Présentation Shiny

library(shiny)

ui <- fluidPage(
  textInput("name", "What is your name?"),
  textOutput("greeting")
)

server <- function(input, output, session) {
  output$greeting <- renderText({paste0("Hello ", input$name)})
 } 

shinyApp(ui, server)

if (!require("pacman")) install.packages("pacman")
pacman::p_load(bslib, shiny, shinylive, shinythemes, shinydashboard)

ui <- page_sidebar(

  title = "Importance de la taille d'échantillon",

  sidebar = sidebar(

    radioButtons(
      "dist",
      "Loi de distribution:",
      c(
        "Normale" = "norm",
        "Uniforme" = "unif",
        "Log-normale" = "lnorm",
        "Exponentielle" = "exp"
      )
    ),
    
    br(),

    numericInput(
      "n",
      "Nombre d'observations:",
      value = 300,
      min = 1,
      max = 10000
    )
  ),

  navset_card_underline(

    nav_panel("Plot", plotOutput("plot")),

    nav_panel("Summary", verbatimTextOutput("summary")),

    nav_panel("Table", tableOutput("table")), 
    
    nav_panel("Télécharger les données", downloadButton("Data","Télécharger les données"))
  )
)

server <- function(input, output) {

  d <- reactive({
    dist <- switch(
      input$dist,
      norm = rnorm,
      unif = runif,
      lnorm = rlnorm,
      exp = rexp,
      rnorm
    )

    dist(input$n)
  })

  output$plot <- renderPlot({
    dist <- input$dist
    n <- input$n

    hist(
      d(),
      main = paste("r", dist, "(", n, ")", sep = ""),
      col = "aquamarine",
      border = "white"
    )
  })

  output$summary <- renderPrint({
    summary(d())
  })

  output$table <- renderTable({
    d()
  })

 output$Data <- downloadHandler(
  filename = function() {
    "Data.csv"
  },
  content = function(file) {
    write.csv2(d(), file, row.names = FALSE)
  }
)

}

shinyApp(ui, server) 

if (!require("pacman")) install.packages("pacman")
pacman::p_load(bslib, dplyr, ggplot2, here, readxl, shiny, shinylive, shinythemes, shinydashboard)

DF_path <- here("data", "DF_ERFI_anonym.xlsx")
DF <- read_excel(DF_path)

DF$OA_SATREP <- as.numeric(DF$OA_SATREP)
DF$MA_SEXE <- as.factor(DF$MA_SEXE)
DF2 <- subset(DF, select = c(OA_SATREP, MA_SEXE))
DF2 <- na.omit(DF2)

levels(DF2$MA_SEXE) <- c("Hommes", "Femmes")

# UI
ui <- fluidPage(
  theme = shinytheme("flatly"),
  titlePanel("Histogramme de la satisfaction de la répartition des tâches ménagères selon le genre"),
  sidebarLayout(
    sidebarPanel(
      selectInput(
        inputId = "gender",
        label = "Sélectionner le genre :",
        choices = c("Tous" = "Tous", "Hommes" = "Hommes", "Femmes" = "Femmes"),
        selected = "Tous"
      )
    ),
    mainPanel(
      plotOutput("histPlot", height = "500px")
    )
  )
)

# Server
server <- function(input, output) {
  output$histPlot <- renderPlot({
    filtered_data <- DF2
    if (input$gender != "Tous") {
      filtered_data <- DF2 %>% filter(MA_SEXE == input$gender)
    }
    
    if (input$gender == "Tous") {
      p <- ggplot(filtered_data, aes(x = OA_SATREP, fill = MA_SEXE)) +
        geom_histogram(bins = 30, position = "dodge") +
        facet_wrap(~ MA_SEXE, ncol = 2) +
        labs(
          title = "Histogramme de la satisfaction (Tous)",
          x = "Satisfaction de la répartition des tâches ménagères",
          y = "Fréquence"
        ) +
        theme_minimal() +
        scale_fill_manual(values = c("Hommes" = "aquamarine", "Femmes" = "steelblue"))
    } else {
      gender_color <- if (input$gender == "Hommes") "aquamarine" else "steelblue"
      p <- ggplot(filtered_data, aes(x = OA_SATREP)) +
        geom_histogram(bins = 30, fill = gender_color, color = "black") +
        labs(
          title = paste("Histogramme de la satisfaction (", input$gender, ")", sep = ""),
          x = "Satisfaction de la répartition des tâches ménagères",
          y = "Fréquence"
        ) +
        theme_minimal() +
        theme(legend.position = "none")
    }
    
    print(p)
  })
}

# Run the app
shinyApp(ui = ui, server = server)