Package 'jsmodule'

Title: 'RStudio' Addins and 'Shiny' Modules for Medical Research
Description: 'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Authors: Jinseob Kim [aut, cre] (ORCID: <https://orcid.org/0000-0002-9403-605X>), Zarathu [cph, fnd], Hyunki Lee [aut], Changwoo Lim [aut], Jinhwan Kim [aut] (ORCID: <https://orcid.org/0009-0009-3217-2417>), Yoonkyoung Jeon [aut], Jaewoong Heo [aut], Youngsun Park [aut] (ORCID: <https://orcid.org/0009-0009-9336-2281>), Hyungwoo Jo [aut], Jeongmin Seo [aut], Hojun LEE [aut], Sungho Choi [aut], Yeji Kang [aut], Mingu Jee [aut]
Maintainer: Jinseob Kim <[email protected]>
License: Apache License 2.0
Version: 2.0.1
Built: 2026-05-27 15:14:25 UTC
Source: https://github.com/jinseob2kim/jsmodule

Help Index

aiAssistant: AI Assistant module server

Description

AI-powered statistical analysis assistant module server

Usage

aiAssistant(
  input,
  output,
  session,
  data,
  data_label,
  data_varStruct = NULL,
  api_key = NULL,
  stats_guide = NULL,
  show_api_config = TRUE,
  analysis_context = NULL
)

Arguments

input

input
output

output
session

session
data

Data (reactive). Should return the current data set each time it is called.
data_label

Data label (reactive). Typically created with 'jstable::mk.lev()'.
data_varStruct

Variable structure list of data (reactive or NULL). If NULL, automatically generates 'list(variable = names(data()))'. Can also be a reactive returning a named list with elements like 'Base', 'Event', 'Time', etc. Default: NULL
api_key

API key for AI service. If NULL, reads from provider-specific environment variables ('ANTHROPIC_API_KEY', 'OPENAI_API_KEY', 'GOOGLE_API_KEY') configured in '.Renviron' file
stats_guide

Optional custom statistical guide text to override default guidelines. Can be a character string or reactive. If NULL, uses built-in statistical best practices guide. Useful for adding domain-specific statistical conventions or organizational standards.
show_api_config

If TRUE, shows API config UI. If FALSE, uses only env vars. Default: TRUE
analysis_context

Optional character string, list, or reactive returning that information. Used to pass prior analysis context that the AI can reference in follow-up questions.

Details

  • 'data' and 'data_label' must be reactives; their values are re-evaluated every time the module needs data.

  • 'data_varStruct' can be NULL (auto-generated) or a reactive returning a named list with elements like 'variable', 'Base', 'Event', 'Time', etc. This mirrors the structure used by other *jsmodule* components.

  • Generated code runs in a sandbox that only exposes the supplied data and allows the following packages: jstable, jskm, jsmodule, survival, ggplot2, ggpubr, pROC, data.table, DT, gridExtra, GGally, forestploter, MatchIt, timeROC.

  • API keys are resolved in the order: explicit 'api_key' argument, UI input (if 'show_api_config = TRUE'), provider-specific environment variables ('ANTHROPIC_API_KEY', 'OPENAI_API_KEY', 'GOOGLE_API_KEY').

  • To use environment variables for API keys, add them to your '.Renviron' file (use 'usethis::edit_r_environ()' to open it) with lines like:
    'ANTHROPIC_API_KEY=your_key_here'
    'OPENAI_API_KEY=your_key_here'
    'GOOGLE_API_KEY=your_key_here'
    Then restart R session for changes to take effect.

  • 'analysis_context' can be a static string/list or a reactive that returns a description of prior analyses (tables, plots, code snippets). The text is appended to the system prompt so the AI can reference earlier steps.

Value

Server module (no explicit return value). Creates reactive outputs and observers for chat conversation history, generated code execution, analysis results (tables, plots, text), and export functionality.

Examples

## Not run: 
# Setup: Add API key to .Renviron file
# usethis::edit_r_environ()
# Add line: ANTHROPIC_API_KEY=your_actual_key_here
# Save and restart R

library(shiny)
library(DT)
library(survival)

# Example 1: Basic usage with auto-generated variable structure
ui <- fluidPage(
  titlePanel("AI Statistical Assistant"),
  aiAssistantUI("ai")
)

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

  callModule(aiAssistant, "ai",
    data = data,
    data_label = data.label,
    data_varStruct = NULL  # Auto-generates variable structure
  )
}

shinyApp(ui, server)

# Example 2: With custom variable structure and analysis context
ui2 <- fluidPage(
  titlePanel("Survival Analysis Assistant"),
  aiAssistantUI("ai")
)

server2 <- function(input, output, session) {
  data <- reactive(colon)
  data.label <- reactive(jstable::mk.lev(colon))

