However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), …

plotly: turn your ggplot interactive Another awesome feature of ggplot2 is its link with the plotly library. If you know how to make a ggplot2 chart, you are 10 seconds away to rendering an interactive version. …

Nov 24, 2025 · A curated ggplot2 hub for R. Learn geoms, axes/scales, labels/annotations, themes, faceting, colors, and saving plots—each with working code and examples.

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes …

However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), …

The expression variable is evaluated within the layer data, so there is no need to refer to the original dataset (i.e., use ggplot (df, aes (variable)) instead of ggplot (df, aes (df$variable))). The names for x …

ggplot2 is an incredibly powerful tool to create great charts with R. But it has a bit of a learning curve. This tutorial shows you everything you need to know to get started with ggplot

You’ll learn the basics of ggplot() along with some useful “recipes” to make the most important plots. ggplot() allows you to make complex plots with just a few lines of code because it’s based on a rich …

As the first step in many plots, you would pass the data to the ggplot() function, which stores the data to be used later by other parts of the plotting system.

However, in most cases you start with ggplot (), supply a dataset and aesthetic mapping (with aes ()). You then add on layers (like geom_point () or geom_histogram ()), scales (like scale_colour_brewer …