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new shiny app 09_student
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inst/shiny/09_student/server.R

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#
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library(shiny)
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# Define server logic required to draw a histogram
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shinyServer(function(input, output) {
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value <- reactive({
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means_n <- numeric(10000) # Vecteur de 10000 valeurs
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for (i in 1:10000)
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means_n[i] <- mean(rnorm(input$numb, mean = 8, sd = 2))
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df_test <- tibble::enframe(means_n, name = NULL)
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df_test
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})
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output$mean_value <- renderText({
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t <- value()
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mean(t$value)
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})
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output$sd_value <- renderText({
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t <- value()
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sd(t$value)
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})
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output$mean_plot <- renderPlot({
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t <- value()
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#hist(t$value)
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chart::chart(data = t, ~ value) +
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ggplot2::geom_histogram(bins = 30) +
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ggplot2::labs(x = "Moyenne des échantillons", y = "Dénombrement")
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})
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})

inst/shiny/09_student/ui.R

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#
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library(shiny)
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# Define UI for application that draws a histogram
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shinyUI(fluidPage(
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# Application title
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titlePanel("Effet de l'effectif"),
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# Sidebar with a slider input for number of bins
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sidebarLayout(
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sidebarPanel(
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sliderInput("numb",
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"Nombre d'individus échantillonner observations",
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min = 3,
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max = 100,
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value = 9),
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withMathJax(),
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p("La moyenne d'un échantillon suit l'équation suivante :"),
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helpText("$$\\bar{x}=\\sum_{i=1}^n{\\frac{x_i}{n}}$$"),
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p("L'écart-type d'un échantillon suit l'équation suivante : "),
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helpText("$$s_x = \\sqrt{\\sum_{i=1}^n{\\frac{(x_i - \\bar{x})^2}{n-1}}}$$"),
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hr(),
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helpText("La moyenne de la distribution d’échantillonnage"),
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verbatimTextOutput("mean_value"),
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helpText("L'écart-type de la la distribution d’échantillonnage"),
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verbatimTextOutput("sd_value"),
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hr()
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),
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# Show a plot of the generated distribution
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mainPanel(
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withMathJax(),
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helpText("Partons d'une distribution théorique de la population
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qui soit normale, de moyenne \\(\\mu = 8\\) et d'écart
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type \\(\\sigma = 2\\)."),
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strong("Comment varie la moyenne et l'écart-type de la distribution
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d'échantillonnage ?"),
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plotOutput("mean_plot"),
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hr()
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)
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)
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))

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