This is the 1st chapter of the Dash Fundamentals.
The next chapter covers Dash callbacks.

library(dash)
library(dashCoreComponents)

# Create a Dash app
app <- dash_app()

# Set the layout of the app
app %>% set_layout(
  h1('Hello Dash'),
  div("Dash: A web application framework for your data."),
  dccGraph(
    figure = list(
      data = list(
        list(
          x = list(1, 2, 3),
          y = list(4, 1, 2),
          type = 'bar',
          name = 'SF'
        ),
        list(
          x = list(1, 2, 3),
          y = list(2, 4, 5),
          type = 'bar',
          name = 'Montr\U{00E9}al'
        )
      ),
      layout = list(title = 'Dash Data Visualization')
    )
  )
)

# Run the app
app %>% run_app()

Hello Dash

Dash: A web application framework for your data.

> source("app.R")
Fire started at 127.0.0.1:8050
start: 127.0.0.1:8050

Visit http://127.0.0.1:8050/
in your web browser. You should see an app that looks like the one above.

Note:

The layout is composed of a tree of “components” such as
div
and dccGraph.
The
dash package contains an html list which
has a component for every HTML tag. For convenience, some common HTML tags have shortcuts as functions; e.g. h1() and div() are aliases for html$h1() and html$div().
The h1('Hello Dash')
component generates a <h1>Hello Dash<h1> HTML element in your app.
Not all components are pure HTML.
DashCoreComponents
contains higher-level components that are interactive and are generated with
JavaScript, HTML, and CSS through the React.js library.
Each component is described entirely through the parameters to its function.
For pure HTML components, any named argument becomes an attribute on the HTML tag, while any unnamed argument becomes a child. For example, div(id = "name", "Joe") results in <div>Joe<div>.
The fonts in your app will look a little bit different than what is displayed here.
This app is using a custom CSS stylesheet and Dash Enterprise Design Kit
to modify the default styles of the elements. You can learn more about custom CSS in the
CSS tutorial.

A Quick Note about Syntax

You’ll notice that most examples on this website use the pipe operator (%>%) to manipulate the Dash app object. The above code can be rewritten to fully embrace the pipe operator as

dash_app() %>%
  set_layout(...) %>%
  run_app()

or it can be rewritten without the pipe operator as

app <- dash_app()
set_layout(app, ...)
run_app(app)

Functionally there is no difference between these three, so you can choose whichever style best suits you.

Making Your First Change

You can change the title “Hello Dash” in your app or change the
x or y data. Save the file and restart the app to see your changes.

More about HTML Components

Any valid HTML tag can be added to a Dash app using the HTML components in the html list. For example, a div component that results in a <div> tag in the app can be created with html$div().

Some common HTML tags, such as div and button, are also available as functions for your convenience, so you can simply use div() or button(). Run ?dash::html to see the full list of HTML tags that support this short form.

An HTML tag can contain arbitrary attributes and content: any named arguments to the function become attributes in the HTML tag, and any unnamed arguments become children. The children can either be simple text or they can be other Dash components themselves.

Let’s customize the text in our app by modifying the inline styles of the components.
Create a file named app.R with the following code:

library(dash)
library(dashCoreComponents)

app <- dash_app()

colors <- list(
  background = '#111111',
  text = '#7FDBFF'
)

app %>% set_layout(
  div(
    h1(
      'Hello Dash',
      style = list(
        textAlign = 'center',
        color = colors$text
      )
    ),
    div(
      'Dash for R: A web application framework for R.',
      style = list(
        textAlign = 'center',
        color = colors$text
      )
    ),
    dccGraph(
      id = 'example-graph-2',
      figure = list(
        data = list(
          list(
            x = list(1, 2, 3),
            y = list(4, 1, 2),
            type = 'bar',
            name = 'SF'
          ),
          list(
            x = list(1, 2, 3),
            y = list(2, 4, 5),
            type = 'bar',
            name = 'Montr\U{00E9}al'
          )
        ),
        layout = list(
          plot_bgcolor = colors$background,
          paper_bgcolor = colors$background,
          font = list(color = colors$text)
        )
      )
    ),
    style = list(backgroundColor = colors$background)
  )
)

app %>% run_app()

Hello Dash

Dash: A web application framework for your data.

In this example, we modified the inline styles of the
title h1
and subtitle divcomponents with the style property.

h1(
  'Hello Dash',
  style = list(
    textAlign = 'center',
    color = '#7FDBFF'
  )
)

The above code is rendered in the Dash app as
<h1>Hello Dash<h1>.

