Why does conditionalPanel() not reset if condition is changed?

Question

I am trying to build a simple shinyapp as part of learning shiny.

Problem: I have a conditionalPanel located in sidebarPanel, which I would like to reset in case the condition changes back.

I have found similar questions on StackOverflow here and here. Both threads indicated that the solution was to add id; however, it did not solve my problem.

The shinyapp works by calculating a nomogram-score, which can be used to predict overall survival. It contains four components:

(1) n.fjernet is the number of lymph nodes removed on the cervical neck

(2) n.sygdom is the number of lymph nodes with evidence of cancer metastises.

(3) ecs is extracapsular spread (of metastises, i.e. the metastises penetrating one or more of the lymph nodes) in n.sygdom. Therefore, this radioButtion() can only be available if n.sygdom ≥ 1

(4) contra.pos is whether the spread (of metastises) includes the uni- or contralateral side of the neck. Therefore, this radioButtion() can only be available if n.sygdom ≥ 1.

So, currently, the radioButtons("ecs") and radioButtons("contra.pos") activates if n.sygdom ≥ 1. If the user changes back to n.sygdom==0, the radioButtions() disappear. But, if n.sygdom ≥ 1 is chosen again, the radioButtons() activates at the same input-value as when they were deactivated by n.sygdom==0.

Please, how can I change this script so that the input from the radioButtons() reset if switched off?

I tried (and numerous different id-names)

sidebarPanel(id="n_sygdom", ...),
      sliderInput("n_fjernet", ... )

Script:

library(shiny)
library(survminer)


ui <- fluidPage(
  titlePanel("Survival Curve of individualized pN-staging\n"),
  sidebarLayout(
    sidebarPanel(id="",
      sliderInput("n_fjernet", "Lymph Nodal Yield", min = 2, max = 120, value = 40),
      sliderInput("n_sygdom", "Number of positive lymph nodes", min = 0, max = 40, value = 0),
      conditionalPanel(
        condition = "input.n_sygdom >= 1",
        radioButtons("ecs", "Extracapsular extension", c("No","Yes")),
        radioButtons("contra.pos", "Neck involvement", c("Contra.","Ipsi.")
        )
      ),
      verbatimTextOutput("out.score"),
      verbatimTextOutput("out.score.group")
    ),
    mainPanel(
      plotOutput("surv_plot")
    )
  )
)



server <- function(input, output, session) {

  calc_score <- reactive({
    round(nom$ecs$points[nom$ecs$ecs==input$ecs] +
          nom$contra.pos$points[nom$contra.pos$contra.pos==input$contra.pos] +
          nom$n_fjernet$points[nom$n_fjernet$n_fjernet==input$n_fjernet] +
          nom$n_sygdom$points[nom$n_sygdom$n_sygdom==input$n_sygdom], digits=1)
  })

  observe(
    updateSliderInput(
      session = session,
      inputId = "n_sygdom",
      max = min(40, input$n_fjernet),
      value = min(input$n_fjernet, input$n_sygdom)
    )
  )

  calc_score_group <- function(score) {
    cut(score, c(0,35.9,55.2,70.0,83.3,Inf), include.lowest = TRUE, labels = c("1","2","3","4","5"))
  }

  fit_data <- reactive({
    p %>% filter(score.group == as.numeric(calc_score_group(calc_score())))
  })

  fit_model <- reactive({
    survfit(Surv(os.neck, mors) ~ 1, data = fit_data())
  })



  output$out.score <- renderText(calc_score())
  output$out.score.group <- renderText(calc_score_group(calc_score()))

  output$surv_plot <- renderPlot({
    ggsurvplot(
      fit_model(),                     
      data = fit_data(), 
      risk.table = TRUE, 
      pval = F,      
      pval.coord = c(0, 0.25),
      conf.int = T,         
      size=1,                    
      xlim = c(0,60),
      conf.int.alpha=c(0.2),
      break.x.by = 6,    
      xlab="Time in months",
      ylab="Probability of overall survival",
      ggtheme = theme,             
      surv.median.line = "v",
      ylim=c(0,1),
      palette="#2C77BF",
      tables.theme=theme,
      legend.title=paste("Score group", calc_score_group(calc_score())),
      surv.scale="percent",
      tables.col="strata",
      risk.table.col = "strata",
      risk.table.y.text = FALSE,
      tables.y.text = FALSE)
  })


