Title:	'RStudio' Addins and 'Shiny' Modules for Medical Research
Version:	1.6.11
Date:	2025-06-30
Description:	'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Depends:	R (≥ 3.4.0)
License:	Apache License 2.0
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	data.table, DT, epiDisplay, flextable, forestploter, geepack, GGally, ggplot2, bslib, ggpubr, haven, Hmisc, jskm(≥ 0.4.4), jstable(≥ 1.3.8), labelled, MatchIt(≥ 3.0.0), maxstat, methods, officer, pROC, purrr, RColorBrewer, readr, readxl, rstudioapi, rvg, scales, see, shiny, shinycustomloader, shinyjs, shinyWidgets, stats, survey, survIDINRI, survival, timeROC, utils, ggrepel, htmltools, riskRegression, R6
URL:	https://jinseob2kim.github.io/jsmodule/, https://github.com/jinseob2kim/jsmodule
BugReports:	https://github.com/jinseob2kim/jsmodule/issues
Suggests:	testthat, shinytest, knitr, rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-02 08:09:45 UTC; js
Author:	Jinseob Kim [aut, cre], Zarathu [cph, fnd], Hyunki Lee [aut], Changwoo Lim [aut], Jinhwan Kim [aut], Yoonkyoung Jeon [aut], Jaewoong Heo [aut], Youngsun Park [aut], Hyungwoo Jo [aut], Jeongmin Seo [aut], Hojun LEE [aut], Sungho Choi [aut], Yeji Kang [aut]
Maintainer:	Jinseob Kim <jinseob2kim@gmail.com>
Repository:	CRAN
Date/Publication:	2025-07-02 08:30:05 UTC

DataManager R6 Class for Shiny Modules

Description

An R6 class to manage data loading, preprocessing, and common UI elements for various Shiny modules in the package. It handles file reading (csv, xlsx, sav, sas7bdat, dta), initial variable analysis, and the rendering and logic for common data manipulations like creating binary variables, changing factor references, and subsetting data. This class is designed to be used internally by other Shiny server modules to reduce code duplication.

Public fields

Methods

Public methods

• DataManager$new()

• DataManager$get_reactive_data()

• DataManager$clone()

Method new()

Create a new DataManager object.

Usage

DataManager$new(input, output, session, nfactor.limit = 20)

Arguments

Returns

A new 'DataManager' object.

Method get_reactive_data()

Returns the final processed data as a reactive expression. This is the main output to be used by the calling module.

Usage

DataManager$get_reactive_data()

Returns

A reactive expression that returns a list containing the processed 'data', 'label' information, and 'naomit' message.

Method clone()

The objects of this class are cloneable with this method.

Usage

DataManager$clone(deep = FALSE)

Arguments

FilePs: Shiny module Server for propensity score analysis.

Description

Server-side logic for propensity score analysis. It uses 'DataManager' for common data tasks and adds specific controls and calculations for propensity score matching.

Usage

FilePs(input, output, session, nfactor.limit = 20)

Arguments

Value

A reactive expression returning a list with matched data and other information.

FilePsInput: Shiny module UI for propensity score analysis.

Description

Provides a file input and UI outputs for options related to propensity score matching.

Usage

FilePsInput(id, label = "Upload data (csv/xlsx/sav/sas7bdat/dta)")

Arguments

Value

A Shiny UI object.

Examples

if (interactive()) {
  library(shiny)
  library(DT)
  library(jstable)

  ui <- fluidPage(
    sidebarLayout(
      sidebarPanel(
        FilePsInput("datafile")
      ),
      mainPanel(
        tabsetPanel(
          type = "pills",
          tabPanel("Data", DTOutput("data")),
          tabPanel("Matching data", DTOutput("matdata")),
          tabPanel("Label", DTOutput("data_label"))
        )
      )
    )
  )

  server <- function(input, output, session) {
    mat_info <- callModule(FilePs, "datafile")

    output$data <- renderDT({ mat_info()$data })
    output$matdata <- renderDT({ mat_info()$matdata })
    output$data_label <- renderDT({ mat_info()$label })
  }
  shinyApp(ui, server)
}

FileRepeated: Server for repeated measures analysis.

Description

Server module for repeated measures analysis. It uses 'DataManager' and adds a control for selecting the repeated measures variable.

Usage

FileRepeated(input, output, session, nfactor.limit = 20)

Arguments

Value

A reactive list with the processed 'data', 'label', and 'id.gee'.

FileRepeatedInput: UI for repeated measures analysis.

Description

File upload UI for repeated measure analysis.

Usage

FileRepeatedInput(id, label = "Upload data (csv/xlsx/sav/sas7bdat/dta)")

Arguments

Value

A Shiny UI object.

Examples

if (interactive()) {
  library(shiny)
  library(DT)
  library(jstable)

  ui <- fluidPage(
    sidebarLayout(
      sidebarPanel(FileRepeatedInput("datafile")),
      mainPanel(
        tabsetPanel(
          type = "pills",
          tabPanel("Data", DTOutput("data")),
          tabPanel("Label", DTOutput("data_label"))
        )
      )
    )
  )

  server <- function(input, output, session) {
    data_info <- callModule(FileRepeated, "datafile")
    output$data <- renderDT({
      data_info()$data
    })
    output$label <- renderDT({
      data_info()$label
    })
  }
  shinyApp(ui, server)
}

FileSurvey: Server for survey data analysis.

Description

Server module for survey data analysis. It uses 'DataManager' and adds controls and logic for creating a 'survey.design' object.

Usage

FileSurvey(input, output, session, nfactor.limit = 20)

Arguments

Value

A reactive list with 'data', 'label', 'naomit', and the 'survey' object.

FileSurveyInput: UI for survey data analysis.

Description

File upload UI for survey data analysis, with controls for survey design elements.

Usage

FileSurveyInput(id, label = "Upload data (csv/xlsx/sav/sas7bdat/dta)")

Arguments

Value

A Shiny UI object.

Examples

if (interactive()) {
  library(shiny)
  library(DT)
  library(jstable)
  library(survey)

  ui <- fluidPage(
    sidebarLayout(
      sidebarPanel(FileSurveyInput("datafile")),
      mainPanel(
        h4("Survey object details:"),
        verbatimTextOutput("survey_summary"),
        tabsetPanel(
          type = "pills",
          tabPanel("Data", DTOutput("data")),
          tabPanel("Label", DTOutput("data_label"))
        )
      )
    )
  )

  server <- function(input, output, session) {
    data_info <- callModule(FileSurvey, "datafile")
    output$data <- renderDT({
      data_info()$data
    })
    output$label <- renderDT({
      data_info()$label
    })
    output$survey_summary <- renderPrint({
      print(data_info()$survey)
    })
  }
  shinyApp(ui, server)
}

GEEModuleLinear: shiny modulde server for gaussian generalized estimating equation(GEE) using reactive data.

Description

Shiny modulde server for gaussian generalized estimating equation(GEE) using reactive data.

Usage

GEEModuleLinear(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  id.gee,
  vec.event = NULL
)

Arguments

Details

Shiny modulde server for gaussian generalized estimating equation(GEE) using reactive data.

Value

Shiny modulde server for gaussian generalized estimating equation(GEE).

