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GuyliannEngelsGuyliannEngels
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rename shinyapps
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‎inst/shiny/B01Sa_reglin/app.R‎

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learndown::learndownShinyVersion("1.0.0")
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conf <- BioDataScience::config()
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library(shiny)
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library(learndown)
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library(BioDataScience2)
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a_init <- -1.5
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b_init <- 3.5
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error_sd <- 0.25
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set.seed(42)
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reglin <- function(x, a, b)
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(a * x) + b
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model_data <- tibble::tibble(
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x = seq(0, 10, by = 0.25),
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y = reglin(x, a = a_init, b = b_init) +
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rnorm(n = length(x), sd = error_sd))
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ui <- fluidPage(
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learndownShiny("Ajustement manuel d'un modèle : régression linéaire"),
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sidebarLayout(
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sidebarPanel(
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withMathJax(),
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p("$$y(x) = a \\ x + \\ b $$"),
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sliderInput("a", label = "a",
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value = 0, min = -5, max = 5, step = 0.5),
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sliderInput("b", label = "b",
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value = 0, min = -5, max = 5, step = 0.5),
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hr(),
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submitQuitButtons()
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),
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mainPanel(
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plotOutput("model_plot"),
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hr(),
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withMathJax(),
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fluidRow(
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column(width = 6,
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p("Modèle paramétré :"),
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uiOutput("model_equation")),
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column(width = 6,
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p("Somme des carrés des résidus (valeur à minimiser) :"),
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uiOutput("model_resid"))
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)
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)
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)
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)
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server <- function(input, output, session) {
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model_predict <- reactive({
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dplyr::mutate(model_data,
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y_predit = reglin(x, a = input$a, b = input$b),
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distance2 = (y_predit - y)^2
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)
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})
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output$model_equation <- renderUI({
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withMathJax(
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sprintf("$$y(x) \\ = %.02f \\ x + \\ %.02f$$",
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input$a, input$b))
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})
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output$model_resid <- renderUI({
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data <- model_predict()
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withMathJax(sprintf("$$ \\ %.02f \\ $$", sum(data$distance2)))
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})
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output$model_plot <- renderPlot({
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data <- model_predict()
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chart::chart(data, y ~ x) +
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ggplot2::geom_point() +
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ggplot2::geom_line(chart::f_aes(y_predit ~ x), color = "red") +
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ggplot2::xlab("x") +
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ggplot2::ylab("y")
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})
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trackEvents(session, input, output,
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sign_in.fun = BioDataScience::sign_in, conf = conf)
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trackSubmit(session, input, output, max_score = 2,
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solution = list(a = a_init, b = b_init),
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comment = "y = a.x + b",
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message.success = "Correct, c'est le meilleur modèle. a est la pente et b est l'ordonnée à l'origine de la droite.",
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message.error = "Incorrect, un modèle mieux ajusté existe.")
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trackQuit(session, input, output, delay = 20)
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}
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shinyApp(ui, server)

