The Gapminder Dataset

The Gapminder dataset is a well-known data resource often used for data analysis and visualization. The data source, Systema Globalis, contains data on income, life expectancy and child mortality by country and is used by Gapminder. This dataset contains information about global development trends, focusing on indicators like population, life expectancy, and GDP per capita across countries and regions over time. This dataset is particularly useful for exploring relationships between socio-economic and health variables and how they evolve.

Key Features of the Gapminder Dataset:

• Country: The name of the country or region.

• Year: The year for which the data is recorded.

• Life Expectancy: The average lifespan of people in a given country.

• Population: The total population of the country.

• GDP per Capita: The gross domestic product divided by the population, reflecting the average economic output per person.

• Region: Country is divided into region4, four world regions, and region6, six world regions.

Example of Gapminder Data:

Here is a subset of the datset in 2007.

Country	Year	Life Expectancy	Population	GDP per Capita
Afghanistan	2007	43.828	31889923	974.5803384
Albania	2007	76.423	3600523	5937.029526
Algeria	2007	72.301	33333216	6223.367465
Angola	2007	42.731	12420476	4797.231267

Why Use the Gapminder Dataset?

The Gapminder dataset provides numerous benefits.

• Versatility: It allows for a variety of analyses, from simple univariate plots to complex multivariate visualizations.

• Time Series Analysis: The dataset spans multiple years, making it ideal for studying trends and changes over time.

• Cross-Disciplinary Applications: Useful for economics, public health, and social sciences.

Common Visualizations with Gapminder

1. Scatter Plots: Show relationships between variables like GDP per capita and life expectancy.

2. Line Charts: Track changes in a specific variable over time, such as population growth.

3. Bubble Charts: Combine multiple dimensions, such as population size, GDP per capita, and life expectancy, for rich visual storytelling.

Here’s an example of creating a bubble chart using Plotly and the Gapminder dataset:

import plotly.express as px

df = px.data.gapminder()

# Create a bubble chart
fig = px.scatter(
    df[df['year'] == 2007],  # Filter for the year 2007
    x='gdpPercap', 
    y='lifeExp', 
    size='pop', 
    color='continent', 
    hover_name='country', 
    log_x=True,  # Log scale for better readability
    title='Gapminder Data: GDP vs Life Expectancy (2007)'
)
fig.show()

The Gapminder dataset is an excellent tool for teaching and practicing data visualization and analysis, providing insights into global development and its disparities over time.