}

shinyApp(ui, server)

My data

p <- structure(list(contra.pos = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Ipsi.", "Contra."), class = "factor"), 
    ecs = structure(c(1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 
    2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
    2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
    2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
    2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
    1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 
    1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L
    ), .Label = c("No", "Yes"), class = "factor"), 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), n.sygdom = c(1L, 2L, 1L, 3L, 1L, 0L, 3L, 
    0L, 2L, 1L, 4L, 4L, 1L, 0L, 2L, 2L, 1L, 0L, 0L, 4L, 0L, 0L, 
    1L, 1L, 0L, 1L, 4L, 3L, 1L, 0L, 8L, 1L, 1L, 1L, 1L, 1L, 0L, 
    1L, 2L, 1L, 0L, 2L, 1L, 0L, 2L, 0L, 3L, 0L, 1L, 1L, 1L, 2L, 
    0L, 3L, 2L, 1L, 0L, 0L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 1L, 1L, 
    0L, 0L, 1L, 4L, 0L, 0L, 2L, 2L, 1L, 1L, 0L, 0L, 3L, 1L, 6L, 
    0L, 0L, 0L, 3L, 2L, 2L, 4L, 0L, 3L, 27L, 0L, 2L, 1L, 0L, 
    0L, 1L, 1L, 2L, 2L, 5L, 1L, 0L, 0L, 1L, 0L, 5L, 0L, 0L, 2L, 
    10L, 0L, 6L, 2L, 1L, 2L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 1L, 
    5L, 2L, 2L, 1L, 2L, 1L, 0L, 0L, 1L, 13L, 0L, 1L, 0L, 1L, 
    0L, 1L, 1L, 0L, 23L, 0L, 2L, 2L, 0L, 2L, 0L, 0L, 1L, 1L, 
    0L, 0L, 0L, 2L, 3L, 1L, 4L, 0L, 1L, 0L, 5L, 5L, 4L, 0L, 0L, 
    4L, 0L, 1L, 1L, 0L, 2L, 5L, 1L, 3L, 6L, 1L, 1L, 1L, 0L, 0L, 
    1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 1L, 
    0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 40L, 2L, 0L, 1L, 
    0L, 2L, 0L, 3L, 1L, 1L, 4L, 1L), 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), 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), score = c(47.16, 47.55, 39.27, 72.23, 
    44.91, 25.74, 62.36, 25.74, 55.44, 34.48, 82.32, 76.4, 60.64, 
    32.22, 52.34, 36.13, 41.81, 34.2, 33.91, 65.66, 32.51, 34.76, 
    42.37, 38.14, 35.61, 45.75, 61.23, 59.04, 46.6, 33.35, 84.05, 
    26.5, 38.99, 62.89, 47.16, 38.42, 35.32, 37.01, 56.85, 29.96, 
    31.66, 60.52, 42.93, 35.04, 62.21, 37.86, 64.96, 35.32, 40.96, 
    38.71, 47.16, 58.77, 27.71, 54.81, 50.09, 42.65, 28.56, 36.73, 
    31.1, 43.88, 38.43, 78.15, 32.51, 32.22, 33.07, 47.16, 44.63, 