‎inst/shiny/B04Sa_micmen/app.R‎

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learndown::learndownShinyVersion("0.0.9000")
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conf <- BioDataScience::config()
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library(shiny)
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library(learndown)
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vm_init <- 6
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k_init <- 2
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error_sd <- 0.05
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set.seed(42)
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model_data <- tibble::tibble(
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x = seq(0, 25, by = 0.1),
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y = SSmicmen(x, Vm = vm_init, K = k_init) +
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rnorm(n = length(x), sd = error_sd))
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ui <- fluidPage(
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learndownShiny("Ajustement manuel d'un modèle : Michaelis-Menten"),
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sidebarLayout(
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sidebarPanel(
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withMathJax(),
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p("$$y(x) = \\frac{V_{m} * x}{K + x}$$"),
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sliderInput("vm", label = "Vm",
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value = 1, min = 0, max = 10, step = 0.5),
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sliderInput("k", label = "K",
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value = 1, min = 0, max = 10, step = 0.5),
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hr(),
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submitQuitButtons()
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),
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mainPanel(
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plotOutput("model_plot"),
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hr(),
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withMathJax(),
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fluidRow(
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column(width = 6,
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p("Modèle paramétré :"),
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uiOutput("model_equation")),
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column(width = 6,
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p("Somme des carrés des résidus (valeur à minimiser) :"),
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uiOutput("model_resid"))
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)
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)
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)
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)
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server <- function(input, output, session) {
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model_predict <- reactive({
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dplyr::mutate(model_data,
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y_predit = SSmicmen(x, Vm = input$vm, K = input$k),
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distance2 = (y_predit - y)^2
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)
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})
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output$model_equation <- renderUI({
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withMathJax(
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sprintf("$$y(x) = \\frac{%.02f * x}{%.02f + x}$$",
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input$vm, input$k))
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})
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output$model_resid <- renderUI({
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data <- model_predict()
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withMathJax(sprintf("$$ \\ %.02f \\ $$", sum(data$distance2)))
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})
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output$model_plot <- renderPlot({
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data <- model_predict()
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chart::chart(data, y ~ x) +
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ggplot2::geom_point() +
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ggplot2::geom_line(chart::f_aes(y_predit ~ x), color = "red") +
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ggplot2::xlab("x") +
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ggplot2::ylab("y")
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})
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trackEvents(session, input, output,
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sign_in.fun = BioDataScience::sign_in, config = conf)
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trackSubmit(session, input, output, max_score = 2,
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solution = list(vm = vm_init, k = k_init),
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comment = "y = Vm*x/K+x",
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message.success = "Correct, c'est le meilleur modèle.",
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message.error = "Incorrect, un modèle mieux ajusté existe.")
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trackQuit(session, input, output, delay = 20)
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}
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shinyApp(ui, server)

‎inst/shiny/B04Sb_exponent/app.R‎

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learndown::learndownShinyVersion("0.0.9000")
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conf <- BioDataScience::config()
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library(shiny)
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library(learndown)
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y0_init <- 3.5
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k_init <- 0.10
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error_sd <- 0.5
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set.seed(42)
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exponent <- function(x, y0, k)
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y0 * exp(k * x)
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ui <- fluidPage(
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learndownShiny("Ajustement manuel d'un modèle : courbe exponentielle"),
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sidebarLayout(
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sidebarPanel(
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withMathJax(),
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p("$$y(x) = y_0 \\ e^{k \\ x}$$"),
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sliderInput("y0", label = "y0",
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value = 1, min = -5, max = 5, step = 0.5),
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sliderInput("k", label = "k",
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value = 0.025, min = -0.20, max = 0.20, step = 0.025),
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hr(),
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submitQuitButtons()
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),
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mainPanel(
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plotOutput("model_plot"),
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hr(),
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withMathJax(),
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fluidRow(
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column(width = 6,
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p("Modèle paramétré :"),
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uiOutput("model_equation")),
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column(width = 6,
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p("Somme des carrés des résidus (valeur à minimiser) :"),
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uiOutput("model_resid"))
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)
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)
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)
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)
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server <- function(input, output, session) {
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model_data <- tibble::tibble(
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x = seq(0, 20, by = 0.5),
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y = exponent(x, y0 = y0_init, k = k_init) +
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rnorm(n = length(x), sd = error_sd))
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model_predict <- reactive({
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dplyr::mutate(model_data,
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y_predit = exponent(x, y0 = input$y0, k = input$k),
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distance2 = (y_predit - y)^2
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)
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})
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output$model_equation <- renderUI({
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withMathJax(
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sprintf("$$y(x) \\ = %.02f \\ e^{%.02f \\ x}$$", input$vm, input$k))
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})
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output$model_resid <- renderUI({
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data <- model_predict()
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withMathJax(sprintf("$$ \\ %.02f \\ $$", sum(data$distance2)))
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})
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output$model_plot <- renderPlot({
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data <- model_predict()
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chart::chart(data, y ~ x) +
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ggplot2::geom_point() +
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ggplot2::geom_line(chart::f_aes(y_predit ~ x), color = "red") +
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ggplot2::xlab("x") +
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ggplot2::ylab("y")
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})
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trackEvents(session, input, output,
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sign_in.fun = BioDataScience::sign_in, config = conf)
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trackSubmit(session, input, output, max_score = 2,
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solution = list(y0 = y0_init, k = k_init),
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comment = "y = y0.e^(k.x)",
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message.success = "Correct, c'est le meilleur modèle. y0 et la valeur de y pour x = 0 et k est la vitesse de croissance.",
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message.error = "Incorrect, un modèle mieux ajusté existe.")
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trackQuit(session, input, output, delay = 20)
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}
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shinyApp(ui, server)