  # Custom variable structure for survival analysis
  var_struct <- reactive({
    list(
      variable = names(colon),
      Base = c("rx", "sex", "age", "obstruct", "nodes"),
      Event = "status",
      Time = "time"
    )
  })

  callModule(aiAssistant, "ai",
    data = data,
    data_label = data.label,
    data_varStruct = var_struct,
    analysis_context = reactive({
      "Colon cancer adjuvant chemotherapy trial (survival::colon).
       Primary outcome: time to recurrence or death (status/time).
       Treatment groups: Observation, Levamisole, Levamisole+5-FU."
    })
  )
}

shinyApp(ui2, server2)

# Example 3: Production deployment without API config UI
ui_prod <- fluidPage(
  aiAssistantUI("ai", show_api_config = FALSE)
)

server_prod <- function(input, output, session) {
  # Relies entirely on .Renviron configuration
  callModule(aiAssistant, "ai",
    data = reactive(mtcars),
    data_label = reactive(jstable::mk.lev(mtcars)),
    show_api_config = FALSE
  )
}

shinyApp(ui_prod, server_prod)

## End(Not run)

aiAssistantUI: AI Assistant module UI

Description

AI-powered statistical analysis assistant module UI

Usage

aiAssistantUI(id, show_api_config = TRUE)

Arguments

id

Module's namespace ID. Used to create unique identifiers for UI elements.
show_api_config

If TRUE, shows API configuration UI. If FALSE, uses only env vars. Default: TRUE

Details

Provides an interactive chat interface with AI for statistical analysis code generation

Value

Shiny UI tagList containing the AI Assistant interface with chat, code editor, and result panels

Examples

## Not run: 
# Setup: Add API key to .Renviron file
# usethis::edit_r_environ()
# Add line: ANTHROPIC_API_KEY=your_actual_key_here
# Save and restart R

library(shiny)
library(DT)
library(survival)

# Example 1: Basic usage with auto-generated variable structure
ui <- fluidPage(
  titlePanel("AI Statistical Assistant"),
  aiAssistantUI("ai")
)

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

  callModule(aiAssistant, "ai",
    data = data,
    data_label = data.label,
    data_varStruct = NULL  # Auto-generates variable structure
  )
}

shinyApp(ui, server)

# Example 2: With custom variable structure and analysis context
ui2 <- fluidPage(
  titlePanel("Survival Analysis Assistant"),
  aiAssistantUI("ai")
)

server2 <- function(input, output, session) {
  data <- reactive(colon)
  data.label <- reactive(jstable::mk.lev(colon))

  # Custom variable structure for survival analysis
  var_struct <- reactive({
    list(
      variable = names(colon),
      Base = c("rx", "sex", "age", "obstruct", "nodes"),
      Event = "status",
      Time = "time"
    )
  })

  callModule(aiAssistant, "ai",
    data = data,
    data_label = data.label,
    data_varStruct = var_struct,
    analysis_context = reactive({
      "Colon cancer adjuvant chemotherapy trial (survival::colon).
       Primary outcome: time to recurrence or death (status/time).
       Treatment groups: Observation, Levamisole, Levamisole+5-FU."
    })
  )
}

shinyApp(ui2, server2)

# Example 3: Production deployment without API config UI
ui_prod <- fluidPage(
  aiAssistantUI("ai", show_api_config = FALSE)
)

server_prod <- function(input, output, session) {
  # Relies entirely on .Renviron configuration
  callModule(aiAssistant, "ai",
    data = reactive(mtcars),
    data_label = reactive(jstable::mk.lev(mtcars)),
    show_api_config = FALSE
  )
}

shinyApp(ui_prod, server_prod)

## End(Not run)

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

id

id
data

Reactive data
data_label

Reactive data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10

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

id

id
label

label

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

id

id
data

Reactive data
data_label

Reactive data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10

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

id

id
label

label

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

input

input
output

output
session

session
data

reactive data
data_label

reactuve data label
data_varStruct

reactive list of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

reactive survey data. default: NULL
default.unires

Set default independent variables using univariate analysis.
limit.unires

Change to default.unires = F if number of independent variables > limit.unires, Default: 20
id.cluster

reactive cluster variable if marginal cox model, Default: NULL
ties.coxph

'coxph' ties option, one of 'efron', 'breslow', 'exact', default: 'erfon'
vec.event

event variables as vector for survival analysis, Default: NULL
vec.time

time variables as vector for survival analysis, Default: NULL

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

id

id

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

input, output, session

Standard Shiny server parameters.
nfactor.limit

An integer, the threshold for unique values to suggest a numeric variable as categorical, Default: 20

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

id

A string, the module id.
label

A string, the label for the file input, Default: 'Upload data (csv/xlsx/sav/sas7bdat/dta)'

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)
}

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

input, output, session

Standard Shiny server parameters.
nfactor.limit

An integer, the threshold for unique values.

