Plotting with Plots.jl

Plots.jl is not itself a rendering engine — it is a unified, consistent plotting API that delegates the actual drawing work to a swappable 'backend' package, most commonly GR (the fast default, good for large datasets and quick iteration), PyPlot (a wrapper around Python's matplotlib), or PlotlyJS (interactive, browser-based plots with zoom and hover tooltips). You select a backend by calling its activation function once, such as gr() or plotlyjs(), after which every subsequent plot() call in your session uses that backend, meaning the same plotting code can produce a fast static PNG one day and an interactive HTML chart the next simply by switching the active backend.

The core function is plot(x, y; kwargs...), which creates a new figure, while plot!(x, y; kwargs...) — note the bang — mutates the current figure in place by adding a new series to it rather than starting a fresh plot, a convention Julia uses consistently for any function that modifies its argument. Related shorthand functions like scatter(), bar(), histogram(), and heatmap() call plot() internally with seriestype set appropriately, and any of them accepts common attributes as keyword arguments such as label, xlabel, ylabel, title, linewidth, and color, which apply uniformly across every backend even though the backends render them differently under the hood.

Plots.jl exposes a large, consistent attribute vocabulary — legend=:topright, xlims=(0, 10), yscale=:log10, seriestype=:bar, and dozens more — that works identically regardless of which backend renders the figure, so learning the attribute names once transfers across every backend you might switch to later. Attributes can be set globally for the whole session with default(fontfamily="Computer Modern", linewidth=2), per-plot by passing them to plot(), or per-series when using plot!() to layer multiple series with different styling onto the same axes, giving fine control without needing backend-specific code anywhere in your script.

Multiple subplots are arranged in a single figure using the layout keyword, either as a simple grid like layout=(2,2) for a 2-row, 2-column arrangement, or via the more expressive @layout macro for irregular grids, such as @layout [a{0.3h}; b c] which creates one wide panel on top and two narrower panels below with a specific height ratio. Each individual subplot in the grid can be targeted for further customization with plot!(p, subplot=i, ...), and this layout system composes naturally with the same plot()/plot!() and attribute vocabulary used for single-panel figures, so nothing new needs to be learned beyond the layout specification itself.

Any figure can be written to disk with savefig("filename.ext"), and Plots.jl infers the output format entirely from the file extension — .png and .svg work with essentially every backend, .pdf gives a scalable vector format well suited for print or publication, and interactive backends like PlotlyJS can export standalone .html files that retain zoom and hover interactivity even when opened outside of Julia. For high-resolution raster output intended for print or presentation slides, passing dpi=300 to plot() (or setting it via the size and dpi keyword combination) avoids the blurry, pixelated look that a default-resolution PNG produces when scaled up.