Multiple Chart Types in Julia

Chart Types versus Trace Types

Plotly figures support defining subplots of various types (e.g. cartesian, polar, [3-dimensional], [maps] etc) with attached traces of various compatible types (e.g. scatter, bar, choropleth, surface etc). This means that Plotly figures are not constrained to representing a fixed set of "chart types" such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.

Multiple Trace Types

The first argument to the plot function accepts and array of traces. Each of these traces can be of a different (but compatible) type. For example, below we have a line and bar chart in the same figure:

using PlotlyJS

fruits = ["apples", "oranges", "bananas"]
plot([
    scatter(x=fruits, y=[1, 3, 2], name="This year", mode="lines"),
    bar(x=fruits, y=[2, 1, 3], name="Last year", mode="lines")
])

A Contour and Scatter Plot of the Method of Steepest Descent

The example below shows how to display both a scatter plot and contour plot on the same axes.

This is useful for demonstrating progress in a steepest descent algorithm

using PlotlyJS, HTTP, JSON

response = HTTP.get("https://raw.githubusercontent.com/plotly/datasets/master/steepest.json")
data = JSON.parse(String(response.body))

plot([
    contour(
        z=data["contour_z"][1],
        y=data["contour_y"][1],
        x=data["contour_x"][1],
        ncontours=30,
        showscale=false
    ),
    scatter(
        x=data["trace_x"], y=data["trace_y"], mode="markers+lines",
        name="steepest", line_color="black"
    )
])

Reference

See https://plotly.com/julia/reference/ for more information and attribute options!