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

id

A string, the module id.
label

A string, the label for the file input.

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

input, output, session

Standard Shiny server parameters.
nfactor.limit

An integer, the threshold for unique values.

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

id

A string, the module id.
label

A string, the label for the file input.

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

input, output, session

Standard Shiny server parameters.
nfactor.limit

An integer, the threshold for unique values.

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

id

A string, the module id.
label

A string, the label for the file input.

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)
}

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

id

id
data

Reactive data
data_label

Reactive data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

reactive survey data. default: NULL
cluster_id

cluster option variable for marginal cox model
vec.event

event variables as vector for survival analysis, Default: NULL
vec.time

time variables as vector for survival analysis, Default: NULL

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

id

id
label

label, Default: 'forestplot'

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

id

id
data

Reactive data
data_label

Reactive data label
family

family, "gaussian" or "binomial" or 'poisson' or 'quasipoisson'
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

reactive survey data. default: NULL
repeated_id

data when repeated id. default: F

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

id

id
label

label, Default: 'forestplot'

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]]
  })
}

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

input

input
output

output
session

session
data

reactive data, ordered by id.
data_label

reactive data label
data_varStruct

List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
id.gee

reactive repeated measure variable
vec.event

event variables as vector for gaussian generalized estimating equation(GEE), Default: NULL

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

input

input
output

output
session

session
data

reactive data, ordered by id.
data_label

reactive data label
data_varStruct

List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
id.gee

reactive repeated measure variable
vec.event

event variables as vector for binomial gaussian generalized estimating equation(GEE), Default: NULL

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

id

id

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"))
  })
}

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

input

input
output

output
session

session
data

data
data_label

data label
data_varStruct

List of variable structure, Default: NULL
nfactor.limit

nlevels limit for categorical variables, Default: 20

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

input

input
output

output
session

session
data

Reactive data
data_label

Reactive data label
data_varStruct

List of variable structure, Default: NULL
nfactor.limit

nlevels limit for categorical variables, Default: 20

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

id

id

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

id

id

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

id

id

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

id

id
data

Reactive data
data_label

Reactive data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10

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

id

id
label

label

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())
  })
}

Detect if running in production/deployment environment

Description

Detect if running in production/deployment environment

Usage

is_production_environment()

Details

Default is FALSE (development mode) when DEPLOYMENT_ENV is not set

Value

Logical. TRUE if in production, FALSE if local development

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

nfactor.limit

nlevels limit for categorical variables, Default: 20
max.filesize

Maximum file size to upload (MB), Default: 2048 (2 GB)

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

data

data
nfactor.limit

nlevels limit for categorical variables

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

nfactor.limit

nlevels limit for categorical variables, Default: 20
max.filesize

Maximum file size to upload (MB), Default: 2048 (2 GB)

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

data

data
nfactor.limit

nlevels limit for categorical variables, Default: 20

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

nfactor.limit

nlevels limit for categorical variables, Default: 20
max.filesize

Maximum file size to upload (MB), Default: 2048 (2 GB)

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

data

data
nfactor.limit

nlevels limit for categorical variables

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

nfactor.limit

nlevels limit for categorical variables, Default: 20
max.filesize

Maximum file size to upload (MB), Default: 2048 (2 GB)

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

data

data
nfactor.limit

nlevels limit for categorical variables

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

input

input
output

output
session

session
data

Reactive data
data_label

Reactive data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

Reactive survey data. default: NULL
id.cluster

Reactive cluster variable if marginal model, Default: NULL
timeby

timeby, Default: NULL
range.x

range of x axis, Default: NULL
range.y

range of y axis, Default: NULL
vec.event

event variables as vector for survival analysis, Default: NULL
vec.time

time variables as vector for survival analysis, Default: NULL

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

id

id

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

id

id
data

Reactive data
data_label

Reactive data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10

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

id

id
label

label

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

logistic.model

glm object(binomial)
alpha

alpha, Default: 0.05
crude

crude, Default: TRUE
crude.p.value

crude.p.value, Default: FALSE
decimal

decimal, Default: 2
simplified

simplified, Default: FALSE

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

input

input
output

output
session

session
data

reactive data
data_label

reactive data label
data_varStruct

List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

reactive survey data. default: NULL
default.unires

Set default independent variables using univariate analysis, Default: T
limit.unires

Change to default.unires = F if number of independent variables > limit.unires, Default: 20
vec.event

event variables as vector for logistic regression, Default: NULL

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

out.old

raw data
out.label

pre-defined label data

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

varlist

Original variable list.
vars

variable to include.

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

varlist

Original variable list
vars

variable to exclude.

