R/jsBasicGadget.R

In jsmodule: 'RStudio' Addins and 'Shiny' Modules for Medical Research

#' @title jsBasicGadget: Shiny Gadget of Basic Statistics in Medical Research.
#' @description Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plot
#' @param data data
#' @param nfactor.limit nlevels limit for categorical variables
#' @return Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plot
#' @details Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plot
#' @examples
#' if (interactive()) {
#'   jsBasicGadjet(mtcars)
#' }
#' @rdname jsBasicGadget
#' @export
#' @importFrom GGally ggpairs
#' @importFrom stats as.formula binomial glm
#' @importFrom data.table data.table := .SD
#' @importFrom DT datatable %>% formatStyle styleEqual renderDT DTOutput
#' @importFrom shinycustomloader withLoader
#' @importFrom jstable opt.data opt.tb1 opt.tbreg
#' @import ggplot2
#' @import shiny

jsBasicGadget <- function(data, nfactor.limit = 20) {
  requireNamespace("survival")
  # requireNamespace("survC1")

  ## To remove NOTE.
  val_label <- BinaryGroupRandom <- variable <- NULL

  out <- data.table(data, check.names = F)
  name.old <- names(out)
  out <- data.table(data, check.names = T)
  name.new <- names(out)
  # ref <- data.table(name.old = name.old, name.new = name.new);setkey(ref, name.new)
  ref <- list(name.old = name.old, name.new = name.new)

  ## factor variable
  factor_vars <- names(out)[out[, lapply(.SD, class) %in% c("factor", "character")]]
  out[, (factor_vars) := lapply(.SD, as.factor), .SDcols = factor_vars]
  conti_vars <- setdiff(names(out), factor_vars)
  nclass <- unlist(out[, lapply(.SD, function(x) {
    length(unique(x))
  }), .SDcols = conti_vars])
  # except_vars <- names(nclass)[ nclass== 1 | nclass >= 10]
  add_vars <- names(nclass)[nclass >= 1 & nclass <= 5]

  data.list <- list(data = out, factor_original = factor_vars, conti_original = conti_vars, factor_adds_list = names(nclass)[nclass <= nfactor.limit], factor_adds = add_vars)