    27.71, 38.99, 40.96, 68.56, 28.28, 37.02, 58.83, 58.48, 42.09, 
    46.32, 37.86, 30.53, 60.73, 55, 77.47, 41.53, 36.17, 32.79, 
    71.38, 56.79, 57.98, 76.32, 29.97, 63.55, 123.86, 35.61, 
    59.39, 41.24, 37.3, 33.35, 37.5, 39.27, 58.83, 53.47, 52.02, 
    35.52, 27.71, 31.1, 51.39, 21.51, 83.34, 37.02, 39.55, 50.79, 
    91.42, 33.07, 63.65, 70.61, 51.62, 52.34, 29.97, 29.12, 36.17, 
    35.04, 85.42, 31.66, 40.12, 55.28, 90.67, 51.15, 53.47, 43.5, 
    55.16, 47.67, 34.76, 35.04, 35.89, 98.47, 38.99, 59.23, 40.12, 
    57.25, 31.94, 52.46, 49.14, 33.35, 92.46, 33.91, 50.93, 49.8, 
    38.43, 57.42, 37.86, 35.89, 47.73, 57.82, 39.27, 33.07, 36.45, 
    57.13, 70.03, 57.25, 71.95, 35.32, 60.64, 38.14, 71.76, 91.52, 
    61.15, 39.27, 33.35, 56.36, 35.32, 47.73, 43.78, 33.91, 62.77, 
    75.44, 51.39, 78.71, 78.39, 47.16, 45.75, 55, 24.61, 37.58, 
    42.65, 62.05, 35.04, 35.32, 51.62, 38.43, 34.2, 34.2, 42.29, 
    33.91, 49.42, 60.8, 36.45, 56.97, 61.2, 37.86, 38.14, 40.68, 
    61.77, 54.43, 27.15, 49.7, 46.04, 60.07, 39.27, 130.25, 59.89, 
    37.02, 40.32, 40.68, 69.48, 39.84, 73.64, 53.87, 60.92, 71.95, 
    51.05), score.group = structure(c(2L, 2L, 2L, 4L, 2L, 1L, 
    3L, 1L, 3L, 1L, 4L, 4L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 
    1L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 5L, 1L, 2L, 3L, 2L, 2L, 
    1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 
    3L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 
    2L, 1L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 3L, 2L, 
    4L, 2L, 2L, 1L, 4L, 3L, 3L, 4L, 1L, 3L, 5L, 1L, 3L, 2L, 2L, 
    1L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 5L, 2L, 2L, 2L, 
    5L, 1L, 3L, 4L, 2L, 2L, 1L, 1L, 2L, 1L, 5L, 1L, 2L, 3L, 5L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 5L, 2L, 3L, 2L, 3L, 1L, 2L, 
    2L, 1L, 5L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 
    3L, 4L, 3L, 4L, 1L, 3L, 2L, 4L, 5L, 3L, 2L, 1L, 3L, 1L, 2L, 
    2L, 1L, 3L, 4L, 2L, 4L, 4L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 
    1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 
    3L, 2L, 1L, 2L, 2L, 3L, 2L, 5L, 3L, 2L, 2L, 2L, 3L, 2L, 4L, 
    2L, 3L, 4L, 2L), .Label = c("1", "2", "3", "4", "5"), class = "factor")), row.names = c(NA, 
220L), class = "data.frame")

