jjallaire

---
title: "Dygraphs in R Markdown"
output: html_document
runtime: shiny
---

```{r}
library(dygraphs)

hw <- HoltWinters(ldeaths)

jjallaire

---
title: "Untitled"
output: 
  revealjs_presentation:
    theme: serif
    self_contained: false
    incremental: true
    transition: none
    pandoc_args: ["--css", "style.css"]
---

jjallaire

#' @export
justgage <- function(title, value, min, max, 
                     label = NULL, width = 200, height = 160) {
  
  structure(class = "justgage", list(
    title = title,
    label = label,
    value = value,
    min = min,
    max = max,

jjallaire

library(shiny)
library(datasets)

# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
  
  # Return the requested dataset
  datasetInput <- reactive({
    switch(input$dataset,
           "rock" = rock,

jjallaire

library(shiny)

# Define server logic for random distribution application
shinyServer(function(input, output) {
  
  # Reactive expression to generate the requested distribution. This is 
  # called whenever the inputs change. The output functions defined 
  # below then all use the value computed from this expression
  data <- reactive({
    dist <- switch(input$dist,

jjallaire

library(shiny)

# Define server logic for slider examples
shinyServer(function(input, output) {
  
  # Reactive expression to compose a data frame containing all of the values
  sliderValues <- reactive({
    
    # Compose data frame
    data.frame(

jjallaire

library(shiny)
library(datasets)

# We tweak the "am" field to have nicer factor labels. Since this doesn't
# rely on any user inputs we can do this once at startup and then use the
# value throughout the lifetime of the application
mpgData <- mtcars
mpgData$am <- factor(mpgData$am, labels = c("Automatic", "Manual"))

jjallaire

library(shiny)
library(datasets)

# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {

  # By declaring databaseInput as a reactive expression we ensure that:
  #
  #  1) It is only called when the inputs it depends on changes
  #  2) The computation and result are shared by all the callers (it 

jjallaire

library(shiny)
library(datasets)

# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
  
  # Return the requested dataset
  datasetInput <- reactive({
    switch(input$dataset,
           "rock" = rock,

jjallaire

library(shiny)

# Define server logic required to generate and plot a random distribution
shinyServer(function(input, output) {
   
  # Expression that generates a plot of the distribution. The expression
  # is wrapped in a call to renderPlot to indicate that:
  #
  #  1) It is "reactive" and therefore should be automatically 
  #     re-executed when inputs change