  ui <- navbarPage(
    header = tagList(
      includeCSS(system.file("www", "style.css", package = "jsmodule")),
      tags$head(tags$link(rel = "shortcut icon", href = "www/favicon.ico"))
    ),
    # theme = bslib::bs_theme(bootswatch = 'solar'),
    inverse = TRUE,
    title = span(
      "Basic statistics",
      span( # Github & Homepage
        a(
          icon("house"),
          href = "https://www.zarathu.com/",
          target = "_blank",
          style = "color: white;margin-right: 1em;"
        ),
        a(
          icon("github"),
          href = "https://github.com/jinseob2kim/jsmodule",
          target = "_blank",
          style = "color: white;"
        ),
        style = "right: 1em; position: absolute;"
      )
    ),
    tabPanel("Data",
      icon = icon("table"),
      sidebarLayout(
        sidebarPanel(
          uiOutput("factor"),
          uiOutput("binary_check"),
          uiOutput("binary_var"),
          uiOutput("binary_val"),
          uiOutput("ref_check"),
          uiOutput("ref_var"),
          uiOutput("ref_val"),
          uiOutput("subset_check"),
          uiOutput("subset_var"),
          uiOutput("subset_val")
        ),
        mainPanel(
          tabsetPanel(
            type = "pills",
            tabPanel(title = "Data", withLoader(DTOutput("data"), type = "html", loader = "loader6")),
            tabPanel(title = "Label", withLoader(DTOutput("data_label", width = "100%"), type = "html", loader = "loader6"))
          )
        )
      )
    ),
    tabPanel("Table 1",
      icon = icon("percentage"),
      sidebarLayout(
        sidebarPanel(
          tb1moduleUI("tb1")
        ),
        mainPanel(
          withLoader(
            DTOutput("table1"),
            type = "html", loader = "loader6"
          ),
          wellPanel(
            h5("Normal continuous variables  are summarized with Mean (SD) and t-test (2 groups) or ANOVA (> 2 groups)"),
            h5("Non-normal continuous variables are summarized with median [IQR or min,max] and wilcox(2 groups)/kruskal-wallis(>3 groups) test"),
            h5("Categorical variables  are summarized with table")
          )
        )
      )
    ),
    navbarMenu("Regression",
      icon = icon("list-alt"),
      tabPanel(
        "Linear regression",
        sidebarLayout(
          sidebarPanel(
            regressModuleUI("linear")
          ),
          mainPanel(
            withLoader(DTOutput("lineartable"), type = "html", loader = "loader6"),
            br(),
            uiOutput("warning_linear")
          )
        )
      ),
      tabPanel(
        "Logistic regression",
        sidebarLayout(
          sidebarPanel(
            regressModuleUI("logistic")
          ),
          mainPanel(
            withLoader(DTOutput("logistictable"), type = "html", loader = "loader6")
          )
        )
      ),
      tabPanel(
        "Cox model",
        sidebarLayout(
          sidebarPanel(
            coxUI("cox")
          ),
          mainPanel(
            withLoader(DTOutput("coxtable"), type = "html", loader = "loader6")
          )
        )
      )
    ),
    navbarMenu("Plot",
      icon = icon("chart-column"),
      tabPanel(
        "Basic plot",
        sidebarLayout(
          sidebarPanel(
            ggpairsModuleUI1("ggpairs")
          ),
          mainPanel(
            withLoader(plotOutput("ggpairs_plot"), type = "html", loader = "loader6"),
            ggpairsModuleUI2("ggpairs")
          )
        )
      ),
      tabPanel(
        "Histogram",
        sidebarLayout(
          sidebarPanel(
            histogramUI("histogram")
          ),
          mainPanel(
            withLoader(plotOutput("histogram"), type = "html", loader = "loader6"),
            ggplotdownUI("histogram")
          )
        )
      ),
      tabPanel(
        "Scatterplot",
        sidebarLayout(
          sidebarPanel(
            scatterUI("scatter")
          ),
          mainPanel(
            withLoader(plotOutput("scatter_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("scatter")
          )
        )
      ),
      tabPanel(
        "Boxplot",
        sidebarLayout(
          sidebarPanel(
            boxUI("box")
          ),
          mainPanel(
            withLoader(plotOutput("box_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("box")
          )
        )
      ),
      tabPanel(
        "Barplot",
        sidebarLayout(
          sidebarPanel(
            barUI("bar")
          ),
          mainPanel(
            withLoader(plotOutput("bar_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("bar")
          )
        )
      ),
      tabPanel(
        "Lineplot",
        sidebarLayout(
          sidebarPanel(
            lineUI("line")
          ),
          mainPanel(
            withLoader(plotOutput("line_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("line")
          )
        )
      ),
      tabPanel(
        "Kaplan-meier plot",
        sidebarLayout(
          sidebarPanel(
            kaplanUI("kaplan")
          ),
          mainPanel(
            optionUI("kaplan"),
            withLoader(plotOutput("kaplan_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("kaplan")
          )
        )
      )
    ),
    navbarMenu("ROC analysis",
      icon = icon("check"),
      tabPanel(
        "ROC",
        sidebarLayout(
          sidebarPanel(
            rocUI("roc")
          ),
          mainPanel(
            withLoader(plotOutput("plot_roc"), type = "html", loader = "loader6"),
            ggplotdownUI("roc"),
            withLoader(DTOutput("table_roc"), type = "html", loader = "loader6")
          )
        )
      ),
      tabPanel(
        "Time-dependent ROC",
        sidebarLayout(
          sidebarPanel(
            timerocUI("timeroc")
          ),
          mainPanel(
            withLoader(plotOutput("plot_timeroc"), type = "html", loader = "loader6"),
            withLoader(tableOutput("cut_timeroc"), type = "html", loader = "loader6"),
            ggplotdownUI("timeroc"),
            withLoader(DTOutput("table_timeroc"), type = "html", loader = "loader6")
          )
        )
      )
    )
  )

  server <- function(input, output, session) {
    output$factor <- renderUI({
      selectInput("factor_vname",
        label = "Additional categorical variables",
        choices = data.list$factor_adds_list, multiple = T,
        selected = data.list$factor_adds
      )
    })

    observeEvent(input$factor_vname, {
      output$binary_check <- renderUI({
        checkboxInput("check_binary", "Make binary variables")
      })

      output$ref_check <- renderUI({
        checkboxInput("check_ref", "Change reference of categorical variables")
      })


      output$subset_check <- renderUI({
        checkboxInput("check_subset", "Subset data")
      })
    })

    observeEvent(input$check_binary, {
      var.conti <- setdiff(names(data.list$data), c(data.list$factor_original, input$factor_vname))
      output$binary_var <- renderUI({
        req(input$check_binary == T)
        selectInput("var_binary", "Variables to dichotomize",
          choices = var.conti, multiple = T,
          selected = var.conti[1]
        )
      })