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      GEEModuleUI("linear")
    ),
    mainPanel(
      DTOutput("lineartable")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))
  id.gee <- reactive("mpg")

  out_linear <- callModule(GEEModuleLinear, "linear",
    data = data, data_label = data.label,
    data_varStruct = NULL, id.gee = id.gee
  )

  output$lineartable <- renderDT({
    hide <- which(colnames(out_linear()$table) == "sig")
    datatable(out_linear()$table,
      rownames = T, extension = "Buttons", caption = out_linear()$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("**", "yellow"))
  })
}

GEEModuleLogistic: shiny modulde server for binomial gaussian generalized estimating equation(GEE) using reactive data.

Description

Shiny modulde server for binomial gaussian generalized estimating equation(GEE) using reactive data.

Usage

GEEModuleLogistic(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  id.gee,
  vec.event = NULL
)

Arguments

Details

Shiny modulde server for binomial gaussian generalized estimating equation(GEE) using reactive data.

Value

Shiny modulde server for binomial gaussian generalized estimating equation(GEE).

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      GEEModuleUI("logistic")
    ),
    mainPanel(
      DTOutput("logistictable")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))
  id.gee <- reactive("mpg")

  out_logistic <- callModule(GEEModuleLogistic, "logistic",
    data = data, data_label = data.label,
    data_varStruct = NULL, id.gee = id.gee
  )

  output$logistictable <- renderDT({
    hide <- which(colnames(out_logistic()$table) == "sig")
    datatable(out_logistic()$table,
      rownames = T, extension = "Buttons",
      caption = out_logistic()$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("**", "yellow"))
  })
}

GEEModuleUI: shiny modulde UI for generalized estimating equation(GEE).

Description

Shiny modulde UI for generalized estimating equation(GEE).

Usage

GEEModuleUI(id)

Arguments

Details

Shiny modulde UI for generalized estimating equation(GEE).

Value

Shiny modulde UI for generalized estimating equation(GEE).

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      GEEModuleUI("linear")
    ),
    mainPanel(
      DTOutput("lineartable")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))
  id.gee <- reactive("mpg")

  out_linear <- callModule(GEEModuleLinear, "linear",
    data = data, data_label = data.label,
    data_varStruct = NULL, id.gee = id.gee
  )

  output$lineartable <- renderDT({
    hide <- which(colnames(out_linear()$table) == "sig")
    datatable(out_linear()$table,
      rownames = T, extension = "Buttons", caption = out_linear()$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("**", "yellow"))
  })
}

ROC_table: extract AUC, NRI and IDI information from list of roc object in pROC packages.

Description

extract AUC, NRI and IDI information from list of roc in pROC packages

Usage

ROC_table(ListModel, dec.auc = 3, dec.p = 3)

Arguments

Details

extract AUC, NRI and IDI information from list of roc object in pROC packages.

Value

table of AUC, NRI and IDI information

See Also

ci.auc,roc.test data.table, rbindlist

Examples

library(pROC)
m1 <- glm(vs ~ am + gear, data = mtcars, family = binomial)
m2 <- glm(vs ~ am + gear + wt, data = mtcars, family = binomial)
m3 <- glm(vs ~ am + gear + wt + mpg, data = mtcars, family = binomial)
roc1 <- roc(m1$y, predict(m1, type = "response"))
roc2 <- roc(m2$y, predict(m2, type = "response"))
roc3 <- roc(m3$y, predict(m3, type = "response"))
list.roc <- list(roc1, roc2, roc3)
ROC_table(list.roc)

barServer: shiny module server for barplot.

Description

Shiny module server for barplot.

Usage

barServer(id, data, data_label, data_varStruct = NULL, nfactor.limit = 10)

Arguments

Details

Shiny module server for barplot.

Value

Shiny module server for barplot.

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      barUI("bar")
    ),
    mainPanel(
      optionUI("bar"),
      plotOutput("bar_plot"),
      ggplotdownUI("bar")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

barUI: shiny module UI for barplot

Description

Shiny module UI for barplot

Usage

barUI(id, label = "barplot")

Arguments

Details

Shiny module UI for barplot

Value

Shiny module UI for barplot

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      barUI("bar")
    ),
    mainPanel(
      optionUI("bar"),
      plotOutput("bar_plot"),
      ggplotdownUI("bar")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

boxServer: shiny module server for boxplot.

Description

Shiny module server for boxplot.

Usage

boxServer(id, data, data_label, data_varStruct = NULL, nfactor.limit = 10)

Arguments

Details

Shiny module server for boxplot.

Value

Shiny module server for boxplot.

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      boxUI("box")
    ),
    mainPanel(
      optionUI("box"),
      plotOutput("box_plot"),
      ggplotdownUI("box")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

boxUI: shiny module UI for boxplot

Description

Shiny module UI for boxplot

Usage

boxUI(id, label = "boxplot")

Arguments

Details

Shiny module UI for boxplot

Value

Shiny module UI for boxplot

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      boxUI("box")
    ),
    mainPanel(
      optionUI("box"),
      plotOutput("box_plot"),
      ggplotdownUI("box")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

coxModule: shiny modulde server for Cox's model.

Description

Shiny modulde server for Cox's model.

Usage

coxModule(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  default.unires = T,
  limit.unires = 20,
  id.cluster = NULL,
  ties.coxph = "efron",
  vec.event = NULL,
  vec.time = NULL
)

Arguments

Details

Shiny modulde server for Cox's model.

Value

Shiny modulde server for Cox's model.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      coxUI("cox")
    ),
    mainPanel(
      DTOutput("coxtable")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

  out_cox <- callModule(coxModule, "cox",
    data = data, data_label = data.label,
    data_varStruct = NULL
  )

  output$coxtable <- renderDT({
    datatable(out_cox()$table, rownames = T, caption = out_cox()$caption)
  })
}

coxUI: shiny modulde UI for Cox's model.

Description

Shiny modulde UI for Cox's model.

Usage

coxUI(id)

Arguments

Details

Shiny modulde UI for Cox's model.

Value

coxUI

Examples

coxUI(1)

csvFile: Shiny module Server for file upload.

Description

The server-side logic for the 'csvFileInput' module. It uses the 'DataManager' R6 class to handle all data processing.

Usage

csvFile(input, output, session, nfactor.limit = 20)

Arguments

Value

A reactive expression that returns a list with two elements: 'data' (the processed data.table) and 'label' (a data.table with variable label information).

csvFileInput: Shiny module UI for file upload.

Description

Shiny module UI for file upload supporting csv, xlsx, sav, sas7bdat, and dta formats. It provides UI outputs for various data manipulation options.

Usage

csvFileInput(id, label = "Upload data (csv/xlsx/sav/sas7bdat/dta)")

Arguments

Details

This function only defines the UI. The corresponding server function, 'csvFile', handles the logic.

Value

A Shiny UI object.

