I'm trying to create a dynamic UI that produces N amount of sections based on the number of selected variables from a selectInput() command. For each variable selected, I want to have its own section that lets you further specify other attributes for that variable (e.g. if it's numeric or character, how to impute missing values, etc.)
I have experience with insertUI() and removeUI() and was able to produce a small example of what I want it to look like. The section of my code that does this looks like this:
insertUI(
selector = '#ui_test',
ui = tags$div(id = "extra_criteria",
h4("Covariate 1 (example)"),
selectInput("cov_1_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_1_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_1_impute_default_level", "Impute default level","0"),
tags$hr(),
h4("Covariate 2 (example)"),
selectInput("cov_2_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_2_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_2_impute_default_level", "Impute default level","0"),
tags$hr(),
h4("Covariate 3 (example)"),
selectInput("cov_3_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_3_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_3_impute_default_level", "Impute default level","0"),
tags$hr(),
h4("Covariate 4 (example)"),
selectInput("cov_4_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_4_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_4_impute_default_level", "Impute default level","0")
)
)
What I want to accomplish is to make the section above robust and dynamic in the sense that if the user only selects 2 variables, then I'd only want to create sections h4("Covariate 1 (example)") and h4("Covariate 2 (example)"). For example, if age and sex were selected then I'd want my section to look like:
insertUI(
selector = '#ui_test',
ui = tags$div(id = "extra_criteria",
h4("Age"),
selectInput("age_class", "Covariate class",
choices = c("numeric","character")),
selectInput("age_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("age_impute_default_level", "Impute default level","0"),
tags$hr(),
h4("Sex"),
selectInput("sex_class", "Covariate class",
choices = c("numeric","character")),
selectInput("sex_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("sex_impute_default_level", "Impute default level","0")
)
)
I was initially going to approach this by looping over the variables in the selected input and creating a long character string of the desired output (i.e. the chunks of h4(Covariate N)), and then passing that through eval(parse(text="...")). Something that in the end will look like this:
insertUI(
selector = '#ui_test',
ui = tags$div(id = "extra_criteria",
eval(parse(text="..."))
)
)
where the "..." section are the chunks of h4("Covariate N) treated as a character string. Now, I don't know if this will work but it's the only approach I have at the moment. Is there a better way of approaching this problem, perhaps with some of the functions within shiny? Any help or advice will be greatly appreciated. My mock example can be found below:
library(shiny)
library(shinyjs)
ui <- shinyUI(fluidPage(
shinyjs::useShinyjs(),
navbarPage("Test",id="navbarPage",
tabPanel("First tab", id = "first_tab",
sidebarLayout(
sidebarPanel(
selectInput('covariates', 'Select covariates', choices = c("age","sex","race","bmi"), multiple=TRUE, selectize=TRUE),
actionButton("set.covariates","Set"),
tags$hr(),
tags$div(id = 'ui_test')
),
mainPanel(
verbatimTextOutput("list")
)
)
))
))
# Define server logic required to draw a histogram
server <- shinyServer(function(input, output, session) {
observe({
if (is.null(input$covariates) || input$covariates == "") {
shinyjs::disable("set.covariates")
} else {
shinyjs::enable("set.covariates")
}
})
observeEvent(input$set.covariates, {
shinyjs::disable("set.covariates")
})
prep.list <- eventReactive(input$set.covariates,{
cov <- input$covariates
timeIndep.list <- NULL
for(L0.i in seq_along(cov)){
timeIndep.list[[L0.i]] <- list("categorical"=FALSE,
"impute"=NA,
"impute_default_level"=NA)
}
names(timeIndep.list) <- cov
return(timeIndep.list)
})
output$list <- renderPrint({
prep.list()
})
observeEvent(req(input$set.covariates), {
insertUI(
selector = '#ui_test',
ui = tags$div(id = "extra_criteria",
h4("Covariate 1 (example)"),
selectInput("cov_1_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_1_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_1_impute_default_level", "Impute default level","0"),
tags$hr(),
h4("Covariate 2 (example)"),
selectInput("cov_2_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_2_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_2_impute_default_level", "Impute default level","0"),
tags$hr(),
h4("Covariate 3 (example)"),
selectInput("cov_3_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_3_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_3_impute_default_level", "Impute default level","0"),
tags$hr(),
h4("Covariate 4 (example)"),
selectInput("cov_4_class", "Covariate class",
choices = c("numeric","character")),
selectInput("cov_4_impute", "Impute",
choices = c("default","mean","mode","median")),
textInput("cov_4_impute_default_level", "Impute default level","0")
)
)})
observeEvent({input$covariates}, {
removeUI(selector = '#extra_criteria')
})
})
# Run the application
shinyApp(ui = ui, server = server)
