Dash for Julia

Project Status

As of v1.15.0 of Dash, Julia components can be generated in tandem with Python and R components. Interested in getting involved with the project? Sponsorship is a great way to accelerate the progress of open source projects like this one; please feel free to reach out to us! Just getting started? Check out the Dash for Julia User Guide!

Create beautiful, analytic applications in Julia

🚧 Dash.jl is a work-in-progress. Feel free to test the waters and submit issues.

Built on top of Plotly.js, React and HTTP.jl, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Julia code.

Installation

Please ensure that you are using a version of Julia >= 1.2.

To install the most recently released version:

pkg> add Dash DashCoreComponents DashHtmlComponents DashTable

To install the latest (stable) development version instead:

using Pkg
Pkg.add(PackageSpec(url="https://github.com/plotly/DashBase.jl.git"))
Pkg.add(PackageSpec(url="https://github.com/plotly/dash-html-components.git", rev="master"))
Pkg.add(PackageSpec(url="https://github.com/plotly/dash-core-components.git", rev="master"))
Pkg.add(PackageSpec(url="https://github.com/plotly/dash-table.git", rev="master"))
Pkg.add(PackageSpec(url="https://github.com/plotly/Dash.jl.git", rev="dev"))

Usage

Basic application

julia> using Dash
julia> using DashHtmlComponents
julia> using DashCoreComponents

julia> app = dash(external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"])

julia> app.layout = html_div() do
        html_h1("Hello Dash"),
        html_div("Dash.jl: Julia interface for Dash"),
        dcc_graph(
            id = "example-graph",
            figure = (
                data = [
                    (x = [1, 2, 3], y = [4, 1, 2], type = "bar", name = "SF"),
                    (x = [1, 2, 3], y = [2, 4, 5], type = "bar", name = "Montréal"),
                ],
                layout = (title = "Dash Data Visualization",)
            )
        )
    end

julia> run_server(app, "0.0.0.0", 8080)

• The DashApp struct represents a dashboard application.
• To make DashApp struct use dash(layout_maker::Function, name::String; external_stylesheets::Vector{String} = Vector{String}(), url_base_pathname="/", assets_folder::String = "assets") where layout_maker is a function with signature ()::Component
• Unlike the Python version where each Dash component is represented as a separate class, all components in Dash.jl are represented by struct Component.
• You can create Component specific for concrete Dash component by the set of functions in the form lowercase(<component package>)_lowercase(<component name>). For example, in Python html <div> element is represented as HTML.Div in Dash.jl it is created using function html_div
• The list of all supported components is available in docstring for Dash.jl module.
• All functions for a component creation have the signature (;kwargs...)::Component. List of key arguments specific for the concrete component is available in the docstring for each function.
• Functions for creation components which have children property have two additional methods (children::Any; kwargs...)::Component and (children_maker::Function; kwargs..)::Component. children must by string or number or single component or collection of components.
• make_handler(app::Dash; debug::Bool = false) makes a handler function for using in HTTP package.

Once you have run the code to create the Dashboard, go to http://127.0.0.1:8080 in your browser to view the Dashboard!

Basic Callback

julia> using Dash
julia> using DashHtmlComponents
julia> using DashCoreComponents

julia> app = dash(external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"])

julia> app.layout = html_div() do
        dcc_input(id = "my-id", value="initial value", type = "text"),
        html_div(id = "my-div")
    end

julia> callback!(app, Output("my-div", "children"), Input("my-id", "value")) do input_value
    "You've entered $(input_value)"
end

julia> run_server(app, "0.0.0.0", 8080)

• You can make your dashboard interactive by register callbacks for changes in frontend with function callback!(func::Function, app::Dash, output, input, state)
• Inputs and outputs (and states, see below) of callback can be Input, Output, State objects or vectors of this objects
• Callback function must have the signature(inputs..., states...), and provide a return value comparable (in terms of number of elements) to the outputs being updated.

States and Multiple Outputs

julia> using Dash
julia> using DashHtmlComponents
julia> using DashCoreComponents

julia> app = dash(external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"])

julia> app.layout = html_div() do
        dcc_input(id = "my-id", value="initial value", type = "text"),
        html_div(id = "my-div"),
        html_div(id = "my-div2")
    end

julia> callback!(app, [Output("my-div","children"), Output("my-div2","children")], Input("my-id", "value"), State("my-id", "type")) do input_value, state_value
    "You've entered $(input_value) in input with type $(state_value)",
    "You've entered $(input_value)"
end
julia> run_server(app, "0.0.0.0", 8080)

Comparison with original Python syntax

component naming:

html.Div => html_div, dcc.Graph => dcc_graph and etc

component creation:

Just as in Python, functions for declaring components have keyword arguments, which are the same as in Python. html_div(id="my-id", children="Simple text"). For components which declare children, two additional signatures are available. (children; kwargs..) and (children_maker::Function; kwargs...) so one can write html_div("Simple text", id="my-id") for simple elements, or choose an abbreviated syntax with do syntax for complex elements:

html_div(id="outer-div") do
    html_h1("Welcome"),
    html_div(id="inner-div") do
    ......
    end
end

application and layout:

• python:

app = dash.Dash("Test", external_stylesheets=external_stylesheets)
app.layout = html.Div(children=[....])

• Dash.jl:

app = dash("Test", external_stylesheets=external_stylesheets)

app.layout = html_div() do
    ......
   end

callbacks:

• Python:

@app.callback(Output('output', 'children'),
              [Input('submit-button', 'n_clicks')],
              [State('state-1', 'value'),
               State('state-2', 'value')])
def update_output(n_clicks, state1, state2):
.....

• Dash.jl:

callback!(app, Output("output", "children"),
              [Input("submit-button", "n_clicks")],
              [State("state-1", "value"),
               State("state-2", "value")]) do  n_clicks, state1, state2
.....
end

Be careful - in Dash.jl states come first in an arguments list.

JSON:

I use JSON2.jl for JSON serialization/deserialization, so in callbacks all JSON objects are NamedTuples rather than dictionaries. Within component properties you can use both Dict and NamedTuple for JSON objects.

Note when declaring elements with a single properly that layout = (title = "Test graph") is not interpreted as a NamedTuple by Julia - you'll need to add a comma when declaring the layout, e.g. layout = (title = "Test graph",)