      output$binary_val <- renderUI({
        req(input$check_binary == T)
        req(length(input$var_binary) > 0)
        outUI <- tagList()
        for (v in seq_along(input$var_binary)) {
          med <- stats::quantile(data.list$data[[input$var_binary[[v]]]], c(0.05, 0.5, 0.95), na.rm = T)
          outUI[[v]] <- splitLayout(
            cellWidths = c("25%", "75%"),
            selectInput(paste0("con_binary", v), paste0("Define reference:"),
              choices = c("\u2264", "\u2265", "\u003c", "\u003e"), selected = "\u2264"
            ),
            numericInput(paste0("cut_binary", v), input$var_binary[[v]],
              value = med[2], min = med[1], max = med[3]
            )
          )
        }
        outUI
      })
    })

    observeEvent(input$check_ref, {
      var.factor <- c(data.list$factor_original, input$factor_vname)
      output$ref_var <- renderUI({
        req(input$check_ref == T)
        selectInput("var_ref", "Variables to change reference",
          choices = var.factor, multiple = T,
          selected = var.factor[1]
        )
      })

      output$ref_val <- renderUI({
        req(input$check_ref == T)
        req(length(input$var_ref) > 0)
        outUI <- tagList()
        for (v in seq_along(input$var_ref)) {
          outUI[[v]] <- selectInput(paste0("con_ref", v), paste0("Reference: ", input$var_ref[[v]]),
            choices = levels(factor(data.list$data[[input$var_ref[[v]]]])), selected = levels(factor(data.list$data[[input$var_ref[[v]]]]))[2]
          )
        }
        outUI
      })
    })

    observeEvent(input$check_subset, {
      output$subset_var <- renderUI({
        req(input$check_subset == T)
        # factor_subset <- c(data.list$factor_original, input$factor_vname)

        # validate(
        #  need(length(factor_subset) > 0 , "No factor variable for subsetting")
        # )

        tagList(
          selectInput("var_subset", "Subset variables",
            choices = names(data.list$data), multiple = T,
            selected = names(data.list$data)[1]
          )
        )
      })

      output$subset_val <- renderUI({
        req(input$check_subset == T)
        req(length(input$var_subset) > 0)
        var.factor <- c(data.list$factor_original, input$factor_vname)

        outUI <- tagList()

        for (v in seq_along(input$var_subset)) {
          if (input$var_subset[[v]] %in% var.factor) {
            varlevel <- levels(as.factor(data.list$data[[input$var_subset[[v]]]]))
            outUI[[v]] <- selectInput(paste0("val_subset", v), paste0("Subset value: ", input$var_subset[[v]]),
              choices = varlevel, multiple = T,
              selected = varlevel[1]
            )
          } else {
            val <- stats::quantile(data.list$data[[input$var_subset[[v]]]], na.rm = T)
            outUI[[v]] <- sliderInput(paste0("val_subset", v), paste0("Subset range: ", input$var_subset[[v]]),
              min = val[1], max = val[5],
              value = c(val[2], val[4])
            )
          }
        }
        outUI
      })
    })


    data.info <- reactive({
      out <- data.table::data.table(data.list$data)
      out[, (data.list$conti_original) := lapply(.SD, function(x) {
        as.numeric(as.vector(x))
      }), .SDcols = data.list$conti_original]
      if (!is.null(input$factor_vname)) {
        out[, (input$factor_vname) := lapply(.SD, as.factor), .SDcols = input$factor_vname]
      }
      out.label <- mk.lev(out)
      # out.label[, var_label := ref[out.label$variable, name.old]]

      req(!is.null(input$check_binary))
      if (input$check_binary == T) {
        validate(
          need(length(input$var_binary) > 0, "No variables to dichotomize")
        )
        sym.ineq <- c("\u2264", "\u2265", "\u003c", "\u003e")
        names(sym.ineq) <- sym.ineq[4:1]
        sym.ineq2 <- c("le", "ge", "l", "g")
        names(sym.ineq2) <- sym.ineq
        for (v in seq_along(input$var_binary)) {
          req(input[[paste0("con_binary", v)]])
          req(input[[paste0("cut_binary", v)]])
          if (input[[paste0("con_binary", v)]] == "\u2264") {
            out[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) <= input[[paste0("cut_binary", v)]]))]
          } else if (input[[paste0("con_binary", v)]] == "\u2265") {
            out[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) >= input[[paste0("cut_binary", v)]]))]
          } else if (input[[paste0("con_binary", v)]] == "\u003c") {
            out[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) < input[[paste0("cut_binary", v)]]))]
          } else {
            out[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) > input[[paste0("cut_binary", v)]]))]
          }

          cn.new <- paste0(input$var_binary[[v]], "_group_", sym.ineq2[input[[paste0("con_binary", v)]]], input[[paste0("cut_binary", v)]])
          data.table::setnames(out, "BinaryGroupRandom", cn.new)

