Data Visualization in ggplot2

This going to be an hour practical session on creating clear, professional charts from raw data using R. Move beyond default Excel graphs to publication-ready visuals that communicate your findings effectively.

Why R for viz: R’s `ggplot2` package builds charts layer by layer. You control colors, labels, themes, and fonts with code — so every graph can be updated and reproduced in seconds. No more manual formatting each time data changes.

What we’ll cover:

1. The grammar: Build bar charts, line plots, and scatterplots using `ggplot(data, aes(x, y)) + geom__()`
2. Polish: Add titles, fix axis labels, choose colorblind-friendly palettes, apply clean themes
3. Maps & beyond: Plot survey locations or regional data using shapefiles with `sf` + `ggplot2`
4. Export: Save high-resolution PNG/PDF for reports, papers, and presentations

We’ll work with datasets to show real use cases.

All skill levels are welcome. If you’ve made a chart in Excel, you can do this in R. Basic R knowledge helps but not required. Bring a laptop.

Turn your data tables into stories. Leave with a reusable `ggplot_template.R` for bar, line, map, and scatter plots that you can adapt to your own projects.

_Good analysis deserves good visuals. R makes both possible.