And nomogram

nom <- structure(list(n.fjernet = structure(list(n.fjernet = c(2, 3, 
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 
69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 
85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 
101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 
114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 
127, 128, 129, 130, 131, 132, 133), Xbeta = c(`1` = -0.0114560716414661, 
`2` = -0.0171841074621991, `3` = -0.0229121432829322, `4` = -0.0286401791036652, 
`5` = -0.0343682149243983, `6` = -0.0400962507451313, `7` = -0.0458242865658644, 
`8` = -0.0515523223865974, `9` = -0.0572803582073305, `10` = -0.0630083940280635, 
`11` = -0.0687364298487966, `12` = -0.0744644656695296, `13` = -0.0801925014902627, 
`14` = -0.0859205373109957, `15` = -0.0916485731317288, `16` = -0.0973766089524618, 
`17` = -0.103104644773195, `18` = -0.108832680593928, `19` = -0.114560716414661, 
`20` = -0.120288752235394, `21` = -0.126016788056127, `22` = -0.13174482387686, 
`23` = -0.137472859697593, `24` = -0.143200895518326, `25` = -0.148928931339059, 
`26` = -0.154656967159792, `27` = -0.160385002980525, `28` = -0.166113038801258, 
`29` = -0.171841074621991, `30` = -0.177569110442725, `31` = -0.183297146263458, 
`32` = -0.189025182084191, `33` = -0.194753217904924, `34` = -0.200481253725657, 
`35` = -0.20620928954639, `36` = -0.211937325367123, `37` = -0.217665361187856, 
`38` = -0.223393397008589, `39` = -0.229121432829322, `40` = -0.234849468650055, 
`41` = -0.240577504470788, `42` = -0.246305540291521, `43` = -0.252033576112254, 
`44` = -0.257761611932987, `45` = -0.26348964775372, `46` = -0.269217683574453, 
`47` = -0.274945719395186, `48` = -0.280673755215919, `49` = -0.286401791036652, 
`50` = -0.292129826857386, `51` = -0.297857862678119, `52` = -0.303585898498852, 
`53` = -0.309313934319585, `54` = -0.315041970140318, `55` = -0.320770005961051, 
`56` = -0.326498041781784, `57` = -0.332226077602517, `58` = -0.33795411342325, 
`59` = -0.343682149243983, `60` = -0.349410185064716, `61` = -0.355138220885449, 
`62` = -0.360866256706182, `63` = -0.366594292526915, `64` = -0.372322328347648, 
`65` = -0.378050364168381, `66` = -0.383778399989114, `67` = -0.389506435809847, 
`68` = -0.39523447163058, `69` = -0.400962507451313, `70` = -0.406690543272047, 
`71` = -0.41241857909278, `72` = -0.418146614913513, `73` = -0.423874650734246, 
`74` = -0.429602686554979, `75` = -0.435330722375712, `76` = -0.441058758196445, 
`77` = -0.446786794017178, `78` = -0.452514829837911, `79` = -0.458242865658644, 
`80` = -0.463970901479377, `81` = -0.46969893730011, `82` = -0.475426973120843, 
`83` = -0.481155008941576, `84` = -0.486883044762309, `85` = -0.492611080583042, 
`86` = -0.498339116403775, `87` = -0.504067152224508, `88` = -0.509795188045241, 
`89` = -0.515523223865974, `90` = -0.521251259686707, `91` = -0.526979295507441, 
`92` = -0.532707331328174, `93` = -0.538435367148907, `94` = -0.54416340296964, 
`95` = -0.549891438790373, `96` = -0.555619474611106, `97` = -0.561347510431839, 
`98` = -0.567075546252572, `99` = -0.572803582073305, `100` = -0.578531617894038, 
`101` = -0.584259653714771, `102` = -0.589987689535504, `103` = -0.595715725356237, 
`104` = -0.60144376117697, `105` = -0.607171796997703, `106` = -0.612899832818436, 
`107` = -0.618627868639169, `108` = -0.624355904459902, `109` = -0.630083940280635, 
`110` = -0.635811976101369, `111` = -0.641540011922102, `112` = -0.647268047742835, 
`113` = -0.652996083563568, `114` = -0.658724119384301, `115` = -0.664452155205034, 
