ggplot2 02 Getting Started

Описание: In this lesson, you’ll learn how to get productive with ggplot2 fast—by thinking in the Grammar of Graphics and building plots layer-by-layer. We’ll use real datasets (including mpg fuel economy and economics time series) to practice the core workflow you’ll use in nearly every analysis.

You’ll see how to go from a basic scatterplot to richer, publication-ready visuals using aesthetic mappings, geoms, faceting, and scale/guide behavior—plus practical fixes for common problems like overplotting, confusing legends, and misleading bin widths.

What you’ll learn

The 3 essential components of a ggplot: data + aesthetics + geoms

How aes() works (and why constants inside aes() behave differently)

When to set aesthetics inside vs. outside aes() (and when to use I())

Using alpha and jitter to reduce overplotting

Faceting with facet_wrap() to compare groups cleanly

Choosing the right geom: geom_point(), geom_smooth(), geom_boxplot(), geom_violin(), geom_histogram(), geom_freqpoly(), geom_bar(), geom_col(), geom_line(), geom_path()

Smoothing options in geom_smooth() (loess vs GAM vs LM vs robust LM)

Histogram/binwidth best practices (and how bin choices change the story)

Bar chart gotchas: unsummarized vs presummarized data (geom_bar() vs geom_col())

Modifying axes with xlab()/ylab() and xlim()/ylim() (and what happens to out-of-range data)

Saving and reusing plots with ggsave() and saveRDS(), plus inspecting internals with ggplot_build() and layer helpers

Resources

ggplot2 book (online): https://ggplot2-book.org/

Book source (GitHub): https://github.com/hadley/ggplot2-book/

Practice
If you’re following along, pause after each section and try the embedded exercises (they’re designed to build intuition quickly).

If this helped, consider subscribing—this series builds toward a complete, graduate-level workflow for data visualization in R.

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