          label.binary <- mk.lev(out[, .SD, .SDcols = cn.new])
          label.binary[, var_label := paste0(input$var_binary[[v]], " _group")]
          label.binary[, val_label := paste0(c(input[[paste0("con_binary", v)]], sym.ineq[input[[paste0("con_binary", v)]]]), " ", input[[paste0("cut_binary", v)]])]
          out.label <- rbind(out.label, label.binary)
        }
      }

      if (!is.null(input$check_ref)) {
        if (input$check_ref) {
          validate(
            need(length(input$var_ref) > 0, "No variables to change reference")
          )
          for (v in seq_along(input$var_ref)) {
            req(input[[paste0("con_ref", v)]])
            out[[input$var_ref[[v]]]] <- stats::relevel(out[[input$var_ref[[v]]]], ref = input[[paste0("con_ref", v)]])
            out.label[variable == input$var_ref[[v]], ":="(level = levels(out[[input$var_ref[[v]]]]), val_label = levels(out[[input$var_ref[[v]]]]))]
          }
        }
      }


      if (!is.null(input$check_subset)) {
        if (input$check_subset) {
          validate(
            need(length(input$var_subset) > 0, "No variables for subsetting"),
            need(all(sapply(1:length(input$var_subset), function(x) {
              length(input[[paste0("val_subset", x)]])
            })), "No value for subsetting")
          )
          var.factor <- c(data.list$factor_original, input$factor_vname)
          # var.conti <- setdiff(data()$conti_original, input$factor_vname)

          for (v in seq_along(input$var_subset)) {
            if (input$var_subset[[v]] %in% var.factor) {
              out <- out[get(input$var_subset[[v]]) %in% input[[paste0("val_subset", v)]]]
              # var.factor <- c(data()$factor_original, input$factor_vname)
              out[, (var.factor) := lapply(.SD, factor), .SDcols = var.factor]
              out.label2 <- mk.lev(out)[, c("variable", "level")]
              data.table::setkey(out.label, "variable", "level")
              data.table::setkey(out.label2, "variable", "level")
              out.label <- out.label[out.label2]
            } else {
              out <- out[get(input$var_subset[[v]]) >= input[[paste0("val_subset", v)]][1] & get(input$var_subset[[v]]) <= input[[paste0("val_subset", v)]][2]]
              # var.factor <- c(data()$factor_original, input$factor_vname)
              out[, (var.factor) := lapply(.SD, factor), .SDcols = var.factor]
              out.label2 <- mk.lev(out)[, c("variable", "level")]
              data.table::setkey(out.label, "variable", "level")
              data.table::setkey(out.label2, "variable", "level")
              out.label <- out.label[out.label2]
            }
          }
        }
      }
      for (vn in ref[["name.new"]]) {
        w <- which(ref[["name.new"]] == vn)
        out.label[variable == vn, var_label := ref[["name.old"]][w]]
      }

      return(list(data = out, label = out.label))
    })

    data <- reactive(data.info()$data)
    data.label <- reactive(data.info()$label)

    output$data <- renderDT({
      datatable(data(),
        rownames = F, editable = F, extensions = "Buttons", caption = "Data",
        options = c(jstable::opt.data("data"), list(scrollX = TRUE))
      )
    })


    output$data_label <- renderDT({
      datatable(data.label(),
        rownames = F, editable = F, extensions = "Buttons", caption = "Label of data",
        options = c(jstable::opt.data("label"), list(scrollX = TRUE))
      )
    })




    out_tb1 <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit, showAllLevels = T)

    output$table1 <- renderDT({
      tb <- out_tb1()$table
      cap <- out_tb1()$caption
      out.tb1 <- datatable(tb,
        rownames = T, extensions = "Buttons", caption = cap,
        options = c(
          jstable::opt.tb1("tb1"),
          list(columnDefs = list(list(visible = FALSE, targets = which(colnames(tb) %in% c("test", "sig"))))),
          list(scrollX = TRUE)
        )
      )
      if ("sig" %in% colnames(tb)) {
        out.tb1 <- out.tb1 %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "yellow"))
      }
      return(out.tb1)
    })

    out_linear <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$lineartable <- renderDT({
      hide <- which(colnames(out_linear()$table) == "sig")
      datatable(out_linear()$table,
        rownames = T, extensions = "Buttons", caption = out_linear()$caption,
        options = c(
          jstable::opt.tbreg(out_linear()$caption),
          list(columnDefs = list(list(visible = FALSE, targets = hide))),
          list(scrollX = TRUE)
        )
      ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "yellow"))
    })