Examples

if (interactive()) {
  library(shiny)
  library(DT)
  library(jstable)

  ui <- fluidPage(
    sidebarLayout(
      sidebarPanel(
        csvFileInput("datafile")
      ),
      mainPanel(
        tabsetPanel(
          type = "pills",
          tabPanel("Data", DTOutput("data")),
          tabPanel("Label", DTOutput("data_label"))
        )
      )
    )
  )

  server <- function(input, output, session) {
    data_info <- callModule(csvFile, "datafile")

    output$data <- renderDT({
      data_info()$data
    })

    output$label <- renderDT({
      data_info()$label
    })
  }
  shinyApp(ui, server)
}

forestcoxServer:shiny module server for forestcox

Description

Shiny module server for forestcox

Usage

forestcoxServer(
  id,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  cluster_id = NULL,
  vec.event = NULL,
  vec.time = NULL
)

Arguments

Details

Shiny module server for forestcox

Value

Shiny module server for forestcox

See Also

data.table-package, setDT, setattr TableSubgroupMultiCox forest_theme, forest dml read_pptx, add_slide, ph_with, ph_location

Examples

library(shiny)
library(DT)
mtcars$vs <- factor(mtcars$vs)
mtcars$am <- factor(mtcars$am)
mtcars$kk <- factor(as.integer(mtcars$disp >= 150))
mtcars$kk1 <- factor(as.integer(mtcars$disp >= 200))

library(shiny)
library(DT)
mtcars$vs <- factor(mtcars$vs)
mtcars$am <- factor(mtcars$am)
mtcars$kk <- factor(as.integer(mtcars$disp >= 150))
mtcars$kk1 <- factor(as.integer(mtcars$disp >= 200))

out <- mtcars
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      forestcoxUI("Forest")
    ),
    mainPanel(
      tabsetPanel(
        type = "pills",
        tabPanel(
          title = "Data",
          DTOutput("tablesub"),
        ),
        tabPanel(
          title = "figure",
          plotOutput("forestplot", width = "100%"),
          ggplotdownUI("Forest")
        )
      )
    )
  )
)


server <- function(input, output, session) {
  data <- reactive(out)
  label <- reactive(jstable::mk.lev(out))
  outtable <- forestcoxServer("Forest", data = data, data_label = label)
  output$tablesub <- renderDT({
    outtable()[[1]]
  })
  output$forestplot <- renderPlot({
    a
    outtable()[[2]]
  })
}

forestcoxUI:shiny module UI for forestcox

Description

Shiny module UI for forestcox

Usage

forestcoxUI(id, label = "forestplot")

Arguments

Details

Shinymodule UI for forestcox

Value

Shinymodule UI

Examples

library(shiny)
library(DT)
mtcars$vs <- factor(mtcars$vs)
mtcars$am <- factor(mtcars$am)
mtcars$kk <- factor(as.integer(mtcars$disp >= 150))
mtcars$kk1 <- factor(as.integer(mtcars$disp >= 200))

library(shiny)
library(DT)
mtcars$vs <- factor(mtcars$vs)
mtcars$am <- factor(mtcars$am)
mtcars$kk <- factor(as.integer(mtcars$disp >= 150))
mtcars$kk1 <- factor(as.integer(mtcars$disp >= 200))

out <- mtcars
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      forestcoxUI("Forest")
    ),
    mainPanel(
      tabsetPanel(
        type = "pills",
        tabPanel(
          title = "Data",
          DTOutput("tablesub")
        ),
        tabPanel(
          title = "figure",
          plotOutput("forestplot", width = "100%"),
          ggplotdownUI("Forest")
        )
      )
    )
  )
)


server <- function(input, output, session) {
  data <- reactive(out)
  label <- reactive(jstable::mk.lev(out))
  outtable <- forestcoxServer("Forest", data = data, data_label = label)
  output$tablesub <- renderDT({
    outtable()[[1]]
  })
  output$forestplot <- renderPlot({
    outtable()[[2]]
  })
}

forestglmServer:shiny module server for forestglm

Description

Shiny module server for forestglm

Usage

forestglmServer(
  id,
  data,
  data_label,
  family,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  repeated_id = NULL
)

Arguments

Details

Shiny module server for forestglm

Value

Shiny module server for forestglm

See Also

TableSubgroupMultiGLM data.table-package,setDT, setattr cor, coef surveysummary, svytable forest_theme, forest dml read_pptx, add_slide, ph_with, ph_location

Examples

library(shiny)
library(DT)
mtcars$vs <- factor(mtcars$vs)
mtcars$am <- factor(mtcars$am)
mtcars$kk <- factor(as.integer(mtcars$disp >= 150))
mtcars$kk1 <- factor(as.integer(mtcars$disp >= 200))

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      forestglmUI("Forest")
    ),
    mainPanel(
      tabsetPanel(
        type = "pills",
        tabPanel(
          title = "Data",
          DTOutput("tablesub"),
        ),
        tabPanel(
          title = "figure",
          plotOutput("forestplot", width = "100%"),
          ggplotdownUI("Forest")
        )
      )
    )
  )
)

out <- mtcars

server <- function(input, output, session) {
  data <- reactive(out)
  label <- reactive(jstable::mk.lev(out))
  outtable <- forestglmServer("Forest", data = data, data_label = label, family = "binomial")
  output$tablesub <- renderDT({
    outtable()[[1]]
  })
  output$forestplot <- renderPlot({
    outtable()[[2]]
  })
}

forestglmUI:Shiny module UI for forestglm

Description

Shiny module UI for forestcox

Usage

forestglmUI(id, label = "forestplot")

Arguments

Details

Shinymodule UI for forestglm

Value

Shinymodule UI

Examples

library(shiny)
library(DT)
mtcars$vs <- factor(mtcars$vs)
mtcars$am <- factor(mtcars$am)
mtcars$kk <- factor(as.integer(mtcars$disp >= 150))
mtcars$kk1 <- factor(as.integer(mtcars$disp >= 200))

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      forestglmUI("Forest")
    ),
    mainPanel(
      tabsetPanel(
        type = "pills",
        tabPanel(
          title = "Data",
          DTOutput("tablesub"),
        ),
        tabPanel(
          title = "figure",
          plotOutput("forestplot", width = "100%"),
          ggplotdownUI("Forest")
        )
      )
    )
  )
)

out <- mtcars

server <- function(input, output, session) {
  data <- reactive(out)
  label <- reactive(jstable::mk.lev(out))
  outtable <- forestglmServer("Forest", data = data, data_label = label, family = "binomial")
  output$tablesub <- renderDT({
    outtable()[[1]]
  })
  output$forestplot <- renderPlot({
    outtable()[[2]]
  })
}

ggpairsModule: shiny module server for basic/scatter plot.

Description

Shiny module server for basic/scatter plot.

Usage

ggpairsModule(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 20
)

Arguments

Details

Shiny module server for basic/scatter plot.

Value

Shiny module server for basic/scatter plot.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(GGally)

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      ggpairsModuleUI1("ggpairs")
    ),
    mainPanel(
      plotOutput("ggpairs_plot"),
      ggpairsModuleUI2("ggpairs")
    )
  )
)

server <- function(input, output, session) {
  data <- mtcars
  data.label <- jstable::mk.lev(mtcars)

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

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

ggpairsModule2: shiny module server for basic/scatter plot for reactive data.

Description

Shiny module server for basic/scatter plot for reactive data.

Usage

ggpairsModule2(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 20
)

Arguments

Details

Shiny module server for basic/scatter plot for reactive data.

