Using cross table

`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():

Load libraries
Create data sets
Create an app variable
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

ADSL A wide data set with subject data
ADLB A long data set with lab measurements for each subject

data <- within(data, {
  ADSL <- teal.modules.general::rADSL
  ADLB <- teal.modules.general::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

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