Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
Dash for R
Create beautiful, analytic web applications in R.
Installation
https://dashr.plotly.com/installation
🛑 Make sure you're on at least version3.0.2of R. You can see what version of R you have by enteringversionin the R CLI. CRAN is the easiest place to download the latest R version.
As of 2020-06-04, dash and the currently released versions of all core component libraries are available for download via CRAN! Installing dash and its dependencies is as simple as
install.packages("dash")Users who wish to install (stable) development versions of the package as well as Dash components from GitHub may instead use install_github and specify the development branch:
install.packages(c("fiery", "routr", "reqres", "htmltools", "base64enc", "plotly", "mime", "crayon", "devtools"))
# installs dashHtmlComponents, dashCoreComponents, and dashTable
# and will update the component libraries when a new package is released
devtools::install_github("plotly/dashR", ref="dev", upgrade = TRUE)Then, to load the packages in R:
library(dash)
library(dashHtmlComponents)
library(dashCoreComponents)
library(dashTable)That's it!
Getting Started
https://dashr.plotly.com/getting-started
The R package dash makes it easy to create reactive web applications powered by R. It provides an R6 class, named Dash, which may be initialized via the new() method.
library(dash)
library(dashHtmlComponents)
library(dashCoreComponents)
app <- Dash$new()Similar to Dash for Python and Dash for Julia, every Dash for R application needs a layout (i.e., user interface) and a collection of callback functions which define the updating logic to perform when input value(s) change. Take, for instance, this basic example of formatting a string:
app$layout(
htmlDiv(
list(
dccInput(id = "inputID", value = "initial value", type = "text"),
htmlDiv(id = "outputID")
)
)
)
app$callback(output = list(id="outputID", property="children"),
params = list(input(id="inputID", property="value"),
state(id="inputID", property="type")),
function(x, y) {
sprintf("You've entered: '%s' into a '%s' input control", x, y)
}
)
app$run_server(showcase = TRUE)Here the showcase = TRUE argument opens a browser window and automatically loads the Dash app for you.
Hello world example using dccGraph
app <- Dash$new()
app$layout(
htmlDiv(
list(
dccInput(id = "graphTitle",
value = "Let's Dance!",
type = "text"),
htmlDiv(id = "outputID"),
dccGraph(id = "giraffe",
figure = list(
data = list(x = c(1,2,3), y = c(3,2,8), type = "bar"),
layout = list(title = "Let's Dance!")
)
)
)
)
)
app$callback(output = list(id = "giraffe", property = "figure"),
params = list(input("graphTitle", "value")),
function(newTitle) {
rand1 <- sample(1:10, 1)
rand2 <- sample(1:10, 1)
rand3 <- sample(1:10, 1)
rand4 <- sample(1:10, 1)
x <- c(1,2,3)
y <- c(3,6,rand1)
y2 <- c(rand2,rand3,rand4)
df = data.frame(x, y, y2)
list(
data =
list(
list(
x = df$x,
y = df$y,
type = "bar"
),
list(
x = df$x,
y = df$y2,
type = "scatter",
mode = "lines+markers",
line = list(width = 4)
)
),
layout = list(title = newTitle)
)
}
)
app$callback(output = list(id = "outputID", property = "children"),
params = list(input("graphTitle", "value"),
state("graphTitle", "type")),
function(x, y) {
sprintf("You've entered: '%s' into a '%s' input control", x, y)
}
)
app$run_server(showcase = TRUE)Screenshot of "Hello World" app