Value

Shiny module server for basic/scatter plot

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(GGally)

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      ggpairsModuleUI1("ggpairs")
    ),
    mainPanel(
      plotOutput("ggpairs_plot"),
      ggpairsModuleUI2("ggpairs")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

ggpairsModuleUI1: Variable selection module UI for ggpairs

Description

Variable selection module UI for ggpairs

Usage

ggpairsModuleUI1(id)

Arguments

Details

Variable selection module UI for ggpairs

Value

Variable selection module UI for ggpairs

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(GGally)

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      ggpairsModuleUI1("ggpairs")
    ),
    mainPanel(
      plotOutput("ggpairs_plot"),
      ggpairsModuleUI2("ggpairs")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

ggpairsModuleUI2: Option & download module UI for ggpairs

Description

Option & download module UI for ggpairs

Usage

ggpairsModuleUI2(id)

Arguments

Details

Option & download module UI for ggpairs

Value

Option & download module UI for ggpairs

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(GGally)

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      ggpairsModuleUI1("ggpairs")
    ),
    mainPanel(
      plotOutput("ggpairs_plot"),
      ggpairsModuleUI2("ggpairs")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

ggplotdownUI: Option & download module UI for ggplot

Description

Option & download module UI for ggplot

Usage

ggplotdownUI(id)

Arguments

Details

Option & download module UI for ggplot

Value

Option & download module UI for ggplot

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      kaplanUI("kaplan")
    ),
    mainPanel(
      plotOutput("kaplan_plot"),
      ggplotdownUI("kaplan")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

histogramServer: shiny module server for histogram.

Description

Shiny module server for histogram.

Usage

histogramServer(
  id,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10
)

Arguments

Details

Shiny module server for histogram.

Value

Shiny module server for histogram.

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      histogramUI("histogram")
    ),
    mainPanel(
      plotOutput("histogram"),
      ggplotdownUI("histogram")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

histogramUI: shiny module UI for histogram

Description

Shiny module UI for histogram

Usage

histogramUI(id, label = "histogram")

Arguments

Details

Shiny module UI for histogram

Value

Shiny module UI for histogram

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      histogramUI("histogram")
    ),
    mainPanel(
      plotOutput("histogram"),
      ggplotdownUI("histogram")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

jsBasicAddin: Rstudio addin of jsBasicGadget

Description

Rstudio addin of jsBasicGadget

Usage

jsBasicAddin()

Details

Rstudio addin of jsBasicGadget

Value

Rstudio addin of jsBasicGadget

See Also

rstudio-editors

Examples

if (interactive()) {
  jsBasicAddin()
}

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.

Usage

jsBasicExtAddin(nfactor.limit = 20, max.filesize = 2048)

Arguments

Details

RStudio Addin for basic data analysis with external csv/xlsx/sas7bdat/sav/dta file.

Value

RStudio Addin for basic data analysis with external data.

See Also

lung fwrite opt.tbreg

Examples

if (interactive()) {
  jsBasicExtAddin()
}

jsBasicGadget: Shiny Gadget of Basic Statistics in Medical Research.

Description

Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plot

Usage

jsBasicGadget(data, nfactor.limit = 20)

Arguments

Details

Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plot

Value

Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plot

Examples

if (interactive()) {
  jsBasicGadget(mtcars)
}

jsPropensityAddin: Rstudio addin of jsPropensityGadget

Description

Rstudio addin of jsPropensityGadget

Usage

jsPropensityAddin()

Details

Rstudio addin of jsPropensityGadget

Value

Rstudio addin of jsPropensityGadget

See Also

rstudio-editors

Examples

if (interactive()) {
  jsPropensityAddin()
}

jsPropensityExtAddin: RStudio Addin for propensity score analysis with external data.

Description

RStudio Addin for propensity score analysis with external csv/xlsx/sas7bdat/sav/dta file.

Usage

jsPropensityExtAddin(nfactor.limit = 20, max.filesize = 2048)

Arguments

Details

RStudio Addin for propensity score analysis with external csv/xlsx/sas7bdat/sav/dta file.

Value

RStudio Addin for propensity score analysis with external data.

See Also

pbc fwrite,data.table svydesign opt.tbreg

Examples

if (interactive()) {
  jsPropensityExtAddin()
}

jsPropensityGadget: Shiny Gadget for propensity score analysis.

Description

Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.

Usage

jsPropensityGadget(data, nfactor.limit = 20)

Arguments

Details

Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.

Value

Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.

See Also

data.table matchit,match.data cox2.display,svycox.display survfit,coxph,Surv jskm,svyjskm ggsave svykm

Examples

if (interactive()) {
  jsPropensityGadget(mtcars)
}

jsRepeatedAddin: Rstudio addin of jsRepeatedGadget

Description

Rstudio addin of jsRepeatedGadget

Usage

jsRepeatedAddin()

Details

Rstudio addin of jsRepeatedGadget

Value

Rstudio addin of jsRepeatedGadget

See Also

rstudio-editors

Examples

if (interactive()) {
  jsRepeatedAddin()
}

jsRepeatedExtAddin: RStudio Addin for repeated measure analysis with external data.

Description

RStudio Addin for repeated measure analysis with external csv/xlsx/sas7bdat/sav/dta file.

Usage

jsRepeatedExtAddin(nfactor.limit = 20, max.filesize = 2048)

Arguments

Details

RStudio Addin for repeated measure analysis with external csv/xlsx/sas7bdat/sav/dta file.

Value

RStudio Addin for repeated measure analysis with external data.

See Also

fwrite colon opt.tbreg

Examples

if (interactive()) {
  jsRepeatedExtAddin()
}

jsRepeatedGadget: Shiny Gadget of Repeated measure analysis.

Description

Shiny Gadget including Data, Label info, Table 1, GEE(linear, logistic), Basic plot

Usage

jsRepeatedGadget(data, nfactor.limit = 20)

Arguments

Details

Shiny Gadget including Data, Label info, Table 1, GEE(linear, logistic), Basic plot

Value

Shiny Gadget including Data, Label info, Table 1, GEE(linear, logistic), Basic plot

Examples

if (interactive()) {
  jsRepeatedGadget(mtcars)
}

jsSurveyAddin: Rstudio addin of jsSurveyGadget

Description

Rstudio addin of jsSurveyGadget

Usage

jsSurveyAddin()

Details

Rstudio addin of jsSurveyGadget

Value

Rstudio addin of jsSurveyGadget

See Also

rstudio-editors

Examples

if (interactive()) {
  jsSurveydAddin()
}

jsSurveyExtAddin: RStudio Addin for survey data analysis with external data.

Description

RStudio Addin for survey data analysis with external csv/xlsx/sas7bdat/sav/dta file.

Usage

jsSurveyExtAddin(nfactor.limit = 20, max.filesize = 2048)

Arguments

Details

RStudio Addin for survey data analysis with external csv/xlsx/sas7bdat/sav/dta file.

Value

RStudio Addin for survey data analysis with external data.

See Also

fwrite opt.tb1,opt.tbreg

Examples

if (interactive()) {
  jsSurveyExtAddin()
}

jsSurveyGadget: Shiny Gadget of survey data analysis.

Description

Shiny Gadget including Data, Label info, Table 1, svyglm, Basic plot

Usage

jsSurveyGadget(data, nfactor.limit = 20)

Arguments

Details

Shiny Gadget including Data, Label info, Table 1, svyglm, Basic plot

Value

Shiny Gadget including Data, Label info, Table 1, svyglm, Basic plot

Examples

if (interactive()) {
  jsSurveyGadget(mtcars)
}

kaplanModule: shiny module server for kaplan-meier plot.

