Author: NEST CoreDev

# Using scatterplot matrix

#### NEST CoreDev

# `teal` application to use scatter plot matrix 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 scatter plot matrix module `tm_g_scatterplotmatrix()`:

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 4 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. `ADTTE` A long data set with time to event data
4. `ADLB` A long data set with lab measurements for each subject

    data <- teal_data()
    data <- within(data, {
      ADSL <- teal.data::rADSL %>%
        mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1))
      ADRS <- teal.data::rADRS
      ADTTE <- teal.data::rADTTE
      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_g_scatterplotmatrix()` using different combinations of data sets.

    # configuration for the single wide dataset
    mod1 <- tm_g_scatterplotmatrix(
      label = "Single wide dataset",
      variables = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["ADSL"]]),
          selected = c("AGE", "RACE", "SEX", "BMRKR1", "BMRKR2"),
          multiple = TRUE,
          fixed = FALSE,
          ordered = TRUE
        )
      )
    )

    # configuration for the one long datasets
    mod2 <- tm_g_scatterplotmatrix(
      "One long dataset",
      variables = data_extract_spec(
        dataname = "ADTTE",
        select = select_spec(
          choices = variable_choices(data[["ADTTE"]], c("AVAL", "BMRKR1", "BMRKR2")),
          selected = c("AVAL", "BMRKR1", "BMRKR2"),
          multiple = TRUE,
          fixed = FALSE,
          ordered = TRUE,
          label = "Select variables:"
        )
      )
    )

    # configuration for the two long datasets
    mod3 <- tm_g_scatterplotmatrix(
      label = "Two long datasets",
      variables = list(
        data_extract_spec(
          dataname = "ADRS",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADRS"]]),
            selected = c("AVAL", "AVALC"),
            multiple = TRUE,
            fixed = FALSE,
            ordered = TRUE,
          ),
          filter = filter_spec(
            label = "Select endpoints:",
            vars = c("PARAMCD", "AVISIT"),
            choices = value_choices(data[["ADRS"]], c("PARAMCD", "AVISIT"), c("PARAM", "AVISIT")),
            selected = "OVRINV - SCREENING",
            multiple = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADTTE",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADTTE"]]),
            selected = c("AVAL", "CNSR"),
            multiple = TRUE,
            fixed = FALSE,
            ordered = TRUE
          ),
          filter = filter_spec(
            label = "Select parameters:",
            vars = "PARAMCD",
            choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
            selected = "OS",
            multiple = TRUE
          )
        )
      )
    )

    # initialize the app
    app <- init(
      data = data,
      modules = modules(
        modules(
          label = "Scatterplot matrix",
          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