# 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