zappingseb

# This is an example app to understand how nested shiny modules are
# working and what is the general principle

library(shiny)

# Inner Module representing a widget
# that contains of UI rendered parts (text2)
# and server rendered parts (text1)
ui_inner <- function(id, text2) {
  ns <- NS(id)

zappingseb

# alpine-python-ecmwfapi
FROM rocker/tidyverse:3.6.0
MAINTAINER zappingseb "sebastian@mail-wolf.de"

RUN R -e "install.packages(c('RSelenium'), repos='https://cran.rstudio.com/') "

RUN apt-get update -qq \
  && apt-get install -y \
  python-pip \
  vim

zappingseb

# Create the dataset by merging the two tables per patient
  data_set_reactive <- metaReactive({
    event_data_filtered <- event_data %>% dplyr::filter(PARAMCD == !!input$filter_param)
    ads_selected <- pat_data %>% dplyr::select(dplyr::one_of(c(!!input$select_regressor, c("SUBJID", "STUDYID"))))
    merge(ads_selected, event_data_filtered, by = c("SUBJID", "STUDYID"))
  })

  # Create a pure reactive to create the formula shown in the linear model
  formula_reactive <- reactive({
    validate(need(is.character(input$select_regressor), "Cannot work without selected column"))

zappingseb

observeEvent(input$show_r_code, {
    showModal(modalDialog(
      title = "R Code",
      tags$pre(
        id = "r_code",
        expandChain(
          library_code,
          data_code,
          output$plot1(),
          output$text1()

zappingseb

# Plot Regression vs fitted
output$plot1 <- metaRender(renderPlot, {
  plot(!!model_reactive(), which = 1)
})

# show model summary
output$text1 <- metaRender(renderPrint, {
  !!model_reactive() %>% summary()
})

zappingseb

# Create a linear model
  model_reactive <- metaReactive2({
    validate(need(is.data.frame(data_set_reactive()), "Data Set could not be created"))
    validate(need(is.language(formula_reactive()), "Formula could not be created from column selections"))

    metaExpr(bindToReturn = TRUE, {
      model_data <- !!data_set_reactive()
      lm(formula = !!formula_reactive(), data = model_data)
    })
  })

zappingseb

formula_reactive <- reactive({
  validate(need(is.character(input$select_regressor), "Cannot work without selected column"))
  regressors <- Reduce(function(x, y) call("+", x, y), rlang::syms(input$select_regressor))
  rlang::new_formula(rlang::sym("AVAL"), regressors)
})

zappingseb

data_set_reactive <- metaReactive({
    event_data_filtered <- event_data %>% dplyr::filter(PARAMCD == !!input$filter_param)
    ads_selected <- pat_data %>% dplyr::select(dplyr::one_of(c(!!input$select_regressor, c("SUBJID", "STUDYID"))))
    merge(ads_selected, event_data_filtered, by = c("SUBJID", "STUDYID"))
  })

zappingseb

data_code <- quote({

  # Patient listing
  pat_data <- ...

  # Days where Overall Survival (OS), Event free survival (EFS) and Progression Free Survival (PFS) happened
  event_data <- ...
})

eval(data_code)

zappingseb

# Create a linear model
model_reactive <- reactive({
      validate(need(is.character(input$select_regressor), "Cannot work without selected column"))

      regressors <- Reduce(function(x, y) call("+", x, y), rlang::syms(input$select_regressor))
      formula_value <- rlang::new_formula(rlang::sym("AVAL"), regressors)

      event_data_filtered <- event_data %>% dplyr::filter(PARAMCD == input$filter_param)
      ads_selected <- pat_data %>% dplyr::select(dplyr::one_of(c(input$select_regressor, c("SUBJID", "STUDYID"))))