title: shiny: How to integrate values from sliderInput within renderUI() into an eventReactive() used in renderPlot()
body: <p>My <code>app</code> prints a <code>renderUI()</code> comprising a <code>tabsetPanel()</code> containg a plot based on the <code>sliderInput()</code> <code>input$n_fjernet</code> from <code>ui</code></p>
  
  <p><strong>Problem</strong></p>
  
  <p>The idea is, that <code>renderUI()</code> returns <strong>(1)</strong> a new <code>sliderInput()</code> called <code>input$time_cali</code> which reactively inserts the chosen slider-value into the <code>( ...) times=reactive({input$time_cali}),</code> argument in <code>plotCalibration</code>, which currently has <code>60</code> written; and <strong>(2)</strong>, the <code>output$cali_plot</code> should print/update reactively based on the <code>input$time_cali</code>-value from <code>renderUI()</code> stored in <code>output$test</code></p>
  
  <p>How can this be done? </p>
  
  <p>Expected output </p>
  
  <p><a href="https://i.stack.imgur.com/NgEMN.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/NgEMN.png" alt="enter image description here"></a></p>
  
  <p>I tried <code>times=reactive({input$time_cali})</code> and various variations of <code>eventReactive()</code></p>
  
  <p>Written with</p>
  
  <pre><code>library(shiny)
  library(shinyjs)
  library(survival)
  library(tidyverse)
  library(riskRegression)
  library(rms)
  
  ui &lt;- fluidPage(
  
  
    useShinyjs(),
  
    fluidRow(
  
      column(
        12,
        wellPanel(
          style = "height:150px", 
          sliderInput("n_fjernet", "Lymph Nodal Yield", 
                      min = 4, max = 120, value = 40)
        )
      ),
  
      fluidRow(align="center", br(), actionButton("do", "Submit"),
  
               fluidRow(br(),
  
                        column(12, 
  
                               uiOutput("test")
                        )
               )
      )
    )
  )
  
  server &lt;- function(input, output, session) {
  
    fit_data &lt;- eventReactive(input$do, {
      p %&gt;% filter(n_fjernet == as.numeric(input$n_fjernet))
    })
  
  
    reactive_cali_plot &lt;- eventReactive(input$do, {
  
      plotCalibration(Score(list(Nomogram=cph(Surv(os.neck,mors)~alder,
                                              data=fit_data(), y=TRUE, x=TRUE)), 
                            Hist(os.neck,mors)~1,
                            data=fit_data(), 
                            plots=c("cal"),
                            times=60, ## This part should be reactively based on input$time_cali from renderUI() in output$test
                            metrics=c("auc","brier")), 
                      cens.method = "local", 
                      legend.x=.6,
                      legend.y=.35,
                      cex=1,
                      brier.in.legend = TRUE,
                      auc.in.legend = TRUE)
    })
  
    output$cali_plot &lt;- renderPlot({
  
  
      reactive_cali_plot()
  
    })
  
  
    observeEvent(input$do, {
  
      output$test &lt;- renderUI({
  
  
        tabsetPanel(id = "something", 
                    tabPanel(title = "Cali plot",
                             sliderInput("time_cali", "Months to predict", 
                                         min = 12, max = 120, value = 60),
                             plotOutput("cali_plot",width = "90%", height="650px"))
  
        )
      })
    })
  