‎inst/shiny/B04Sc_logis/app.R‎

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learndown::learndownShinyVersion("0.0.9000")
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conf <- BioDataScience::config()
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library(shiny)
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library(learndown)
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asym_init <- 5
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xmid_init <- 4
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scal_init <- 0.5
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error_sd <- 0.1
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set.seed(42)
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model_data <- tibble::tibble(
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x = seq(0, 8, by = 0.1),
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y = SSlogis(x, Asym = asym_init, xmid = xmid_init, scal = scal_init) +
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rnorm(n = length(x), sd = error_sd))
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ui <- fluidPage(
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learndownShiny("Ajustement manuel d'un modèle : courbe logistique"),
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sidebarLayout(
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sidebarPanel(
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withMathJax(),
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p("$$y(x) = \\frac{Asym}{1 + e^{\\frac{xmid - x}{scal}}}$$"),
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sliderInput("asym", label = "Asym",
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value = 1.00, min = 0.50, max = 10.00, step = 0.5),
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sliderInput("xmid", label = "Xmid",
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value = 1.00, min = 0.25, max = 10.00, step = 0.25),
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sliderInput("scal", label = "Scal",
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value = 1.00, min = 0.25, max = 10.00, step = 0.25),
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hr(),
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submitQuitButtons()
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),
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mainPanel(
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plotOutput("model_plot"),
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hr(),
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withMathJax(),
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fluidRow(
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column(width = 6,
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p("Modèle paramétré :"),
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uiOutput("model_equation")),
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column(width = 6,
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p("Somme des carrés des résidus (valeur à minimiser) :"),
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uiOutput("model_resid"))
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)
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)
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)
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)
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server <- function(input, output, session) {
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model_predict <- reactive({
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dplyr::mutate(model_data,
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y_predit = SSlogis(x, Asym = input$asym, xmid = input$xmid,
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scal = input$scal),
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distance2 = (y_predit - y)^2
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)
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})
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output$model_equation <- renderUI({
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withMathJax(
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sprintf("$$y(x) = \\frac{%.02f}{1 + e^{\\frac{%.02f - x}{%.02f}}}$$",
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input$asym, input$xmid, input$scal))
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})
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output$model_resid <- renderUI({
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data <- model_predict()
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withMathJax(sprintf("$$ \\ %.02f \\ $$", sum(data$distance2)))
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})
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output$model_plot <- renderPlot({
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data <- model_predict()
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chart::chart(data, y ~ x) +
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ggplot2::geom_point() +
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ggplot2::geom_line(chart::f_aes(y_predit ~ x), color = "red") +
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ggplot2::xlab("x") +
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ggplot2::ylab("y")
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})
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trackEvents(session, input, output,
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sign_in.fun = BioDataScience::sign_in, config = conf)
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trackSubmit(session, input, output, max_score = 2, solution =
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list(asym = asym_init, xmid = xmid_init, scal = scal_init),
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comment = "y = asym/1+e(xmid-x/scal)",
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message.success = "Correct, c'est le meilleur modèle.",
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message.error = "Incorrect, un modèle mieux ajusté existe.")
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trackQuit(session, input, output, delay = 20)
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}
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shinyApp(ui, server)

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