Life Expectancy

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Python
Code 
import pandas as pd
import numpy as np
import plotnine as gg
import matplotlib.pyplot as plt

# Data
life_expectancy = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-12-05/life_expectancy.csv')

# Colours
bg_col = "#262626"
text_col = "#e5e5e5"

# Wrangling
plot_data = (life_expectancy
    .query("Year >= 2011 and Year <= 2021")
    .groupby("Year", group_keys=False)
    .apply(lambda x: x.nlargest(8, "LifeExpectancy"))
    .sort_values(by=["Year", "LifeExpectancy"], ascending=[True, False])
    .reset_index(drop=True)
)
plot_data["rank"] = plot_data.groupby("Year").cumcount() + 1
plot_data["rank"] = plot_data["rank"].astype(str)

# Colours
colors = ["#5A5156", "#E4E1E3", "#F6222E", "#FE00FA", "#16FF32", "#3283FE", "#FEAF16", "#B00068", "#1CFFCE", "#90AD1C", "#2ED9FF", "#DEA0FD", "#AA0DFE", "#F8A19F", "#325A9B", "#C4451C", "#1C8356"]

# Plot
g = (
    gg.ggplot()
    + gg.geom_point(gg.aes(x="Year", y="rank", color="Entity"), data=plot_data, size=2.5)
    + gg.geom_line(gg.aes(x="Year", y="rank", color="Entity", group="Entity"), data=plot_data)
    + gg.scale_x_continuous(expand=(0, 0.1), limits=(2010.75, 2021.25), breaks=np.arange(2011, 2022), minor_breaks=np.arange(2011, 2021.25, 0.25))
    + gg.scale_y_discrete(limits=reversed)
    + gg.scale_color_manual(values=colors)
    + gg.theme_light()
    + gg.theme(
        legend_position="none",
        plot_margin=0,
        axis_title=gg.element_blank(),
        axis_text=gg.element_blank(),
        plot_background=gg.element_rect(fill=bg_col, color=bg_col),
        panel_background=gg.element_rect(fill=bg_col, color=bg_col),
        panel_grid_major_y=gg.element_line(color=text_col, alpha=0.5, size=0.1),
        panel_grid_major_x=gg.element_line(color=text_col, alpha=0.5, size=0.1),
        panel_grid_minor_x=gg.element_line(color=text_col, alpha=0.2, size=0.1),
        panel_border=gg.element_rect(color=text_col, alpha=0.7, fill=None, size=0.1),
        axis_ticks=gg.element_blank(),
        figure_size = (6, 6)
    )
)
g.draw()

Our World in Data is an online publication and research organisation that provides comprehensive, data-driven insights on global issues, such as health, education, poverty, and environment. It aims to make complex global trends accessible by visualising data through interactive charts and research. Life expectancy, a key indicator in their research, reflects the average number of years a person can expect to live, influenced by factors like healthcare, lifestyle, and socio-economic conditions

Across the world, people are living longer - that’s the headline from a recent report from Our World in Data. The report uses data from the United Nations World Population Prospects (2022); Human Mortality Database (2023); Zijdeman, Richard and Filipa Ribeira da Silva (2015), Life Expectancy at Birth; Riley, J.C. (2005); and Estimates of Regional and Global Life Expectancy 1800 - 2001.

This artistic visualisation shows the top 8 ranking of countries with the highest life expectancy between 2011 and 2021. It’s immediately clear that the top three countries are have established themselves consistently at the top of the ranking, whereas there is more competition for the next few ranks.

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