  
  }
  
  shinyApp(ui, server)
  </code></pre>
  
  <p>My data <code>p</code></p>
  
  <pre><code>p &lt;- structure(list(os.neck = c(9.63, 7.03, 9.17, 10.48, 7.69, 15.18, 
  13.5, 16.33, 15.31, 12.09, 12.35, 22.28, 15.77, 14.39, 10.02, 
  14.52, 8.44, 23.82, 5.95, 3.78, 19.32, 20.14, 15.51, 19.78, 12.98, 
  32.92, 9.76, 5.65, 30.75, 2.79, 33.58, 27.53, 27.63, 14.62, 29.17, 
  25.4, 18.43, 5.29, 30.75, 28.48, 14.69, 13.14, 6.6, 26.81, 40.74, 
  11.63, 13.31, 10.41, 9.56, 17.51, 35.78, 35.75, 37.62, 33.25, 
  36.96, 34.56, 40.05, 41.26, 24.34, 37.49, 40.94, 24.11, 39.33, 
  11.24, 39.1, 19.75, 38.93, 39.36, 36.34, 48, 29.17, 47.93, 3.68, 
  24.21, 46.36, 49.12, 50.96, 14.16, 54.01, 19.88, 50.86, 1.87, 
  54.24, 13.93, 11.6, 10.05, 23.1, 62.78, 12.58, 39, 59.83, 6.77, 
  60.39, 18.46, 61.77, 58.41, 49.45, 64.26, 2.4, 26.51, 58.94, 
  69.91, 64.66, 55.56, 46.55, 29.63, 55.66, 19.68, 7.62, 2.73, 
  17.77, 10.12, 9.95, 74.22, 57.3, 58.94, 27.01, 34.23, 78.82, 
  27.2, 83.02, 76.68, 58.15, 22.18, 14.49, 3.91, 25.92, 74.64, 
  66.83, 70.74, 38.08, 7.69, 74.55, 49.94, 11.1, 88.54, 6.44, 79.54, 
  80.82, 70.83, 12.91, 81.25, 17.38, 29.96, 94.72, 73.53, 72.54, 
  1.35, 89.69, 62.85, 7.62, 93.27, 5.09, 51.25, 62, 55.33, 44.62, 
  56.94, 94.55, 88.61, 32.46, 11.04, 16.53, 100.04, 24.74, 24.54, 
  5.75, 59.83, 59.83, 77.77, 92.78, 49.58, 91.2, 1.18, 18.92, 6.34, 
  32.46, 72.41, 105.82, 1.84, 12.78, 57.56, 59.14, 104.08, 15.54, 
  117.75, 4.27, 67.61, 19.78, 112.49, 53.59, 107.01, 47.57, 9.46, 
  53.59, 46.46, 57.33, 18.76, 82.04, 13.67, 67.45, 28.98, 21.19, 
  121.4, 91.07, 50.83, 121.72, 123.04, 6.31, 123.5, 58.68, 9.56, 
  34.1, 90.48, 71.1, 11.33, 65.35, 54.21, 34.99, 62.06, 199.1, 
  65.74, 61.64, 15.44, 52.21, 19.88, 7.82, 5.39, 39.98, 5.49, 35.98, 
  22.67, 26.55, 23.89, 22.44, 1.77, 14.92, 17.64, 11.53, 58.74, 
  0.82, 0.26, 25.17, 18.27, 18.46, 9.17, 18.27, 129.71, 153.7, 
  123.86), mors = c(0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
  0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 
  0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 
  1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 
  1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 
  0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 
  1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
  1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 
  0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 
  1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L), 
      n_fjernet = c(22L, 61L, 50L, 47L, 30L, 60L, 82L, 60L, 33L, 