    output$warning_linear <- renderText({
      paste("<b>", out_linear()$warning, "</b>")
    })

    out_logistic <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$logistictable <- renderDT({
      hide <- which(colnames(out_logistic()$table) == "sig")
      datatable(out_logistic()$table,
        rownames = T, extensions = "Buttons", caption = out_logistic()$caption,
        options = c(
          jstable::opt.tbreg(out_logistic()$caption),
          list(columnDefs = list(list(visible = FALSE, targets = hide))),
          list(scrollX = TRUE)
        )
      ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "yellow"))
    })

    out_cox <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = T, nfactor.limit = nfactor.limit)

    output$coxtable <- renderDT({
      hide <- which(colnames(out_cox()$table) == c("sig"))
      datatable(out_cox()$table,
        rownames = T, extensions = "Buttons", caption = out_cox()$caption,
        options = c(
          opt.tbreg(out_cox()$caption),
          list(columnDefs = list(list(visible = FALSE, targets = hide)))
        )
      ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "yellow"))
    })


    out_ggpairs <- callModule(ggpairsModule2, "ggpairs", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$ggpairs_plot <- renderPlot({
      print(out_ggpairs())
    })

    out_scatter <- scatterServer("scatter", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$scatter_plot <- renderPlot({
      print(out_scatter())
    })

    out_box <- boxServer("box", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$box_plot <- renderPlot({
      print(out_box())
    })

    out_histogram <- histogramServer("histogram", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$histogram <- renderPlot({
      print(out_histogram())
    })

    out_bar <- barServer("bar", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$bar_plot <- renderPlot({
      print(out_bar())
    })

    out_line <- lineServer("line", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$line_plot <- renderPlot({
      print(out_line())
    })

    out_kaplan <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$kaplan_plot <- renderPlot({
      print(out_kaplan())
    })

    out_roc <- callModule(rocModule, "roc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$plot_roc <- renderPlot({
      print(out_roc()$plot)
    })

    output$table_roc <- renderDT({
      datatable(out_roc()$tb,
        rownames = F, editable = F, extensions = "Buttons",
        caption = "ROC results",
        options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE))
      )
    })


    out_timeroc <- callModule(timerocModule, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$plot_timeroc <- renderPlot({
      print(out_timeroc()$plot)
    })

    output$table_timeroc <- renderDT({
      datatable(out_timeroc()$tb,
        rownames = F, editable = F, extensions = "Buttons", caption = "ROC results",
        options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE))
      )
    })

    output$cut_timeroc <- renderTable(
      {
        out_timeroc()$cut
      },
      caption = "Best cutoff",
      caption.placement = "top"
    )

    session$onSessionEnded(function() {
      stopApp()
    })
  }





  # viewer <- dialogViewer("Descriptive statistics", width = 1100, height = 850)
  viewer <- browserViewer(browser = getOption("browser"))
  # viewer <- paneViewer()
  runGadget(ui, server, viewer = viewer)
}



#' @title jsBasicAddin: Rstudio addin of jsBasicGadget
#' @description Rstudio addin of jsBasicGadget
#' @return Rstudio addin of jsBasicGadget
#' @details Rstudio addin of jsBasicGadget
#' @examples
#' if (interactive()) {
#'   jsBasicAddin()
#' }
#' @seealso
#'  \code{\link[rstudioapi]{rstudio-editors}}
#' @rdname jsBasicAddin
#' @export
#' @importFrom rstudioapi getActiveDocumentContext


jsBasicAddin <- function() {
  context <- rstudioapi::getActiveDocumentContext()
  # Set the default data to use based on the selection.
  dataString <- context$selection[[1]]$text
  data <- get(dataString, envir = .GlobalEnv)
  # viewer <- dialogViewer("Subset", width = 1000, height = 800)
  jsBasicGadget(data)
}


#' @title jsBasicExtAddin: RStudio Addin for basic data analysis with external data.
#' @description RStudio Addin for basic data analysis with external csv/xlsx/sas7bdat/sav/dta file.
#' @param nfactor.limit nlevels limit for categorical variables, Default: 20
#' @param max.filesize Maximum file size to upload (MB), Default: 2048 (2 GB)
#' @return RStudio Addin for basic data analysis with external data.
#' @details RStudio Addin for basic data analysis with external csv/xlsx/sas7bdat/sav/dta file.
#' @examples
#' if (interactive()) {
#'   jsBasicExtAddin()
#' }
#' @seealso
#'  \code{\link[survival]{lung}}
#'  \code{\link[data.table]{fwrite}}
#'  \code{\link[jstable]{opt.tbreg}}
#' @rdname jsBasicExtAddin
#' @export
#' @importFrom data.table fwrite
#' @importFrom jstable opt.tbreg
#' @importFrom DT datatable %>% formatStyle styleEqual renderDT DTOutput
#' @importFrom shinycustomloader withLoader
#' @import shiny

