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https://github.com/fgazzelloni/updvwr

Unlocking the power of data visualization with R: This is a collection of data visualizations with code made with R
https://github.com/fgazzelloni/updvwr

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Unlocking the power of data visualization with R: This is a collection of data visualizations with code made with R

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README

          

# Unlocking the Power of Data Visualization with R

> “Visualization is not an accessory to analysis — it’s the language through which data speaks.”

## Overview

This is a collection of reproducible examples demonstrating how to transform raw data into visual stories using R. The project explores a variety of datasets and visualization techniques, showing how the R ecosystem enables creative and transparent data communication.

Each example highlights a different aspect of visualization, from exploratory graphics to storytelling and mapping, using open data from the [#TidyTuesday](https://github.com/rfordatascience/tidytuesday), [#30DayChartChallenge](https://github.com/30DayChartChallenge), [#30daymapchallenge](https://30daymapchallenge.com/), and [#duboischallenge](https://www.datavisualizationsociety.org/news/2025/2/3/2025-du-bois-visualization-challenge) projects and other public sources.

## Motivation

Data visualization is not just about making charts, it’s about understanding patterns, communicating insights, and promoting reproducibility.
This repository aims to help learners and practitioners:

- Discover the versatility of R for visual analytics;
- Learn by example through real-world datasets;
- Apply principles of clarity, design, and reproducibility.

## Structure

The repository is organized into thematic sections, including:

- **Basics of ggplot2** – foundational plots and aesthetic mapping
- **Advanced Visualizations** – facets, themes, and custom functions
- **Spatial Data** – creating maps with `sf`, `rnaturalearth`, and `ggplot2`
- **Interactive Graphics** – visualizing data dynamically with `plotly` and `leaflet`
- **Case Studies** – reproducible analyses from #TidyTuesday datasets

Each folder contains a Quarto (`.qmd`) or R Markdown (`.Rmd`) file, code snippets, and rendered output.

## Tools and Packages

The examples make use of several core packages from the R ecosystem:

```r
library(ggplot2)
library(dplyr)
library(tidyr)
library(sf)
library(viridis)
library(patchwork)
library(rnaturalearth)
```

Other packages such as **plotly**, **leaflet**, and **gganimate** are introduced for interactive and animated graphics.

### Example Highlight

One featured example visualizes **Disability-Adjusted Life Years (DALYs) across countries from 1990–2021** — created for **Day 1 (“Points”)** of the **#30DayMapChallenge**. Each point represents the centroid of a country, coloured according to its average DALY value, revealing global disparities in infectious disease burden.



### How to Use

Clone this repository and open it in RStudio or VS Code:

`git clone https://github.com//data-visualization-with-R.git`

`quarto::quarto_render("path/to/file.qmd")`

All examples are self-contained and reproducible.

### License

This project is licensed under the MIT License.

### Author

Developed and maintained by Federica Gazzelloni
📧 fede.gazzelloni@gmail.com
🌐 [federicagazzelloni.com](https://federicagazzelloni.com/)