`116` = -0.670180191025767, `117` = -0.6759082268465, `118` = -0.681636262667233, 
`119` = -0.687364298487966, `120` = -0.693092334308699, `121` = -0.698820370129432, 
`122` = -0.704548405950165, `123` = -0.710276441770898, `124` = -0.716004477591631, 
`125` = -0.721732513412364, `126` = -0.727460549233097, `127` = -0.73318858505383, 
`128` = -0.738916620874563, `129` = -0.744644656695296, `130` = -0.750372692516029, 
`131` = -0.756100728336763, `132` = -0.761828764157496), points = c(`1` = 27.84103949255, 
`2` = 27.6285124735229, `3` = 27.4159854544958, `4` = 27.2034584354687, 
`5` = 26.9909314164416, `6` = 26.7784043974145, `7` = 26.5658773783874, 
`8` = 26.3533503593603, `9` = 26.1408233403332, `10` = 25.9282963213061, 
`11` = 25.715769302279, `12` = 25.5032422832519, `13` = 25.2907152642248, 
`14` = 25.0781882451977, `15` = 24.8656612261706, `16` = 24.6531342071435, 
`17` = 24.4406071881164, `18` = 24.2280801690893, `19` = 24.0155531500622, 
`20` = 23.8030261310351, `21` = 23.590499112008, `22` = 23.3779720929809, 
`23` = 23.1654450739538, `24` = 22.9529180549267, `25` = 22.7403910358996, 
`26` = 22.5278640168725, `27` = 22.3153369978454, `28` = 22.1028099788183, 
`29` = 21.8902829597912, `30` = 21.6777559407641, `31` = 21.465228921737, 
`32` = 21.2527019027099, `33` = 21.0401748836828, `34` = 20.8276478646557, 
`35` = 20.6151208456286, `36` = 20.4025938266015, `37` = 20.1900668075744, 
`38` = 19.9775397885473, `39` = 19.7650127695202, `40` = 19.5524857504931, 
`41` = 19.339958731466, `42` = 19.1274317124389, `43` = 18.9149046934118, 
`44` = 18.7023776743847, `45` = 18.4898506553576, `46` = 18.2773236363305, 
`47` = 18.0647966173034, `48` = 17.8522695982763, `49` = 17.6397425792492, 
`50` = 17.4272155602221, `51` = 17.214688541195, `52` = 17.0021615221679, 
`53` = 16.7896345031408, `54` = 16.5771074841137, `55` = 16.3645804650866, 
`56` = 16.1520534460595, `57` = 15.9395264270324, `58` = 15.7269994080053, 
`59` = 15.5144723889782, `60` = 15.3019453699511, `61` = 15.089418350924, 
`62` = 14.8768913318969, `63` = 14.6643643128698, `64` = 14.4518372938427, 
`65` = 14.2393102748156, `66` = 14.0267832557885, `67` = 13.8142562367614, 
`68` = 13.6017292177343, `69` = 13.3892021987072, `70` = 13.1766751796801, 
`71` = 12.964148160653, `72` = 12.7516211416259, `73` = 12.5390941225988, 
`74` = 12.3265671035717, `75` = 12.1140400845446, `76` = 11.9015130655175, 
`77` = 11.6889860464904, `78` = 11.4764590274633, `79` = 11.2639320084362, 
`80` = 11.0514049894091, `81` = 10.838877970382, `82` = 10.6263509513549, 
`83` = 10.4138239323278, `84` = 10.2012969133007, `85` = 9.98876989427365, 
`86` = 9.77624287524655, `87` = 9.56371585621945, `88` = 9.35118883719235, 
`89` = 9.13866181816525, `90` = 8.92613479913816, `91` = 8.71360778011105, 
`92` = 8.50108076108396, `93` = 8.28855374205686, `94` = 8.07602672302976, 
`95` = 7.86349970400266, `96` = 7.65097268497556, `97` = 7.43844566594846, 
`98` = 7.22591864692136, `99` = 7.01339162789426, `100` = 6.80086460886717, 
`101` = 6.58833758984007, `102` = 6.37581057081297, `103` = 6.16328355178587, 
`104` = 5.95075653275877, `105` = 5.73822951373167, `106` = 5.52570249470457, 
`107` = 5.31317547567747, `108` = 5.10064845665037, `109` = 4.88812143762328, 
`110` = 4.67559441859617, `111` = 4.46306739956908, `112` = 4.25054038054198, 
`113` = 4.03801336151488, `114` = 3.82548634248778, `115` = 3.61295932346068, 
`116` = 3.40043230443358, `117` = 3.18790528540648, `118` = 2.97537826637939, 
