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. …

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()), …

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 …

Plots may be created via the convenience function qplot() where arguments and defaults are meant to be similar to base R's plot() function. [19][20] More complex plotting capacity is available via ggplot() …

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.

The main difference is that, unlike base graphics, ggplot works with dataframes and not individual vectors. All the data needed to make the plot is typically be contained within the dataframe supplied …

ggplot() helpfully takes care of the remaining five elements by using defaults (default coordinate system, scales, faceting scheme, etc.). There are also a couple of plot elements not technically part of the …

ggplot(my_data, aes(x = col, y = row, fill = value)) + geom_raster() A next step in automating plotting of your data type is to write an method. These are autolayer() typically wrappers around geoms or stats …