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

id

id

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

data

Data frame or matrix that includes the outcome and predictors variables.
cOutcome

Column number of the outcome variable.
predrisk1

Vector of predicted risks of all individuals using initial model.
predrisk2

Vector of predicted risks of all individuals using updated model.
cutoff

Cutoff values for risk categories. Define the cut-off values. Ex: c(0,.20,.30,1)
dec.value

digits of value, Default: 4
dec.p

digits of p, Default: 3

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

regress.model

lm object
alpha

alpha, Default: 0.05
crude

crude, Default: FALSE
crude.p.value

crude.p.value, Default: FALSE
decimal

decimal, Default: 2
simplified

simplified, Default: FALSE

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

input

input
output

output
session

session
data

reactive data
data_label

reactive data label
data_varStruct

List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

reactive survey data. default: NULL
default.unires

Set default independent variables using univariate analysis, Default: T
limit.unires

Change to default.unires = F if number of independent variables > limit.unires, Default: 20
vec.event

event variables as vector for linear regression, Default: NULL

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

id

id

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)
  })
}

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

ListModel

list of roc object
dec.auc

digits for AUC, Default: 3
dec.p

digits for p value, Default: 3

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)

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

input

input
output

output
session

session
data

Reactive data
data_label

Reactuve data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

Reactive survey data. default: NULL
id.cluster

Reactive cluster variable if marginal model, Default: NULL

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$cut_roc <- renderTable({
    if (is.null(out_roc()$cut)) return(NULL)
    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

input

input
output

output
session

session
data

Reactive data
data_label

Reactuve data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

Reactive survey data. default: NULL
id.cluster

Reactive cluster variable if marginal model, Default: NULL

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

id

id

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$cut_roc <- renderTable({
    if (is.null(out_roc()$cut)) return(NULL)
    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))
    )
  })
}

Safe evaluation wrapper with environment-aware security

Description

Safe evaluation wrapper with environment-aware security

Usage

safe_eval_expr(expr, envir, timeout = 10)

Arguments

expr

Expression to evaluate
envir

Environment for evaluation
timeout

Timeout in seconds (default: 10)

Details

In production mode, uses RAppArmor::eval.secure if available. In development mode, uses standard eval for easier debugging.

Value

Evaluation result

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

id

id
data

Reactive data
data_label

Reactive data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10

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(
      optionUI("scatter"),
      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

id

id
label

label

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(
      optionUI("scatter"),
      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

var.event

event
var.time

time
list.vars.ind

list of independent variable
t

time
data

data
dec.auc

digits for AUC, Default: 3
dec.p

digits for p value, Default: 3
id.cluster

cluster variable if marginal model, Default: NULL

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

input

input
output

output
session

session
data

Data
data_label

Data label
data_varStruct

Variable structure list of data, Default: NULL
nfactor.limit

maximum factor levels to include, Default: 10
design.survey

survey data of survey package. default: NULL
showAllLevels

Show All label information with 2 categorical variables, Default: T
argsExact

Option for Fisher exact test memory limit.

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

input

input
output

output
session

session
data

Reactive data
data_label

Reactive data label
data_varStruct

Variable structure list of data, Default: NULL
nfactor.limit

maximum factor levels to include, Default: 10
design.survey

Reactive survey data of survey package. Default: NULL
showAllLevels

Show All label information with 2 categorical variables, Default: T
argsExact

Option for Fisher exact test memory limit.

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

id

id

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

input

input
output

output
session

session
data

Original data with propensity score
matdata

Matching data
data_label

Data label
data_varStruct

List of variable structure, Default: NULL
group_var

Group variable to run propensity score analysis.
showAllLevels

Show All label information with 2 categorical variables, Default: T

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

input

input
output

output
session

session
data

Original reactive data with propensity score
matdata

Matching reactive data
data_label

Reactive data label
data_varStruct

List of variable structure, Default: NULL
vlist

List including factor/continuous/binary/except/non-normal variables
group_var

Group variable to run propensity score analysis.
showAllLevels

Show All label information with 2 categorical variables, Default: T

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

id

id

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

ListModel

list of timeROChelper object
dec.auc

digits for AUC, Default: 3
dec.p

digits for p value, Default: 3

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

var.event

event
var.time

time
vars.ind

independent variable
t

time
data

data
design.survey

survey data, Default: NULL
id.cluster

cluster variable if marginal model, Default: NULL

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

input

input
output

output
session

session
data

Reactive data
data_label

Reactuve data label
data_varStruct

Reactive List of variable structure, Default: NULL
nfactor.limit

nlevels limit in factor variable, Default: 10
design.survey

Reactive survey data. default: NULL
id.cluster

Reactive cluster variable if marginal model, Default: NULL
iid

logical, get CI of AUC, Default: T
NRIIDI

logical, get NRI & IDI, Default: T

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

id

id

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)