`119` = 2.76285124735228, `120` = 2.55032422832519, `121` = 2.33779720929809, 
`122` = 2.12527019027099, `123` = 1.91274317124389, `124` = 1.70021615221679, 
`125` = 1.48768913318969, `126` = 1.27516211416259, `127` = 1.06263509513549, 
`128` = 0.850108076108397, `129` = 0.637581057081296, `130` = 0.425054038054198, 
`131` = 0.212527019027097, `132` = 0)), info = list(nfun = 3L, 
    predictor = "n.fjernet", effect.name = "n.fjernet", type = "main")), 
    n.sygdom = structure(list(n.sygdom = c(0, 1, 2, 3, 4, 5, 
    6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 
    22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 
    37, 38, 39, 40), Xbeta = c(`133` = 0, `134` = 0.32236136668714, 
    `135` = 0.648650531337351, `136` = 0.909274081797897, `137` = 1.09719119915252, 
    `138` = 1.2269431700224, `139` = 1.31307128102872, `140` = 1.37011681879267, 
    `141` = 1.41262106993544, `142` = 1.452701773308, `143` = 1.49278247668057, 
    `144` = 1.53286318005315, `145` = 1.57294388342572, `146` = 1.61302458679829, 
    `147` = 1.65310529017085, `148` = 1.69318599354344, `149` = 1.733266696916, 
    `150` = 1.77334740028855, `151` = 1.81342810366113, `152` = 1.8535088070337, 
    `153` = 1.89358951040629, `154` = 1.93367021377884, `155` = 1.97375091715141, 
    `156` = 2.01383162052397, `157` = 2.05391232389658, `158` = 2.09399302726916, 
    `159` = 2.13407373064171, `160` = 2.17415443401433, `161` = 2.21423513738691, 
    `162` = 2.25431584075947, `163` = 2.29439654413205, `164` = 2.33447724750454, 
    `165` = 2.37455795087723, `166` = 2.41463865424957, `167` = 2.45471935762221, 
    `168` = 2.49480006099482, `169` = 2.53488076436739, `170` = 2.57496146774009, 
    `171` = 2.61504217111266, `172` = 2.65512287448523, `173` = 2.69520357785787
    ), points = c(`133` = 0, `134` = 11.9605572408505, `135` = 24.0668473679043, 
    `136` = 33.7367495824038, `137` = 40.7090287415156, `138` = 45.5232094563172, 
    `139` = 48.7188163378863, `140` = 50.8353739973004, `141` = 52.412407045637, 
    `142` = 53.8995193254604, `143` = 55.3866316052841, `144` = 56.8737438851078, 
    `145` = 58.3608561649314, `146` = 59.8479684447552, `147` = 61.3350807245785, 
    `148` = 62.8221930044026, `149` = 64.309305284226, `150` = 65.7964175640487, 
    `151` = 67.283529843873, `152` = 68.7706421236966, `153` = 70.257754403521, 
    `154` = 71.7448666833438, `155` = 73.2319789631674, `156` = 74.7190912429907, 
    `157` = 76.2062035228159, `158` = 77.6933158026398, `159` = 79.1804280824625, 
    `160` = 80.6675403622881, `161` = 82.1546526421122, `162` = 83.6417649219352, 
    `163` = 85.1288772017594, `164` = 86.6159894815798, `165` = 88.103101761408, 
    `166` = 89.5902140412232, `167` = 91.0773263210494, `168` = 92.5644386008743, 
    `169` = 94.0515508806979, `170` = 95.5386631605266, `171` = 97.0257754403502, 
    `172` = 98.5128877201739, `173` = 100)), info = list(nfun = 3L, 
        predictor = "n.sygdom", effect.name = "n.sygdom", type = "main")), 
    ecs = structure(list(ecs = c("No", "Yes"), Xbeta = c(`174` = 0, 
    `175` = 0.352802098746005), points = c(`174` = 0, `175` = 13.0899981598574
    )), info = list(nfun = 3L, predictor = "ecs", effect.name = "ecs", 
        type = "main")), contra.pos = structure(list(contra.pos = c("Ipsi.", 
    "Contra."), Xbeta = c(`176` = 0, `177` = -0.149053853083395
    ), points = c(`176` = 5.53033745977221, `177` = 0)), info = list(
        nfun = 3L, predictor = "contra.pos", effect.name = "contra.pos", 
        type = "main")), total.points = list(x = c(0, 10, 20, 