      67L, 35L, 56L, 15L, 37L, 44L, 124L, 41L, 30L, 31L, 35L, 36L, 
      28L, 39L, 54L, 25L, 27L, 69L, 53L, 24L, 33L, 52L, 77L, 51L, 
      7L, 22L, 53L, 26L, 58L, 28L, 83L, 39L, 15L, 37L, 27L, 9L, 
      17L, 32L, 26L, 44L, 52L, 22L, 62L, 53L, 68L, 52L, 38L, 50L, 
      21L, 41L, 74L, 15L, 26L, 36L, 37L, 34L, 22L, 31L, 53L, 13L, 
      44L, 43L, 51L, 20L, 21L, 63L, 40L, 25L, 17L, 43L, 47L, 35L, 
      21L, 4L, 23L, 35L, 50L, 69L, 24L, 38L, 45L, 37L, 35L, 25L, 
      19L, 43L, 19L, 33L, 38L, 50L, 21L, 40L, 100L, 45L, 53L, 41L, 
      7L, 75L, 48L, 20L, 11L, 72L, 37L, 34L, 70L, 20L, 47L, 44L, 
      45L, 48L, 23L, 27L, 24L, 39L, 9L, 34L, 22L, 89L, 40L, 35L, 
      34L, 61L, 28L, 27L, 62L, 47L, 13L, 20L, 9L, 27L, 38L, 44L, 
      15L, 33L, 65L, 31L, 49L, 53L, 15L, 26L, 17L, 24L, 20L, 25L, 
      12L, 34L, 22L, 27L, 14L, 27L, 31L, 26L, 15L, 16L, 30L, 19L, 
      51L, 12L, 33L, 68L, 26L, 20L, 34L, 31L, 7L, 76L, 7L, 24L, 
      36L, 22L, 27L, 35L, 64L, 18L, 38L, 10L, 27L, 26L, 47L, 15L, 
      30L, 30L, 21L, 31L, 14L, 14L, 22L, 28L, 13L, 17L, 16L, 7L, 
      11L, 37L, 55L, 13L, 26L, 17L, 12L, 44L, 58L, 20L, 28L, 7L, 
      24L, 10L, 42L, 39L, 14L, 31L, 49L, 87L, 18L, 26L, 24L, 20L, 
      41L, 31L, 13L, 41L, 25L, 16L, 18L, 26L, 35L, 36L, 22L, 20L, 
      16L, 10L, 19L, 46L, 6L, 49L, 70L, 46L, 55L, 25L, 22L, 37L, 
      28L), alder = c(47, 50, 61, 83, 38, 44, 45, 47, 52, 54, 56, 
      58, 58, 59, 63, 65, 65, 67, 71, 71, 73, 73, 77, 88, 89, 35, 
      35, 41, 47, 48, 49, 51, 51, 53, 55, 59, 60, 65, 67, 68, 68, 
      70, 74, 84, 27, 48, 49, 50, 55, 56, 57, 58, 58, 60, 62, 62, 
      63, 63, 64, 64, 66, 70, 71, 72, 75, 76, 80, 83, 84, 44, 46, 
      51, 58, 59, 60, 61, 63, 63, 66, 66, 67, 69, 70, 79, 80, 82, 
      84, 51, 53, 53, 54, 54, 54, 57, 58, 59, 60, 61, 61, 62, 62, 
      67, 71, 72, 72, 73, 75, 77, 80, 85, 38, 46, 49, 49, 51, 52, 
      54, 54, 55, 55, 58, 59, 61, 62, 63, 64, 66, 66, 66, 67, 70, 
      71, 73, 73, 81, 34, 41, 46, 47, 51, 54, 54, 55, 57, 57, 58, 
      58, 60, 61, 64, 75, 77, 78, 79, 80, 83, 86, 36, 38, 42, 47, 
      49, 49, 49, 52, 53, 55, 55, 55, 60, 62, 63, 63, 64, 64, 65, 
      67, 67, 68, 69, 71, 71, 80, 80, 38, 39, 47, 51, 53, 53, 58, 
      58, 66, 67, 69, 70, 73, 74, 76, 87, 43, 46, 53, 55, 56, 56, 
      59, 60, 61, 68, 70, 74, 77, 80, 51, 63, 70, 52, 56, 75, 65, 
      65, 41, 82, 47, 56, 66, 65, 49, 75, 48, 72, 43, 52, 77, 51, 
      57, 53, 64, 50, 86, 69, 72, 39, 65, 63, 27, 75, 49, 61)), row.names = c(NA, 
  250L), class = "data.frame")
  </code></pre>