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
library(lattice)

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