    30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140)), lp = list(
        x = c(8.59901355289166, 27.1504878870641, 45.7019622212365, 
        64.253436555409, 82.8049108895814, 101.356385223754, 
        119.907859557926, 138.459333892099), x.real = c(-1, -0.5, 
        0, 0.5, 1, 1.5, 2, 2.5)), `Probability of 1 year survival` = list(
        x = c(132.923978485611, 122.154519662532, 112.023605643525, 
        101.668496222993, 90.3441422980089, 77.0166169199793, 
        59.6149679036176, 31.771904926561), x.real = c(0.2, 0.3, 
        0.4, 0.5, 0.6, 0.7, 0.8, 0.9), fat = c("0.2", "0.3", 
        "0.4", "0.5", "0.6", "0.7", "0.8", "0.9"), which = c(FALSE, 
        TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE
        )), `Probability of 3 years survival` = list(x = c(111.455658509363, 
    98.1672795067317, 87.3978451509886, 77.2669361851509, 66.9118109221416, 
    55.5874579746061, 42.2599719337723, 24.8583283615229), x.real = c(0.1, 
    0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8), fat = c("0.1", "0.2", 
    "0.3", "0.4", "0.5", "0.6", "0.7", "0.8"), which = c(FALSE, 
    TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE)), 
    `Probability of 5 years survival` = list(x = c(96.3366217164868, 
    83.0483098712202, 72.2788509036931, 62.1479468853665, 51.7928216700398, 
    40.468478298201, 27.1409533137375, 9.73931852863156), x.real = c(0.1, 
    0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8), fat = c("0.1", "0.2", 
    "0.3", "0.4", "0.5", "0.6", "0.7", "0.8"), which = c(FALSE, 
    TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE))), info = list(
    fun = list(function (x) 
    surv(12, x), function (x) 
    surv(36, x), function (x) 
    surv(60, x)), lp = TRUE, lp.at = c(-1, -0.5, 0, 0.5, 1, 1.5, 
    2, 2.5), discrete = c(n.fjernet = FALSE, n.sygdom = FALSE, 
    ecs = TRUE, contra.pos = TRUE, studie = TRUE), funlabel = c("Probability of 1 year survival", 
    "Probability of 3 years survival", "Probability of 5 years survival"
    ), fun.at = NULL, fun.lp.at = NULL, Abbrev = list(), minlength = 4, 
    conf.int = FALSE, R = structure(c(-0.761828764157496, -0.0114560716414661, 
    0, 2.69520357785787, 0, 0.352802098746005, -0.149053853083395, 
    0), .Dim = c(2L, 4L), .Dimnames = list(NULL, c("n.fjernet", 
    "n.sygdom", "ecs", "contra.pos"))), sc = 37.1029486683449, 
    maxscale = 100, Intercept = -1.23176092093802, nint = 10, 
    space.used = c(main = 4, ia = 0)), class = "nomogram")

Answer 1

@cmirian - nice to see you are making progress.

You could follow the first link you provided to reset your radio buttons, but in your case this might be a simpler solution.

You can detect the event when your slider changes for n_sygdom, and store the most recent value as a reactive value. Then, whenever you have 1 or more lymph nodes positive, and the most recent value for this input was 0, you can use updateRadioButtons to reset the buttons to original state.

Try adding the following to your server function:

rvs <- reactiveValues(n_sygdom = 0)

observeEvent(input$n_sygdom, {
  if (input$n_sygdom >= 1 && rvs$n_sygdom == 0) {
    updateRadioButtons(session, "ecs", selected = "No")
    updateRadioButtons(session, "contra.pos", selected = "Contra.")
  }
  rvs$n_sygdom <- input$n_sygdom
})

Edit: To ensure that the score is calculated correctly after input$n_sygdom returns to zero, reset the radio buttons in that case. You can change your if statement to:

if (input$n_sygdom == 0)

And you wouldn’t need reactive values at all.