Author: NEST CoreDev

# Using outliers module

#### NEST CoreDev

# `teal` application to analyze and report outliers 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 outliers module `tm_outliers()`:

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

## 2 - Create data sets

Inside this app 3 datasets will be used

1. `ADSL` A wide data set with subject data
2. `ADRS` A long data set with response data for subjects at different time points of the study
3. `ADLB` A long data set with lab measurements for each subject

    data <- teal_data()
    data <- within(data, {
      ADSL <- teal.data::rADSL
      ADRS <- teal.data::rADRS
      ADLB <- teal.data::rADLB
    })
    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_outliers()` using different combinations of data sets.

    # configuration for the single wide dataset
    mod1 <- tm_outliers(
      label = "Single wide dataset",
      outlier_var = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1")),
          selected = "AGE",
          fixed = FALSE
        )
      ),
      categorical_var = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(
            data[["ADSL"]],
            subset = names(Filter(isTRUE, sapply(data[["ADSL"]], is.factor)))
          ),
          selected = "RACE",
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )

    # configuration for the wide and long datasets
    mod2 <- tm_outliers(
      label = "Wide and long datasets",
      outlier_var = list(
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1")),
            selected = "AGE",
            fixed = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADLB",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADLB"]], c("AVAL", "CHG2")),
            selected = "AVAL",
            multiple = FALSE,
            fixed = FALSE
          )
        )
      ),
      categorical_var =
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(
              data[["ADSL"]],
              subset = names(Filter(isTRUE, sapply(data[["ADSL"]], is.factor)))
            ),
            selected = "RACE",
            multiple = FALSE,
            fixed = FALSE
          )
        )
    )

    # configuration for the multiple long datasets
    mod3 <- tm_outliers(
      label = "Multiple long datasets",
      outlier_var = list(
        data_extract_spec(
          dataname = "ADRS",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADRS"]], c("ADY", "EOSDY")),
            selected = "ADY",
            fixed = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADLB",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADLB"]], c("AVAL", "CHG2")),
            selected = "AVAL",
            multiple = FALSE,
            fixed = FALSE
          )
        )
      ),
      categorical_var = list(
        data_extract_spec(
          dataname = "ADRS",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADRS"]], c("ARM", "ACTARM")),
            selected = "ARM",
            multiple = FALSE,
            fixed = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADLB",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(
              data[["ADLB"]],
              subset = names(Filter(isTRUE, sapply(data[["ADLB"]], is.factor)))
            ),
            selected = "RACE",
            multiple = FALSE,
            fixed = FALSE
          )
        )
      )
    )

    # initialize the app
    app <- init(
      data = data,
      modules = modules(
        # tm_outliers ----
        modules(
          label = "Outliers module",
          mod1,
          mod2,
          mod3
        )
      )
    )

## 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