Description

Shiny module server for kaplan-meier plot.

Usage

kaplanModule(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  id.cluster = NULL,
  timeby = NULL,
  range.x = NULL,
  range.y = NULL,
  vec.event = NULL,
  vec.time = NULL
)

Arguments

Details

Shiny module server for kaplan-meier plot.

Value

Shiny module server for kaplan-meier plot.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      kaplanUI("kaplan")
    ),
    mainPanel(
      plotOutput("kaplan_plot"),
      ggplotdownUI("kaplan")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

kaplanUI: shiny module UI for kaplan-meier plot

Description

Shiny module UI for kaplan-meier plot

Usage

kaplanUI(id)

Arguments

Details

Shiny module UI for kaplan-meier plot

Value

Shiny module UI for kaplan-meier plot

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      kaplanUI("kaplan")
    ),
    mainPanel(
      plotOutput("kaplan_plot"),
      ggplotdownUI("kaplan")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

lineServer: shiny module server for lineplot.

Description

Shiny module server for lineplot.

Usage

lineServer(id, data, data_label, data_varStruct = NULL, nfactor.limit = 10)

Arguments

Details

Shiny module server for lineplot.

Value

Shiny module server for lineplot.

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      lineUI("line")
    ),
    mainPanel(
      optionUI("line"),
      plotOutput("line_plot"),
      ggplotdownUI("line")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

lineUI: shiny module UI for lineplot

Description

Shiny module UI for lineplot

Usage

lineUI(id, label = "lineplot")

Arguments

Details

Shiny module UI for lineplot

Value

Shiny module UI for lineplot

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      lineUI("line")
    ),
    mainPanel(
      optionUI("line"),
      plotOutput("line_plot"),
      ggplotdownUI("line")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

logistic.display2: Modified epiDisplay's logistic.display function.

Description

Modified epiDisplay's logistic.display function for reactive data.

Usage

logistic.display2(
  logistic.model,
  alpha = 0.05,
  crude = TRUE,
  crude.p.value = FALSE,
  decimal = 2,
  simplified = FALSE
)

Arguments

Details

Modified epiDisplay's logistic.display function for reactive data.

Value

logistic table

Examples

model1 <- glm(am ~ cyl + disp, data = mtcars, family = binomial)
logistic.display2(model1, crude = TRUE, crude.p.value = TRUE, decimal = 3)

logisticModule2: Shiny modulde server for logistic regression for reactive data.

Description

Shiny modulde server for logistic regression for reactive data.

Usage

logisticModule2(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  default.unires = T,
  limit.unires = 20,
  vec.event = NULL
)

Arguments

Details

Shiny modulde server for logistic regression.

Value

Shiny modulde server for logistic regression.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      regressModuleUI("logistic")
    ),
    mainPanel(
      DTOutput("logistictable")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

  output$logistictable <- renderDT({
    datatable(out_logistic()$table, rownames = T, caption = out_logistic()$caption)
  })
}

mk.lev2: level generating function

Description

make level for sav files with labels pre defined from SPSS

Usage

mk.lev2(out.old, out.label)

Arguments

Value

out.label data labels updated

mklist: function to make variable list lncluding specific variables.

Description

Function to make variable list lncluding specific variables.

Usage

mklist(varlist, vars)

Arguments

Details

Internal function

Value

variable list lncluding specific variables.

Examples

data_varStruct <- list(variable = names(mtcars))
mklist(data_varStruct, names(mtcars))

mksetdiff: function to make variable list excluding specific variables.

Description

Function to make variable list excluding specific variables.

Usage

mksetdiff(varlist, vars)

Arguments

Details

Internal function

Value

variable list excluding specific variables.

Examples

data_varStruct <- list(variable = names(mtcars))
mksetdiff(data_varStruct, "mpg")

optionUI: Option UI with icon

Description

Option UI with icon

Usage

optionUI(id)

Arguments

Details

Option UI with icon

Value

Option UI with icon

See Also

dropdownButton,tooltipOptions

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      kaplanUI("kaplan")
    ),
    mainPanel(
      optionUI("kaplan"),
      plotOutput("kaplan_plot"),
      ggplotdownUI("kaplan")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

reclassificationJS: Function for reclassification table and statistics

Description

Modified function of PredictABEL::reclassification: return output table

Usage

reclassificationJS(
  data,
  cOutcome,
  predrisk1,
  predrisk2,
  cutoff,
  dec.value = 3,
  dec.p = 3
)

Arguments

Details

Modified function of PredictABEL::reclassification

Value

Table including NRI(categorical), NRI(continuous), IDI with 95

See Also

rcorrp.cens

Examples

m1 <- glm(vs ~ am + gear, data = mtcars, family = binomial)
m2 <- glm(vs ~ am + gear + wt, data = mtcars, family = binomial)
reclassificationJS(
  data = mtcars, cOutcome = 8,
  predrisk1 = predict(m1, type = "response"),
  predrisk2 = predict(m2, type = "response"), cutoff = c(0, .20, .40, 1)
)

regress.display2: modified epiDisplay's regress.display function

Description

regress.display function for reactive data

Usage

regress.display2(
  regress.model,
  alpha = 0.05,
  crude = FALSE,
  crude.p.value = FALSE,
  decimal = 2,
  simplified = FALSE
)

Arguments

Details

regress.display function for reactive data

Value

regress table

Examples

model1 <- glm(mpg ~ cyl + disp + vs, data = mtcars)
regress.display2(model1, crude = TRUE, crude.p.value = TRUE, decimal = 3)

regressModule2: Shiny modulde server for linear regression for reactive data.

Description

Shiny modulde server for linear regression for reactive data.

Usage

regressModule2(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  default.unires = T,
  limit.unires = 20,
  vec.event = NULL
)

Arguments

Details

Shiny modulde server for linear regression.

Value

Shiny modulde server for linear regression.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      regressModuleUI("linear")
    ),
    mainPanel(
      DTOutput("lineartable")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

  output$lineartable <- renderDT({
    datatable(out_linear()$table, rownames = T, caption = out_linear()$caption)
  })
}

regressModuleUI: shiny modulde UI for linear regression.

Description

Shiny modulde UI for linear regression.

Usage

regressModuleUI(id)

Arguments

Details

Shiny modulde UI for linear regression.

