tb1simpleUI : tb1 module UI for propensity score analysis

Table 1 module UI for propensity score analysis.

tb1simpleUI(id)

Arguments

id	id

Value

Table 1 UI for propensity score analysis

Details

tb1 module 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)
  })
}