A comprehensive R-based project that analyzes the Gapminder dataset (1952–2007) to explore global trends in life expectancy, GDP per capita, and population. The analysis produces a suite of 12 visualizations, fits a regression model, and renders an HTML report.

1. Project Overview
2. Features & Charts
3. Prerequisites
4. Installation
5. Usage
6. File Structure
7. Scripts & Automation
8. Docker & Containerization
9. Extending & Customizing
10. Data Source & Citations
11. License

This repository contains everything needed to:

• Fetch and prepare the Gapminder dataset via the gapminder R package.
• Compute key metrics (global averages, per-country snapshots).
• Visualize 12 charts illustrating temporal trends, economic relationships, continental comparisons, and population impacts.
• Model the relationship between life expectancy and GDP per capita.
• Automate the workflow via shell scripts, a Makefile, and Docker.
• Render a polished RMarkdown report (Gapminder_report.html).

Ideal for students, educators, data scientists, and anyone interested in global development metrics.

1. Global Average Life Expectancy Over Time
2. Global Average GDP per Capita Over Time
3. Global Total Population Over Time
4. Scatter: Life Expectancy vs. GDP per Capita (2007)
5. Regression: Life Expectancy ~ log(GDP per Capita)
6. Top 10 Countries by Life Expectancy (2007)
7. Boxplot: Life Expectancy by Continent (2007)
8. Violin: GDP per Capita by Continent (2007)
9. Line: Average Life Expectancy by Continent Over Time
10. Density: Life Expectancy Distribution by Continent (2007)
11. Heatmap: Average Life Expectancy by Year & Continent
12. Bubble Plot: GDP vs. Life Expectancy Sized by Population (2007)

Each plot is saved to gap-<index>.png and displayed in the R session.

• R ≥ 4.0
• RStudio (optional, but recommended)
• Internet connection (to install R packages and fetch data)

• ggplot2
• dplyr
• gapminder
• scales
• viridis
• tidyr
• forcats
• zoo
• rmarkdown (for report rendering)

The main script auto-installs any missing packages.

1. Clone this repository:

   git clone https://github.com/yourusername/gapminder-analysis.git
   cd gapminder-analysis

2. Install R (if not already).

3. (Optional) Copy .env.example to .env to customize environment variables.

bash scripts/run_gapminder.sh

All 12 charts will print and save as gap-1.png … gap-12.png.

bash scripts/render_report.sh

This produces Gapminder_report.html.

make report

Equivalent to running analysis and report steps.

.
├── Gapminder_Analysis.R       # Main R script
├── Gapminder_Analysis.Rmd     # RMarkdown report
├── Gapminder_report.html      # Generated HTML report
├── gap-*.png                  # Saved plot images
├── scripts/
│   ├── run_gapminder.sh       # Runs the analysis script
│   └── render_report.sh       # Renders the RMarkdown
├── Makefile                   # Make targets for automation
├── Dockerfile                 # Container definition
├── docker-compose.yml         # Compose for containerized run
├── .env.example               # Sample environment config
└── README.md                  # This file

• scripts/run_gapminder.sh Runs Gapminder_Analysis.R to produce and save all plots.

• scripts/render_report.sh Renders Gapminder_Analysis.Rmd to Gapminder_report.html.

• Makefile

  • make analysis — run shell script to generate plots
  • make report — run analysis + render report
  • make clean — delete images & report

Build and run in a container:

docker-compose up --build

This will:

1. Install system and R dependencies
2. Run the analysis script
3. Render the RMarkdown report

All files are shared via a bind mount.

• Add more plots: insert new code chunks in Gapminder_Analysis.R or .Rmd.
• Parameterize: use YAML or command-line args to filter years or continents.
• CI/CD: integrate with GitHub Actions to auto-build the report on push.
• Data sources: replace Gapminder with another tidy dataset for similar workflows.

• Gapminder dataset via R package:

  install.packages("gapminder")

• R Packages: see CRAN for ggplot2, dplyr, etc.

This project is licensed under the MIT License. See LICENSE for details.