Value

Shiny modulde UI for linear regression.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      regressModuleUI("linear")
    ),
    mainPanel(
      DTOutput("lineartable")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

  output$lineartable <- renderDT({
    datatable(out_linear()$table, rownames = T, caption = out_linear()$caption)
  })
}

rocModule: shiny module server for roc analysis

Description

shiny module server for roc analysis

Usage

rocModule(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  id.cluster = NULL
)

Arguments

Details

shiny module server for roc analysis

Value

shiny module server for roc analysis

See Also

quantile setkey ggroc geeglm svyglm theme_modern

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(pROC)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      rocUI("roc")
    ),
    mainPanel(
      plotOutput("plot_roc"),
      tableOutput("cut_roc"),
      ggplotdownUI("roc"),
      DTOutput("table_roc")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(data1))

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

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

  output$cut_roc <- renderTable({
    print(out_roc()$cut)
  })

  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))
    )
  })
}

rocModule2: shiny module server for roc analysis- input number of model as integer

Description

shiny module server for roc analysis- input number of model as integer

Usage

rocModule2(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  id.cluster = NULL
)

Arguments

Details

shiny module server for roc analysis- input number of model as integer

Value

shiny module server for roc analysis- input number of model as integer

See Also

quantile setkey ggroc geeglm svyglm theme_modern

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(pROC)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      rocUI("roc")
    ),
    mainPanel(
      plotOutput("plot_roc"),
      tableOutput("cut_roc"),
      ggplotdownUI("roc"),
      DTOutput("table_roc")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(data1))

  out_roc <- callModule(rocModule2, "roc",
    data = data, data_label = data.label,
    data_varStruct = NULL
  )

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

  output$cut_roc <- renderTable({
    print(out_roc()$cut)
  })

  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))
    )
  })
}

rocUI: shiny module UI for roc analysis

Description

Shiny module UI for roc analysis

Usage

rocUI(id)

Arguments

Details

Shiny module UI for roc analysis

Value

Shiny module UI for roc analysis

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(pROC)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      rocUI("roc")
    ),
    mainPanel(
      plotOutput("plot_roc"),
      tableOutput("cut_roc"),
      ggplotdownUI("roc"),
      DTOutput("table_roc")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(data1))

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

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

  output$cut_roc <- renderTable({
    print(out_roc()$cut)
  })

  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))
    )
  })
}

scatterServer: shiny module server for scatterplot.

Description

Shiny module server for scatterplot.

Usage

scatterServer(id, data, data_label, data_varStruct = NULL, nfactor.limit = 10)

Arguments

Details

Shiny module server for scatterplot.

Value

Shiny module server for scatterplot.

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      scatterUI("scatter")
    ),
    mainPanel(
      plotOutput("scatter_plot"),
      ggplotdownUI("scatter")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

scatterUI: shiny module UI for scatterplot

Description

Shiny module UI for scatterplot

Usage

scatterUI(id, label = "scatterplot")

Arguments

Details

Shiny module UI for scatterplot

Value

Shiny module UI for scatterplot

Examples

library(shiny)
library(ggplot2)
library(ggpubr)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      scatterUI("scatter")
    ),
    mainPanel(
      plotOutput("scatter_plot"),
      ggplotdownUI("scatter")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

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

survIDINRI_helper: Helper function for IDI.INF.OUT in survIDINRI packages

Description

Helper function for IDI.INF.OUT in survIDINRI packages

Usage

survIDINRI_helper(
  var.event,
  var.time,
  list.vars.ind,
  t,
  data,
  dec.auc = 3,
  dec.p = 3,
  id.cluster = NULL
)

Arguments

Details

Helper function for IDI.INF.OUT in survIDINRI packages

Value

IDI, NRI

See Also

data.table model.matrix coxph Surv IDI.INF.OUT IDI.INF

Examples

# library(survival)
# survIDINRI_helper("status", "time", list.vars.ind = list("age", c("age", "sex")),
#                  t = 365, data = lung)

tb1module: table 1 shiny module server.

Description

Table 1 shiny module server for descriptive statistics.

Usage

tb1module(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  showAllLevels = T,
  argsExact = list(workspace = 2 * 10^7, simulate.p.value = T)
)

Arguments

Details

Table 1 shiny module server for descriptive statistics.

Value

Table 1 shiny module server for descriptive statistics.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      tb1moduleUI("tb1")
    ),
    mainPanel(
      DTOutput("table1")
    )
  )
)

server <- function(input, output, session) {
  data <- mtcars
  data.label <- jstable::mk.lev(mtcars)

  out_tb1 <- callModule(tb1module, "tb1",
    data = data, data_label = data.label,
    data_varStruct = NULL
  )

  output$table1 <- renderDT({
    tb <- out_tb1()$table
    cap <- out_tb1()$caption
    out.tb1 <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out.tb1)
  })
}

tb1module2: table 1 shiny module server for reactive data.

Description

Table 1 shiny module server for descriptive statistics for reactive data.

Usage

tb1module2(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  showAllLevels = T,
  argsExact = list(workspace = 2 * 10^7, simulate.p.value = T)
)

Arguments

Details

Table 1 shiny module server for descriptive statistics.

Value

Table 1 shiny module server for descriptive statistics.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      tb1moduleUI("tb1")
    ),
    mainPanel(
      DTOutput("table1")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

  output$table1 <- renderDT({
    tb <- out_tb1()$table
    cap <- out_tb1()$caption
    out.tb1 <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out.tb1)
  })
}

tb1moduleUI: table 1 module UI.

Description

Table 1 shiny module UI for descriptive statistics.

Usage

tb1moduleUI(id)

Arguments

Details

Table 1 shiny module UI for descriptive statistics.

Value

Table 1 module UI.

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      tb1moduleUI("tb1")
    ),
    mainPanel(
      DTOutput("table1")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- reactive(jstable::mk.lev(mtcars))

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

  output$table1 <- renderDT({
    tb <- out_tb1()$table
    cap <- out_tb1()$caption
    out.tb1 <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out.tb1)
  })
}

tb1simple: tb1 module server for propensity score analysis

Description

Table 1 module server for propensity score analysis

Usage

tb1simple(
  input,
  output,
  session,
  data,
  matdata,
  data_label,
  data_varStruct = NULL,
  group_var,
  showAllLevels = T
)

Arguments

Details

Table 1 module server for propensity score analysis

Value

Table 1 with original data/matching data/IPTW data

See Also

var_label CreateTableOneJS svydesign

Examples

library(shiny)
library(DT)
library(data.table)
library(readxl)
library(jstable)
library(haven)
library(survey)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      FilePsInput("datafile"),
      tb1simpleUI("tb1")
    ),
    mainPanel(
      DTOutput("table1_original"),
      DTOutput("table1_ps"),
      DTOutput("table1_iptw")
    )
  )
)

server <- function(input, output, session) {
  mat.info <- callModule(FilePs, "datafile")

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


  vlist <- eventReactive(mat.info(), {
    mklist <- function(varlist, vars) {
      lapply(
        varlist,
        function(x) {
          inter <- intersect(x, vars)
          if (length(inter) == 1) {
            inter <- c(inter, "")
          }
          return(inter)
        }
      )
    }
    factor_vars <- names(data())[data()[, lapply(.SD, class) %in% c("factor", "character")]]
    factor_list <- mklist(data_varStruct(), factor_vars)
    conti_vars <- setdiff(names(data()), c(factor_vars, "pscore", "iptw"))
    conti_list <- mklist(data_varStruct(), conti_vars)
    nclass_factor <- unlist(data()[, lapply(.SD, function(x) {
      length(unique(x)[!is.na(unique(x))])
    }),
    .SDcols = factor_vars
    ])
    class01_factor <- unlist(data()[, lapply(.SD, function(x) {
      identical(levels(x), c("0", "1"))
    }),
    .SDcols = factor_vars
    ])
    validate(
      need(!is.null(class01_factor), "No categorical variables coded as 0, 1 in data")
    )
    factor_01vars <- factor_vars[class01_factor]
    factor_01_list <- mklist(data_varStruct(), factor_01vars)
    group_vars <- factor_vars[nclass_factor >= 2 & nclass_factor <= 10 &
      nclass_factor < nrow(data())]
    group_list <- mklist(data_varStruct(), group_vars)
    except_vars <- factor_vars[nclass_factor > 10 | nclass_factor == 1 |
      nclass_factor == nrow(data())]