jsBasicExtAddin <- function(nfactor.limit = 20, max.filesize = 2048) {
  options(shiny.maxRequestSize = max.filesize * 1024^2)

  ui <- navbarPage(
    header = tagList(
      includeCSS(system.file("www", "style.css", package = "jsmodule")),
      tags$head(tags$link(rel = "shortcut icon", href = "www/favicon.ico"))
    ),
    # theme = bslib::bs_theme(bootswatch = 'solar'),
    inverse = TRUE,
    title = span(
      "Basic statistics",
      span( # Github & Homepage
        a(
          icon("house"),
          href = "https://www.zarathu.com/",
          target = "_blank",
          style = "color: white;margin-right: 1em;"
        ),
        a(
          icon("github"),
          href = "https://github.com/jinseob2kim/jsmodule",
          target = "_blank",
          style = "color: white;"
        ),
        style = "right: 1em; position: absolute;"
      )
    ),
    # Data
    tabPanel(
      title = "Data",
      icon = icon("table"),
      sidebarLayout(
        sidebarPanel(
          uiOutput("import"),
          downloadButton(outputId = "downloadData", label = "Example data", class = "primary")
        ),
        mainPanel(
          tabsetPanel(
            type = "pills",
            tabPanel(
              title = "Data",
              style = "margin-top:1em;",
              markdown("> Category data is shown with <span style='background: #337ab7; color: #fff; border-radius: 3px; margin: 0 3px 3px 0; padding: 1px 3px;'>**Blue**</span>."),
              withLoader(
                DTOutput("data"),
                type = "html",
                loader = "loader6"
              )
            ),
            tabPanel(
              title = "Label",
              style = "margin-top:1em;",
              withLoader(
                DTOutput("data_label", width = "100%"),
                type = "html",
                loader = "loader6"
              )
            )
          ),
          htmlOutput("naomit")
        )
      )
    ),

    # Table 1
    tabPanel(
      title = "Table 1",
      icon = icon("percentage"),
      sidebarLayout(
        sidebarPanel(
          tb1moduleUI("tb1")
        ),
        mainPanel(
          markdown("> Table 1 for Descriptive statistics, see
          <a target = '_blank' href = 'https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-3-structure-content-clinical-study-reports-step-5_en.pdf'>Ich E3 Guideline 11.2 for medical definition</a>"),
          withLoader(
            DTOutput("table1"),
            type = "html",
            loader = "loader6"
          ),
          wellPanel(
            h5("Normal continuous variables  are summarized with Mean (SD) and t-test (2 groups) or ANOVA (> 2 groups)"),
            h5("Non-normal continuous variables are summarized with median [IQR or min,max] and wilcox(2 groups)/kruskal-wallis(>3 groups) test"),
            h5("Categorical variables  are summarized with table")
          )
        )
      )
    ),

    # Regression
    navbarMenu(
      title = "Regression",
      icon = icon("list-alt"),
      tabPanel(
        title = "Linear regression",
        sidebarLayout(
          sidebarPanel(
            regressModuleUI("linear")
          ),
          mainPanel(
            withLoader(
              DTOutput("lineartable"),
              type = "html",
              loader = "loader6"
            ),
            br(),
            uiOutput("warning_linear")
          )
        )
      ),
      tabPanel(
        title = "Logistic regression",
        sidebarLayout(
          sidebarPanel(
            regressModuleUI("logistic")
          ),
          mainPanel(
            withLoader(
              DTOutput("logistictable"),
              type = "html",
              loader = "loader6"
            )
          )
        )
      ),
      tabPanel(
        title = "Cox model",
        sidebarLayout(
          sidebarPanel(
            coxUI("cox")
          ),
          mainPanel(
            withLoader(
              DTOutput("coxtable"),
              type = "html",
              loader = "loader6"
            )
          )
        )
      )
    ),

