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Using cross table

NEST CoreDev

teal application to use cross table with various datasets types

This vignette will guide you through the four parts to create a teal application using various types of datasets using the cross table module tm_t_crosstable():

  1. Load libraries
  2. Create data sets
  3. Create an app variable
  4. Run the app

1 - Load libraries

library(teal.modules.general) # used to create the app
library(dplyr) # used to modify data sets
library(rtables)

2 - Create data sets

Inside this app 2 datasets will be used

  1. ADSL A wide data set with subject data
  2. ADLB A long data set with lab measurements for each subject
data <- within(data, {
  ADSL <- teal.data::rADSL
  ADLB <- teal.data::rADLB %>%
    mutate(CHGC = as.factor(case_when(
      CHG < 1 ~ "N",
      CHG > 1 ~ "P",
      TRUE ~ "-"
    )))
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

3 - Create an app variable

This is the most important section. We will use the teal::init() function to create an app. The data will be handed over using teal.data::teal_data(). The app itself will be constructed by multiple calls of tm_t_crosstable() using different combinations of data sets.

# configuration for the single wide dataset
mod1 <- tm_t_crosstable(
  label = "Single wide dataset",
  x = data_extract_spec(
    "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]]),
      selected = names(data[["ADSL"]])[5],
      multiple = TRUE,
      fixed = FALSE,
      ordered = TRUE
    )
  ),
  y = data_extract_spec(
    "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]]),
      selected = names(data[["ADSL"]])[6],
      multiple = FALSE,
      fixed = FALSE
    )
  )
)

# configuration for the same long datasets (different subsets)
mod2 <- tm_t_crosstable(
  label = "Same long datasets (different subsets)",
  x = data_extract_spec(
    dataname = "ADLB",
    filter = filter_spec(
      vars = "PARAMCD",
      choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
      selected = levels(data[["ADLB"]]$PARAMCD)[1],
      multiple = FALSE
    ),
    select = select_spec(
      choices = variable_choices(data[["ADLB"]]),
      selected = "AVISIT",
      multiple = TRUE,
      fixed = FALSE,
      ordered = TRUE,
      label = "Select variable:"
    )
  ),
  y = data_extract_spec(
    dataname = "ADLB",
    filter = filter_spec(
      vars = "PARAMCD",
      choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
      selected = levels(data[["ADLB"]]$PARAMCD)[1],
      multiple = FALSE
    ),
    select = select_spec(
      choices = variable_choices(data[["ADLB"]]),
      selected = "LOQFL",
      multiple = FALSE,
      fixed = FALSE,
      label = "Select variable:"
    )
  )
)

# initialize the app
app <- init(
  data = data,
  modules = modules(
    modules(
      label = "Cross table",
      mod1,
      mod2
    )
  )
)

4 - Run the app

A simple shiny::shinyApp() call will let you run the app. Note that app is only displayed when running this code inside an R session.

shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))

5 - Try it out in Shinylive

Open in Shinylive

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.