    ## non-normal: shapiro test
    f <- function(x) {
      if (diff(range(x, na.rm = T)) == 0) {
        return(F)
      } else {
        return(shapiro.test(x)$p.value <= 0.05)
      }
    }

    non_normal <- ifelse(nrow(data()) <= 3 | nrow(data()) >= 5000,
      rep(F, length(conti_vars)),
      sapply(conti_vars, function(x) {
        f(data()[[x]])
      })
    )
    return(list(
      factor_vars = factor_vars, factor_list = factor_list, conti_vars = conti_vars,
      conti_list = conti_list, factor_01vars = factor_01vars,
      factor_01_list = factor_01_list, group_list = group_list,
      except_vars = except_vars, non_normal = non_normal
    ))
  })

  out.tb1 <- callModule(tb1simple2, "tb1",
    data = data, matdata = matdata, data_label = data.label,
    data_varStruct = NULL, vlist = vlist,
    group_var = reactive(mat.info()$group_var)
  )

  output$table1_original <- renderDT({
    tb <- out.tb1()$original$table
    cap <- out.tb1()$original$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })

  output$table1_ps <- renderDT({
    tb <- out.tb1()$ps$table
    cap <- out.tb1()$ps$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })

  output$table1_iptw <- renderDT({
    tb <- out.tb1()$iptw$table
    cap <- out.tb1()$iptw$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })
}

tb1simple2: tb1 module for propensity score analysis for reactive data

Description

tb1 module for propensity score analysis for reactive data

Usage

tb1simple2(
  input,
  output,
  session,
  data,
  matdata,
  data_label,
  data_varStruct = NULL,
  vlist,
  group_var,
  showAllLevels = T
)

Arguments

Details

Table 1 module server for propensity score analysis

Value

Table 1 with original data/matching data/IPTW data

See Also

CreateTableOneJS svydesign

Examples

library(shiny)
library(DT)
library(data.table)
library(readxl)
library(jstable)
library(haven)
library(survey)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      FilePsInput("datafile"),
      tb1simpleUI("tb1")
    ),
    mainPanel(
      DTOutput("table1_original"),
      DTOutput("table1_ps"),
      DTOutput("table1_iptw")
    )
  )
)

server <- function(input, output, session) {
  mat.info <- callModule(FilePs, "datafile")

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


  vlist <- eventReactive(mat.info(), {
    mklist <- function(varlist, vars) {
      lapply(
        varlist,
        function(x) {
          inter <- intersect(x, vars)
          if (length(inter) == 1) {
            inter <- c(inter, "")
          }
          return(inter)
        }
      )
    }
    factor_vars <- names(data())[data()[, lapply(.SD, class) %in% c("factor", "character")]]
    factor_list <- mklist(data_varStruct(), factor_vars)
    conti_vars <- setdiff(names(data()), c(factor_vars, "pscore", "iptw"))
    conti_list <- mklist(data_varStruct(), conti_vars)
    nclass_factor <- unlist(data()[, lapply(.SD, function(x) {
      length(unique(x)[!is.na(unique(x))])
    }),
    .SDcols = factor_vars
    ])
    class01_factor <- unlist(data()[, lapply(.SD, function(x) {
      identical(levels(x), c("0", "1"))
    }),
    .SDcols = factor_vars
    ])
    validate(
      need(!is.null(class01_factor), "No categorical variables coded as 0, 1 in data")
    )
    factor_01vars <- factor_vars[class01_factor]
    factor_01_list <- mklist(data_varStruct(), factor_01vars)
    group_vars <- factor_vars[nclass_factor >= 2 & nclass_factor <= 10 &
      nclass_factor < nrow(data())]
    group_list <- mklist(data_varStruct(), group_vars)
    except_vars <- factor_vars[nclass_factor > 10 | nclass_factor == 1 |
      nclass_factor == nrow(data())]

    ## non-normal: shapiro test
    f <- function(x) {
      if (diff(range(x, na.rm = T)) == 0) {
        return(F)
      } else {
        return(shapiro.test(x)$p.value <= 0.05)
      }
    }

    non_normal <- ifelse(nrow(data()) <= 3 | nrow(data()) >= 5000,
      rep(F, length(conti_vars)),
      sapply(conti_vars, function(x) {
        f(data()[[x]])
      })
    )
    return(list(
      factor_vars = factor_vars, factor_list = factor_list, conti_vars = conti_vars,
      conti_list = conti_list, factor_01vars = factor_01vars,
      factor_01_list = factor_01_list, group_list = group_list,
      except_vars = except_vars, non_normal = non_normal
    ))
  })

  out.tb1 <- callModule(tb1simple2, "tb1",
    data = data, matdata = matdata, data_label = data.label,
    data_varStruct = NULL, vlist = vlist,
    group_var = reactive(mat.info()$group_var)
  )

  output$table1_original <- renderDT({
    tb <- out.tb1()$original$table
    cap <- out.tb1()$original$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })

  output$table1_ps <- renderDT({
    tb <- out.tb1()$ps$table
    cap <- out.tb1()$ps$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })

  output$table1_iptw <- renderDT({
    tb <- out.tb1()$iptw$table
    cap <- out.tb1()$iptw$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })
}

tb1simpleUI : tb1 module UI for propensity score analysis

Description

Table 1 module UI for propensity score analysis.

Usage

tb1simpleUI(id)

Arguments

Details

tb1 module UI for propensity score analysis

Value

Table 1 UI for propensity score analysis

Examples

library(shiny)
library(DT)
library(data.table)
library(readxl)
library(jstable)
library(haven)
library(survey)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      FilePsInput("datafile"),
      tb1simpleUI("tb1")
    ),
    mainPanel(
      DTOutput("table1_original"),
      DTOutput("table1_ps"),
      DTOutput("table1_iptw")
    )
  )
)

server <- function(input, output, session) {
  mat.info <- callModule(FilePs, "datafile")

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


  vlist <- eventReactive(mat.info(), {
    mklist <- function(varlist, vars) {
      lapply(
        varlist,
        function(x) {
          inter <- intersect(x, vars)
          if (length(inter) == 1) {
            inter <- c(inter, "")
          }
          return(inter)
        }
      )
    }
    factor_vars <- names(data())[data()[, lapply(.SD, class) %in% c("factor", "character")]]
    factor_list <- mklist(data_varStruct(), factor_vars)
    conti_vars <- setdiff(names(data()), c(factor_vars, "pscore", "iptw"))
    conti_list <- mklist(data_varStruct(), conti_vars)
    nclass_factor <- unlist(data()[, lapply(.SD, function(x) {
      length(unique(x)[!is.na(unique(x))])
    }),
    .SDcols = factor_vars
    ])
    class01_factor <- unlist(data()[, lapply(.SD, function(x) {
      identical(levels(x), c("0", "1"))
    }),
    .SDcols = factor_vars
    ])
    validate(
      need(!is.null(class01_factor), "No categorical variables coded as 0, 1 in data")
    )
    factor_01vars <- factor_vars[class01_factor]
    factor_01_list <- mklist(data_varStruct(), factor_01vars)
    group_vars <- factor_vars[nclass_factor >= 2 & nclass_factor <= 10 &
      nclass_factor < nrow(data())]
    group_list <- mklist(data_varStruct(), group_vars)
    except_vars <- factor_vars[nclass_factor > 10 | nclass_factor == 1 |
      nclass_factor == nrow(data())]

