Coal production

import plotnine as gg
import pandas as pd
import textwrap
import highlight_text as ht
from ninejs import interactive, save

emissions = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-05-21/emissions.csv"
)

# Prep data for plotting
plot_data = emissions[
    emissions["commodity"].isin(
        [
            "Sub-Bituminous Coal",
            "Metallurgical Coal",
            "Bituminous Coal",
            "Thermal Coal",
            "Anthracite Coal",
            "Lignite Coal",
        ]
    )
].copy()
plot_data["commodity"] = plot_data["commodity"].str.replace(" Coal", "")
# Total production per year since 1900
plot_data = plot_data[["year", "commodity", "production_value"]]
plot_data = (
    plot_data.groupby(["year", "commodity"], as_index=False)
    .agg({"production_value": "sum"})
    .rename(columns={"production_value": "n"})
)
plot_data = plot_data[plot_data["year"] >= 1900]
# Sort values by 2022 levels
orders = plot_data[plot_data["year"] == 2022].sort_values(by="n", ascending=False)[
    "commodity"
]
plot_data["commodity"] = pd.Categorical(
    plot_data["commodity"], categories=orders, ordered=True
)
# Values for annotations
exceeds100 = plot_data.groupby("year")["n"].sum()
exceeds100 = exceeds100[exceeds100 > 100].index.min()
# Create data for x-axis labels
segment_data = pd.DataFrame({"year": list(range(1900, 2021, 20))})
# y-axis labels
y_axis_data = pd.DataFrame(
    {
        "value": [0, 2000, 4000, 6000, 8000],
        "label": ["0", "2,000", "4,000", "6,000", "8,000\nmillion\ntonnes"],
    }
)
# Define background colour, text colour, and colour palette
bg_col = "#FFFFFA"
text_col = "#0D5C63"
col_palette = ["#E58606", "#5D69B1", "#52BCA3", "#99C945", "#CC61B0", "#24796C"]
body_font = "Arial"
# title, subtitle
title_text = "Coal production since 1900"
st = "Carbon Majors is a database of historical production data from 122 of the world’s largest oil, gas, coal, and cement producers. This data is used to quantify the direct operational emissions and emissions from the combustion of marketed products that can be attributed to these entities."
wrapped_subtitle = "\n".join(textwrap.wrap(st, width=50))
# annotation labels
coal_types_label = 'Total coal production includes\nproduction of <Bituminous::{"color": "#E58606"}>,\n<Sub-bituminous::{"color": "#5D69B1"}>, <Metallurgical::{"color": "#52BCA3"}>,\n<Lignite::{"color": "#99C945"}>, <Anthracite::{"color": "#CC61B0"}>, and <Thermal::{"color": "#24796C"}>\ncoal. Bituminous accounts\nfor around half.'

# caption
cap = '<Data::{"fontweight": "bold"}>: Carbon Majors\n<Graphic::{"fontweight": "bold"}>: Nicola Rennie (@nrennie)'

p = (
    gg.ggplot(plot_data, gg.aes(x="year", y="n"))
    + gg.geom_area(gg.aes(fill="commodity", tooltip="commodity"))
    + gg.geom_segment(
        data=segment_data,
        mapping=gg.aes(x="year", xend="year", y=0, yend=-1700),
        linetype="dashed",
        alpha=0.4,
        color=text_col,
    )
    + gg.geom_text(
        data=segment_data,
        mapping=gg.aes(x="year", y=-1900, label="year"),
        color=text_col,
        size=8,
        family=body_font,
        ha="left",
    )
    + gg.geom_text(
        data=y_axis_data,
        mapping=gg.aes(x=2023, y="value", label="label"),
        color=text_col,
        size=8,
        family=body_font,
        ha="left",
        va="top",
    )
    + gg.annotate(
        "segment", x=exceeds100, xend=exceeds100, y=0, yend=5000, size=1, color=text_col
    )
    + gg.annotate(
        "text",
        x=exceeds100 + 2,
        y=5000,
        label=exceeds100,
        color=text_col,
        family=body_font,
        ha="left",
        va="top",
        size=10,
        fontweight="bold",
    )
    + gg.annotate(
        "text",
        x=exceeds100 + 2,
        y=5000 - 600,
        label="Total coal production first\nexceeds 100 million tonnes\nper year.",
        color=text_col,
        family=body_font,
        ha="left",
        size=9,
        va="top",
    )
    + gg.annotate("segment", size=1, x=1975, xend=1975, y=0, yend=10000, color=text_col)
    + gg.annotate(
        "text",
        x=1975 + 2,
        y=10000,
        label="Coal types",
        color=text_col,
        family=body_font,
        ha="left",
        va="top",
        size=10,
        fontweight="bold",
    )
    + gg.scale_fill_manual(values=col_palette)
    + gg.scale_x_continuous(limits=(1896, 2034))
    + gg.scale_y_continuous(limits=(-3300, 12000))
    + gg.annotate(
        "text",
        x=1900,
        y=11400,
        label=title_text,
        color=text_col,
        family=body_font,
        ha="left",
        va="top",
        size=13,
        fontweight="bold",
    )
    + gg.annotate(
        "text",
        x=1900,
        y=10500,
        label=wrapped_subtitle,
        color=text_col,
        family=body_font,
        size=9.5,
        ha="left",
        va="top",
    )
    + gg.coord_cartesian(expand=False)
    + gg.theme_void(base_size=8)
    + gg.theme(
        legend_position="none",
        plot_background=gg.element_rect(fill=bg_col, color=bg_col),
        panel_background=gg.element_rect(fill=bg_col, color=bg_col),
    )
)

fig = p.draw()
fig.set_size_inches(8, 6, forward=True)
ax = fig.axes[0]
ht.ax_text(
    1977,
    9400,
    coal_types_label,
    ax=ax,
    vsep=3,
    color=text_col,
    fontname=body_font,
    fontsize=9,
    va="top",
)
# add caption
ht.ax_text(
    1900, -2300, cap, color=text_col, fontname=body_font, fontsize=7.5, va="top", ax=ax
)

interactive(p) + save("docs/iframes/coal-production.html")