    # Plot
    navbarMenu(
      title = "Plot",
      icon = icon("chart-column"),
      tabPanel(
        title = "Basic plot",
        sidebarLayout(
          sidebarPanel(
            ggpairsModuleUI1("ggpairs")
          ),
          mainPanel(
            withLoader(
              plotOutput("ggpairs_plot"),
              type = "html",
              loader = "loader6"
            ),
            ggpairsModuleUI2("ggpairs")
          )
        )
      ),
      tabPanel(
        title = "Histogram",
        sidebarLayout(
          sidebarPanel(
            histogramUI("histogram")
          ),
          mainPanel(
            withLoader(
              plotOutput("histogram"),
              type = "html",
              loader = "loader6"
            ),
            ggplotdownUI("histogram")
          )
        )
      ),
      tabPanel(
        title = "Scatterplot",
        sidebarLayout(
          sidebarPanel(
            scatterUI("scatter")
          ),
          mainPanel(
            withLoader(
              plotOutput("scatter_plot"),
              type = "html",
              loader = "loader6"
            ),
            ggplotdownUI("scatter")
          )
        )
      ),
      tabPanel(
        title = "Boxplot",
        sidebarLayout(
          sidebarPanel(
            boxUI("box")
          ),
          mainPanel(
            withLoader(
              plotOutput("box_plot"),
              type = "html",
              loader = "loader6"
            ),
            ggplotdownUI("box")
          )
        )
      ),
      tabPanel(
        title = "Barplot",
        sidebarLayout(
          sidebarPanel(
            barUI("bar")
          ),
          mainPanel(
            withLoader(plotOutput("bar_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("bar")
          )
        )
      ),
      tabPanel(
        title = "Lineplot",
        sidebarLayout(
          sidebarPanel(
            lineUI("line")
          ),
          mainPanel(
            withLoader(plotOutput("line_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("line")
          )
        )
      ),
      tabPanel(
        title = "Kaplan-meier plot",
        sidebarLayout(
          sidebarPanel(
            kaplanUI("kaplan")
          ),
          mainPanel(
            optionUI("kaplan"),
            withLoader(plotOutput("kaplan_plot"), type = "html", loader = "loader6"),
            ggplotdownUI("kaplan")
          )
        )
      )
    ),
    # ROC Analysis
    navbarMenu(
      title = "ROC analysis",
      icon = icon("check"),
      tabPanel(
        title = "ROC",
        sidebarLayout(
          sidebarPanel(
            rocUI("roc")
          ),
          mainPanel(
            withLoader(plotOutput("plot_roc"), type = "html", loader = "loader6"),
            ggplotdownUI("roc"),
            withLoader(DTOutput("table_roc"), type = "html", loader = "loader6")
          )
        )
      ),
      tabPanel(
        title = "Time-dependent ROC",
        sidebarLayout(
          sidebarPanel(
            timerocUI("timeroc")
          ),
          mainPanel(
            withLoader(plotOutput("plot_timeroc"), type = "html", loader = "loader6"),
            withLoader(tableOutput("cut_timeroc"), type = "html", loader = "loader6"),
            ggplotdownUI("timeroc"),
            withLoader(DTOutput("table_timeroc"), type = "html", loader = "loader6")
          )
        )
      ),
    )
  )

  server <- function(input, output, session) {
    output$downloadData <- downloadHandler(
      filename = function() {
        paste("example_basic", ".csv", sep = "")
      },
      content = function(file) {
        out <- survival::lung
        out$status <- as.integer(out$status == 1)
        data.table::fwrite(out, file)
      }
    )

    output$import <- renderUI({
      csvFileInput(id = "datafile")
    })

    data.info <- callModule(csvFile, "datafile", nfactor.limit = nfactor.limit)
    data <- reactive(data.info()$data)
    data.label <- reactive(data.info()$label)

    data.label

    output$data <- renderDT({
      PRdata <- data()
      dl <- data.label()

      nv <- dl$variable[which(dl$class %in% c("factor", "character"))]

      v <- sapply(colnames(PRdata), function(i) {
        if (i %in% nv) {
          return(paste0("<div style = 'background: #337ab7; color: #fff; border-radius: 3px; margin: 0 3px 3px 0; padding: 1px 3px;'>", i, "</div>"))
        }
        return(i)
      }, simplify = TRUE, USE.NAMES = FALSE)

      colnames(PRdata) <- unlist(v)

      datatable(
        data = PRdata, # column name change
        # data = data(),
        rownames = F,
        editable = F,
        extensions = c("Buttons", "ColReorder", "KeyTable"),
        # filter = 'top', # critical issue with scrollX
        escape = FALSE,