    ## non-normal: shapiro test
    f <- function(x) {
      if (diff(range(x, na.rm = T)) == 0) {
        return(F)
      } else {
        return(shapiro.test(x)$p.value <= 0.05)
      }
    }

    non_normal <- ifelse(nrow(data()) <= 3 | nrow(data()) >= 5000,
      rep(F, length(conti_vars)),
      sapply(conti_vars, function(x) {
        f(data()[[x]])
      })
    )
    return(list(
      factor_vars = factor_vars, factor_list = factor_list,
      conti_vars = conti_vars, conti_list = conti_list, factor_01vars = factor_01vars,
      factor_01_list = factor_01_list, group_list = group_list,
      except_vars = except_vars, non_normal = non_normal
    ))
  })

  out.tb1 <- callModule(tb1simple2, "tb1",
    data = data, matdata = matdata, data_label = data.label,
    data_varStruct = NULL, vlist = vlist,
    group_var = reactive(mat.info()$group_var)
  )

  output$table1_original <- renderDT({
    tb <- out.tb1()$original$table
    cap <- out.tb1()$original$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })

  output$table1_ps <- renderDT({
    tb <- out.tb1()$ps$table
    cap <- out.tb1()$ps$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })

  output$table1_iptw <- renderDT({
    tb <- out.tb1()$iptw$table
    cap <- out.tb1()$iptw$caption
    out <- datatable(tb, rownames = T, extension = "Buttons", caption = cap)
    return(out)
  })
}

templateGenerator: Shiny Gadget for global/app.R template.

Description

Opens a Shiny app that allows users to generate a Shiny project template.

Usage

templateGenerator()

Details

Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plots

Value

Shiny Gadget including Data, Label info, Table 1, Regression(linear, logistic), Basic plots

Examples

if (interactive()) {
  templateGenerator()
}

timeROC_table: extract AUC information from list of timeROChelper object.

Description

extract AUC information from list of timeROChelper object.

Usage

timeROC_table(ListModel, dec.auc = 3, dec.p = 3)

Arguments

Details

extract AUC information from list of timeROChelper object.

Value

table of AUC information

See Also

confint data.table

Examples

# library(survival)
# list.timeROC <- lapply(list("age", c("age", "sex")),
#                      function(x){
#                        timeROChelper("status", "time", x, t = 365, data = lung)
#                       })
# timeROC_table(list.timeROC)

timeROChelper: Helper function for timerocModule

Description

Helper function for timerocModule

Usage

timeROChelper(
  var.event,
  var.time,
  vars.ind,
  t,
  data,
  design.survey = NULL,
  id.cluster = NULL
)

Arguments

Details

Helper function for timerocModule

Value

timeROC and coxph object

See Also

coxph svycoxph predict timeROC

Examples

# library(survival)
# timeROChelper("status", "time", c("age", "sex"), t = 365, data = lung)

timerocModule: shiny module server for time-dependent roc analysis

Description

shiny module server for time-dependent roc analysis

shiny module server for time-dependent roc analysis- input number of model as integer

Usage

timerocModule(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  id.cluster = NULL,
  iid = TRUE,
  NRIIDI = TRUE
)

timerocModule2(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  nfactor.limit = 10,
  design.survey = NULL,
  id.cluster = NULL,
  iid = T,
  NRIIDI = T
)

Arguments

Details

shiny module server for time-dependent roc analysis

shiny module server for time dependent roc analysis- input number of model as integer

Value

shiny module server for time-dependent roc analysis

shiny module server for time dependent roc analysis- input number of model as integer

See Also

quantile setkey data.table rbindlist

quantile setkey data.table rbindlist

Examples

  library(shiny)
  library(DT)
  library(data.table)
  library(jstable)
  library(ggplot2)
  library(timeROC)
  library(survIDINRI)

  ui <- fluidPage(sidebarLayout(
    sidebarPanel(timerocUI("timeroc")),
    mainPanel(
      plotOutput("plot_timeroc"),
      ggplotdownUI("timeroc"),
      DTOutput("table_timeroc")
    )
  ))


  server <- function(input, output, session) {
    data <- reactive({
      dt_data <- as.data.table(pbc)

      factor_vars <- names(dt_data)[sapply(dt_data, function(x){length(table(x))}) <= 6]
      dt_data[, (factor_vars) := lapply(.SD, factor), .SDcols = factor_vars]

      return(dt_data)
    })

    data.label <- reactive({
      jstable::mk.lev(data())
    })

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

    observe({
      tb <- tryCatch(out_timeroc()$tb, error = function(e) NULL)
      print(tb)
    })

    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))
      )
    })


  }


  library(shiny)
  library(DT)
  library(data.table)
  library(jstable)
  library(ggplot2)
  library(timeROC)
  library(survIDINRI)

  ui <- fluidPage(sidebarLayout(
    sidebarPanel(timerocUI("timeroc")),
    mainPanel(
      plotOutput("plot_timeroc"),
      ggplotdownUI("timeroc"),
      DTOutput("table_timeroc")
    )
  ))


  server <- function(input, output, session) {
    data <- reactive({
      dt_data <- as.data.table(pbc)

      factor_vars <- names(dt_data)[sapply(dt_data, function(x){length(table(x))}) <= 6]
      dt_data[, (factor_vars) := lapply(.SD, factor), .SDcols = factor_vars]

      return(dt_data)
    })

    data.label <- reactive({
      jstable::mk.lev(data())
    })

    out_timeroc <- callModule(
      timerocModule2,
      "timeroc",
      data = data,
      data_label = data.label,
      data_varStruct = NULL
    )

    observe({
      tb <- tryCatch(out_timeroc()$tb, error = function(e) NULL)
      print(tb)
    })

    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))
      )
    })


  }

timerocUI: shiny module UI for time-dependent roc analysis

Description

Shiny module UI for time-dependent roc analysis

Usage

timerocUI(id)

Arguments

Details

Shiny module UI for time-dependent roc analysis

Value

Shiny module UI for time-dependent roc analysis

Examples

library(shiny)
library(DT)
library(data.table)
library(jstable)
library(ggplot2)
library(timeROC)
library(survIDINRI)
ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      timerocUI("timeroc")
    ),
    mainPanel(
      plotOutput("plot_timeroc"),
      ggplotdownUI("timeroc"),
      DTOutput("table_timeroc")
    )
  )
)

server <- function(input, output, session) {
  data <- reactive(mtcars)
  data.label <- jstable::mk.lev(mtcars)

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

  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))
    )
  })
}

Include jsmodule CSS styling

Description

Adds the custom 'style.css' file bundled with the jsmodule package to a Shiny UI. This allows consistent styling (e.g., bold navbar title, font tweaks, spacing) across all Shiny applications using this package.

Usage

use_jsmodule_style()

Details

This function is meant to be used inside the UI of a Shiny app. It automatically locates and includes the 'style.css' file found in 'inst/assets/' of the jsmodule package installation.

Value

An HTML '<link>' tag that loads the CSS into a Shiny UI

See Also

include

Examples

## Not run: 
use_jsmodule_style()

## End(Not run)