plot.py

# Load packages -----------------------------------------------------------

import plotnine as gg
import pandas as pd
import textwrap
import matplotlib.pyplot as plt
import highlight_text as ht
import matplotlib.font_manager


# Load data ---------------------------------------------------------------

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


# Data wrangling ----------------------------------------------------------

# Prep data for plotting
# Select only types of coal, then remove word 'Coal'
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', '')

# Keep only relevant columns
plot_data = plot_data[['year', 'commodity', 'production_value']]

# Total production per year since 1900
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))
})

# Create data for 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 colours and fonts-------------------------------------------------

# Define background colour, text colour, and colour palette
bg_col = '#FFFFFA'
text_col = '#0D5C63'
col_palette = [
    '#E58606',
    '#5D69B1',
    '#52BCA3',
    '#99C945',
    '#CC61B0',
    '#24796C']

# Get list of available fonts
flist = matplotlib.font_manager.findSystemFonts()

# Check if 'Arial' in list of installed fonts
flist = ''.join(flist).lower()
if 'arial' in flist:
    body_font = 'Arial'
else:
    body_font = 'sans'


# Define text -------------------------------------------------------------

# 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)'


# Plot --------------------------------------------------------------------

p = (gg.ggplot(plot_data, gg.aes(x='year', y='n')) +
     # Axis lines
     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) +
     # Axis labels
     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') +
     # Annotation 1
     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'
) +
    # Annotation 2
    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'
) +
    # Area plot
    gg.geom_area(gg.aes(fill='commodity')) +
    # Scales
    gg.scale_fill_manual(values=col_palette) +
    gg.scale_x_continuous(limits=(1896, 2034)) +
    gg.scale_y_continuous(limits=(-3300, 12000)) +
    # Text for title and subtitle
    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'
) +
    # Styling
    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)
))

# Add coloured text with matplotlib and highlight-text
# Set up plot options
fig = p.draw()
fig.set_size_inches(8, 6, forward=True)
fig.set_dpi(300)
ax = plt.gca()

# Add coloured text annotation
ht.ax_text(
    1977,
    9400,
    coal_types_label,
    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')

# Save image
plt.savefig('plot.png', dpi=300, bbox_inches='tight')