        # caption = "Data",
        options =
        # opt.data("data"),
          list(
            # dom = 'tlfBrip', # Length, Table, Filter, Button, Information, Pagination
            dom = "lftBrp", # Length, Table, Filter, Button, Information, Pagination
            lengthMenu = list(c(10, 25, -1), c("10", "25", "All")),
            pageLength = 10,
            scrollX = TRUE,
            buttons = c("copy", "print", "csv", "excel", "pdf", I("colvis")),
            colReorder = TRUE,
            keys = TRUE
          )
      )
    })

    output$data_label <- renderDT({
      datatable(
        data = data.label(),
        rownames = F,
        editable = F,
        extensions = c("Buttons", "KeyTable"),
        # filter = 'top', # Not works
        # caption = "Label of data",
        options = c(
          opt.data("label"),
          list(
            scrollX = TRUE,
            keys = TRUE
          )
        )
      )
    })

    output$naomit <- renderText({
      data.info()$naomit
    })

    out_tb1 <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$table1 <- renderDT({
      tb <- out_tb1()$table
      cap <- out_tb1()$caption
      out.tb1 <- datatable(tb,
        rownames = T, extensions = "Buttons", caption = cap,
        options = c(
          opt.tb1("tb1"),
          list(columnDefs = list(list(visible = FALSE, targets = which(colnames(tb) %in% c("test", "sig"))))),
          list(scrollX = TRUE)
        )
      )
      if ("sig" %in% colnames(tb)) {
        out.tb1 <- out.tb1 %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "#fed9cc"))
      }
      return(out.tb1)
    })

    out_linear <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = T, nfactor.limit = nfactor.limit)

    output$lineartable <- renderDT({
      hide <- which(colnames(out_linear()$table) == "sig")
      datatable(out_linear()$table,
        rownames = T, extensions = "Buttons", caption = out_linear()$caption,
        options = c(
          opt.tbreg(out_linear()$caption),
          list(columnDefs = list(list(visible = FALSE, targets = hide))),
          list(scrollX = TRUE)
        )
      ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "#fed9cc"))
    })

    output$warning_linear <- renderText({
      paste("<b>", out_linear()$warning, "</b>")
    })

    out_logistic <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$logistictable <- renderDT({
      hide <- which(colnames(out_logistic()$table) == "sig")
      datatable(out_logistic()$table,
        rownames = T, extensions = "Buttons", caption = out_logistic()$caption,
        options = c(
          opt.tbreg(out_logistic()$caption),
          list(columnDefs = list(list(visible = FALSE, targets = hide))),
          list(scrollX = TRUE)
        )
      ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "#fed9cc"))
    })


    out_cox <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = T, nfactor.limit = nfactor.limit)

    output$coxtable <- renderDT({
      hide <- which(colnames(out_cox()$table) == c("sig"))
      datatable(out_cox()$table,
        rownames = T, extensions = "Buttons", caption = out_cox()$caption,
        options = c(
          opt.tbreg(out_cox()$caption),
          list(columnDefs = list(list(visible = FALSE, targets = hide)))
        )
      ) %>% formatStyle("sig", target = "row", backgroundColor = styleEqual("**", "#fed9cc"))
    })

    out_ggpairs <- callModule(ggpairsModule2, "ggpairs", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$ggpairs_plot <- renderPlot({
      print(out_ggpairs())
    })

    out_scatter <- scatterServer("scatter", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$scatter_plot <- renderPlot({
      print(out_scatter())
    })

    out_box <- boxServer("box", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$box_plot <- renderPlot({
      print(out_box())
    })

    out_histogram <- histogramServer("histogram", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$histogram <- renderPlot({
      print(out_histogram())
    })

    out_bar <- barServer("bar", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$bar_plot <- renderPlot({
      print(out_bar())
    })

    out_line <- lineServer("line", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$line_plot <- renderPlot({
      print(out_line())
    })


    out_kaplan <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$kaplan_plot <- renderPlot({
      print(out_kaplan())
    })

    out_roc <- callModule(rocModule, "roc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$plot_roc <- renderPlot({
      print(out_roc()$plot)
    })

    output$table_roc <- renderDT({
      datatable(out_roc()$tb,
        rownames = F, editable = F, extensions = "Buttons",
        caption = "ROC results",
        options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE))
      )
    })



    out_timeroc <- callModule(timerocModule, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)

    output$plot_timeroc <- renderPlot({
      print(out_timeroc()$plot)
    })

    output$table_timeroc <- renderDT({
      datatable(out_timeroc()$tb,
        rownames = F, editable = F, extensions = "Buttons", caption = "ROC results",
        options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE))
      )
    })

    output$cut_timeroc <- renderTable(
      {
        out_timeroc()$cut
      },
      caption = "Best cutoff",
      caption.placement = "top"
    )

    session$onSessionEnded(function() {
      stopApp()
    })
  }

  # viewer <- dialogViewer("Descriptive statistics", width = 1100, height = 850)
  viewer <- browserViewer(browser = getOption("browser"))
  # viewer <- paneViewer()
  runGadget(ui, server, viewer = viewer)
}