There are a few important differences between the Dash HTML components
and the HTML attributes:

The style property in HTML is a semicolon-separated string. In Dash,
you can just supply a named list.
The names in the style list are camelCased.
So, instead of text-align, it’s textAlign.
The HTML class attribute is className in Dash.
The HTML for attribute is htmlFor in Dash.
A special property n_clicks is automatically added to every HTML tag. This property represents the number of times that an HTML element has been clicked on, which is mostly useful for <button>s. If not explicitly initialized to a certain integer, its default value is NULL initially.

Besides that, all of the available HTML attributes and tags are available
to you within your R context.

A few more useful tips for Dash HTML components:

Named arguments with a value of NULL will not get rendered. This can be useful for conditionally including an attribute.
Named arguments with a value of NA will be rendered as boolean attributes.

Reusable Components

By writing our markup in R, we can create complex reusable components like tables without switching contexts or languages.

Here’s a quick example that generates a Table from a
CSV file. Create a file named app.R with the following code:

library(dash)
library(dashCoreComponents)

df <- read.csv(url("https://gist.githubusercontent.com/chriddyp/c78bf172206ce24f77d6363a2d754b59/raw/c353e8ef842413cae56ae3920b8fd78468aa4cb2/usa-agricultural-exports-2011.csv"))

generate_table <- function(df, nrows = 10) {
  nrows <- min(nrows, nrow(df))
  rows <- lapply(
    seq(nrows),
    function(row) {
      html$tr(lapply(as.character(df[row, ]), html$td))
    }
  )
  header <- html$td(children = lapply(names(df), html$th))
  html$table(
    c(list(header), rows)
  )
}

app <- dash_app()

app %>% set_layout(
  div(
    h4('US Agriculture Exports (2011)'),
    generate_table(df),
    style= list("overflow-x" = "scroll")
  )
)

app %>% run_app()

US Agriculture Exports (2011)

Unnamed: 0	state	total exports	beef	pork	poultry	dairy	fruits fresh	fruits proc	total fruits	veggies fresh	veggies proc	total veggies	corn	wheat	cotton
	Alabama	1390.63	34.4	10.6	481.0	4.06	8.0	17.1	25.11	5.5	8.9	14.33	34.9	70.0	317.61
1	Alaska	13.31	0.2	0.1		0.19				0.6	1.0	1.56
2	Arizona	1463.17	71.3	17.9		105.48	19.3	41.0	60.27	147.5	239.4	386.91	7.3	48.7	423.95
3	Arkansas	3586.02	53.2	29.4	562.9	3.53	2.2	4.7	6.88	4.4	7.1	11.45	69.5	114.5	665.44
4	California	16472.88	228.7	11.1	225.4	929.95	2791.8	5944.6	8736.4	803.2	1303.5	2106.79	34.6	249.3	1064.95
5	Colorado	1851.33	261.4	66.0	14.0	71.94	5.7	12.2	17.99	45.1	73.2	118.27	183.2	400.5
6	Connecticut	259.62	1.1	0.1	6.9	9.49	4.2	8.9	13.1	4.3	6.9	11.16
7	Delaware	282.19	0.4	0.6	114.7	2.3	0.5	1.0	1.53	7.6	12.4	20.03	26.9	22.9
8	Florida	3764.09	42.6	0.9	56.9	66.31	438.2	933.1	1371.36	171.9	279.0	450.86	3.5	1.8	78.24
9	Georgia	2860.84	31.0	18.9	630.4	38.38	74.6	158.9	233.51	59.0	95.8	154.77	57.8	65.4	1154.07

In fact, creating simple tables like this is such a common task that Dash provides a special function for it: simple_table(). You can also use dash::dashDataTable() for more complex tables.

More about Visualization

The DashCoreComponents
package includes a component called
dccGraph.

dccGraph
renders interactive data visualizations using the open source
plotly.js JavaScript graphing
library. Plotly.js supports over 35 chart types and renders charts in
both vector-quality SVG and high-performance WebGL.

The figure argument in thedccGraph
component is the same figure argument that is used by plotly.py, Plotly’s
open source graphing library.
Check out the plotly.py documentation and gallery
to learn more.

