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Author = "Daniel C. Jones"


Gadfly supports advanced plot composition techniques like faceting, stacking, and layering.


It is easy to make multiple plots that all share a common dataset and axis.

using Gadfly, RDatasets
set_default_plot_size(14cm, 8cm) # hide
iris = dataset("datasets", "iris")
plot(iris, xgroup="Species", x="SepalLength", y="SepalWidth",

Geom.subplot_grid can similarly arrange plots vertically, or even in a 2D grid if there are two shared axes.


To composite plots derived from different datasets, or the same data but different axes, a declarative interface is used. The [Tutorial](@ref Rendering) showed how such disparate plots can be horizontally arranged with hstack. Here we illustrate how to vertically stack them with vstack or arrange them in a grid with gridstack. These commands allow more customization in regards to tick marks, axis labeling, and other plot details than is available with Geom.subplot_grid.

using Gadfly, RDatasets, Compose
iris = dataset("datasets", "iris")
set_default_plot_size(14cm, 16cm) # hide
fig1a = plot(iris, x=:SepalLength, y=:SepalWidth, Geom.point)
fig1b = plot(iris, x=:SepalLength, Geom.density,
             Guide.ylabel("density"), Coord.cartesian(xmin=4, xmax=8))

hstack and vstack can be composed to create arbitrary arrangements of panels.


If all rows or columns have the same number of panels, it's easiest to use gridstack.

gridstack([p1 p2; p3 p4])

For each of these commands, you can leave a panel empty by passing in a Compose.context() object.

using Compose
set_default_plot_size(21cm, 16cm) # hide
fig1c = plot(iris, x=:SepalWidth, Geom.density,
             Guide.ylabel("density"), Coord.cartesian(xmin=2, xmax=4.5))
gridstack(Union{Plot,Compose.Context}[fig1a fig1c; fig1b Compose.context()])

Note that in this case the array must be explicitly typed.

Lastly, title can be used to add a descriptive string to the top of a stack.

title(hstack(p1,p2), "My creative title")


Draw multiple layers onto the same plot by inputing Layer objects to plot.

using Gadfly, RDatasets, Distributions, StatsBase
set_default_plot_size(14cm, 8cm)
iris = dataset("datasets", "iris")
xdata = sort(iris[:SepalWidth])
ydata = cumsum(xdata)
line = layer(x=xdata, y=ydata, Geom.line, Theme(default_color="red"))
bars = layer(iris, x=:SepalWidth,
plot(line, bars)

Note that here we used both the DataFrame and AbstractArrays interface to layer, as well a Theme object. See Themes for more information on the latter.

You can also share the same DataFrame across different layers:

     layer(x=:SepalLength, y=:SepalWidth),
     layer(x=:PetalLength, y=:PetalWidth, Theme(default_color="red")))

In this case, Gadfly labels the axes with the column names of first layer listed. If this is not what is desired, Guides may be explicitly added.

     layer(x=:SepalLength, y=:SepalWidth),
     layer(x=:PetalLength, y=:PetalWidth, Theme(default_color="red")),
     Guide.xlabel("length"), Guide.ylabel("width"), Guide.title("Iris data"),

Note that while layer can input Geometries, Statistics, and Themes, it can not input Scales, Coordinates, or Guides.

The sequence in which layers are drawn, whether they overlap or not, can be controlled with the order keyword. Layers with lower order numbers are rendered first. If not specified, the default order for a layer is 0. Layers which have the same order number are drawn in the reverse order in which they appear in plot's input arguments.

bars = layer(iris, x=:SepalWidth,
line = layer(iris, x=xdata, y=ydata, Geom.line, Theme(default_color="red"),
plot(bars, line)