require the coord-polar branch of iangow/plotnine. Standard releases of plotnine do not yet include these changes.

from datetime import date

import polars as pl
import plotnine_polars as p9
import socviz_pl as sv
from plotnine_polars import aes

4.1 FARS pedestrian data

The farsinvolved dataset records daily counts of child pedestrians (aged 0–17) involved in fatal motor vehicle crashes in the United States from 2009 to 2023. We aggregate by calendar month and day, averaging across years. Year 2000 is used as a placeholder date because 2000 was a leap year.

farsinvolved = sv.load_data("farsinvolved")

month_order = [
    "January", "February", "March", "April", "May", "June",
    "July", "August", "September", "October", "November", "December",
]
month_num = {m: i + 1 for i, m in enumerate(month_order)}

fars_agg = (
    farsinvolved
    .with_columns(pl.col("day").cast(pl.Int32))
    .group_by("month", "day")
    .agg(n=pl.col("n").mean())
    .with_columns(
        month_num=pl.col("month").replace(month_num).cast(pl.Int32),
    )
    .with_columns(
        fake_yr=pl.date(2000, pl.col("month_num"), pl.col("day")),
        flag=(pl.col("month") == "October") & (pl.col("day") == 31),
    )
    .sort("fake_yr")
)

Halloween (October 31) stands out as the single most dangerous day for child pedestrians.

The chart below uses coord_radial() with inner_radius=0.25 to create a donut-shaped calendar, matching the style from Data Visualization: A Practical Introduction. The r-axis limits are fixed at (0, 4.25) so the hollow centre corresponds to zero daily average fatalities, and r_axis_inside=True keeps the radial tick labels inside the ring.

plot_data = (
    fars_agg
    .with_columns(
        doy=pl.col("fake_yr").dt.ordinal_day(),
        day_type=pl.when(pl.col("flag"))
                   .then(pl.lit("Halloween"))
                   .otherwise(pl.lit("Other")),
    )
)

m_breaks = [date(2000, m, 15).timetuple().tm_yday for m in range(1, 13)]
m_labels = ["January", "February", "March", "April",
            "May", "June", "July", "August", "September",
            "October", "November", "December"]

(
    plot_data
    .ggplot(aes(x="doy", y="n", fill="day_type", color="day_type"))
    .geom_point(shape="o", size=2.5, stroke=0.4)
    .scale_fill_manual(values={"Halloween": "#E06000", "Other": "white"})
    .scale_color_manual(values={"Halloween": "#333333", "Other": "#222222"})
    .geom_text(data=plot_data.filter(pl.col("day_type") == "Halloween"),
               mapping=aes(x="doy", y="n", label="day_type"),
               color="#E06000", size=7, nudge_x=2, nudge_y=-1,
               ha="right", inherit_aes=False)
    .scale_x_continuous(breaks=m_breaks, labels=m_labels)
    .coord_radial(inner_radius=0.25, r_axis_inside=True, expand=False,
                  theta_labels=True, theta_label_pad=9)
    .scale_y_continuous(limits=(0, 4.25))
    .labs(
        title="Pedestrians aged 0–17 in Fatal Motor Vehicle Crashes",
        subtitle="Daily average, 2009–2023\n\n",
    )
    .add_theme(
        axis_title=p9.element_blank(),
        axis_text_x=p9.element_text(size=7, color="#444444"),
        axis_ticks_major_x=p9.element_blank(),
        legend_position="none",
        figure_size=(5.5, 5.5),
    )
)

Figure 4.1: Pedestrians aged 0–17 in Fatal Motor Vehicle Crashes. Daily average, 2009–2023.