Here’s an example that creates a scatter plot from a
CSV file. Create a file named app.R with the following code:

library(dash)
library(dashCoreComponents)

df <- read.csv(
  file = "https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv",
  stringsAsFactor = FALSE,
  check.names = FALSE
)

continents <- unique(df$continent)

data_gdp_life_exp_2007 <- lapply(
  continents,
  function(cont) {
    list(
      x = df$gdpPercap[df$continent == cont],
      y = df$lifeExp[df$continent == cont],
      opacity = 0.7,
      text = df$country[df$continent == cont],
      mode = 'markers',
      name = cont,
      marker = list(size = 15, line = list(width = 0.5, color = 'white'))
    )
  }
)

app <- dash_app()

app %>% set_layout(
  dccGraph(
    id = 'life-exp-vs-gdp',
    figure = list(
      data =  data_gdp_life_exp_2007,
      layout = list(
        xaxis = list('type' = 'log', 'title' = 'GDP Per Capita'),
        yaxis = list('title' = 'Life Expectancy'),
        margin = list('l' = 40, 'b' = 40, 't' = 10, 'r' = 10),
        legend = list('x' = 0, 'y' = 1),
        hovermode = 'closest'
      )
    )
  )
)

app %>% run_app()

These graphs are interactive and responsive.
Hover over points to see their values,
click on legend items to toggle traces,
click and drag to zoom,
hold down shift, and click and drag to pan.

Markdown

While Dash exposes HTML through DashHtmlComponents, it can be tedious to write your copy in HTML. For writing blocks of text, you can use the
dccMarkdown component in
DashCoreComponents. Create a file named app.R with the following code:

library(dash)
library(dashCoreComponents)

markdown_text <- "
### Dash and Markdown

Dash apps can be written in Markdown.
Dash uses the [CommonMark](http://commonmark.org/)
specification of Markdown.
Check out their [60 Second Markdown Tutorial](http://commonmark.org/help/)
if this is your first introduction to Markdown!
"

dash_app() %>%
  set_layout(dccMarkdown(markdown_text)) %>%
  run_app()

Dash and Markdown

Dash apps can be written in Markdown.
Dash uses the CommonMark
specification of Markdown.
Check out their 60 Second Markdown Tutorial
if this is your first introduction to Markdown!

Core Components

DashCoreComponents
includes a set of higher-level components like dropdowns, graphs, markdown blocks, and more.

Like all Dash components, they are described entirely declaratively.
Every option that is configurable is available as a keyword argument
of the component.

We’ll see many of these components throughout the tutorial.
You can view all of the available components in the
Dash Core Components overview.

Here are a few of the available components.
Create a file named app.R with the following code:

library(dash)
library(dashCoreComponents)

app <- dash_app()

app %>% set_layout(
  div(
    html$label('Dropdown'),
    dccDropdown(
      options = list(list(label = "New York City", value = "NYC"),
                     list(label = "Montreal", value = "MTL"),
                     list(label = "San Francisco", value = "SF")),
      value = 'MTL'
    ),

    html$label('Multi-Select Dropdown'),
    dccDropdown(
      options = list(list(label = "New York City", value = "NYC"),
                     list(label = "Montreal", value = "MTL"),
                     list(label = "San Francisco", value = "SF")),
      value = list('MTL', 'SF'),
      multi = TRUE
    ),

    html$label('Radio Items'),
    dccRadioItems(
      options = list(list(label = "New York City", value = "NYC"),
                     list(label = "Montreal", value = "MTL"),
                     list(label = "San Francisco", value = "SF")),
      value = 'MTL'
    ),

    html$label('Checkboxes'),
    dccChecklist(
      options = list(list(label = "New York City", value = "NYC"),
                     list(label = "Montreal", value = "MTL"),
                     list(label = "San Francisco", value = "SF")),
      value = list('MTL', 'SF')
    ),

    html$label('Text Input'),
    dccInput(value = 'MTL', type = 'text'),

    html$label('Slider'),
    dccSlider(
      min = 0,
      max = 9,
      marks = c("", "Label 1", 2:5),
      value = 5
    ),
    style = list('columnCount' = 2)
  )
)

app %>% run_app()

Dropdown
Multi-Select Dropdown
Radio Items

Checkboxes

Text Input

Slider

Help

Dash components are declarative: every configurable aspect of these
components is set during instantiation as a keyword argument.

Call ? in your R terminal on any of the components to
learn more about a component and its available arguments.

> ?dccDropdown

Summary

The layout of a Dash app describes what the app looks like.
The layout is a hierarchical tree of components, or a list of components (in Dash 2.17 and later).

The
Dash package
provides functions for all of the HTML
tags and the keyword arguments describe the HTML attributes like style, class, and id.
The DashCoreComponents
package
generates higher-level components like controls and graphs.

For reference, see:

The next part of the Dash Fundamentals covers how to make these apps interactive.
Dash Fundamentals Part 2: Basic Callbacks