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Using association plot

NEST CoreDev

`teal` application to use association plot 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 association plot module tm_g_association():

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

2 - Create data sets

Inside this app 4 datasets will be used

ADSL A wide data set with subject data
ADRS A long data set with response data for subjects at different time points of the study
ADTTE A long data set with time to event data
ADLB A long data set with lab measurements for each subject

data <- teal_data()
data <- within(data, {
  ADSL <- teal.modules.general::rADSL %>%
    mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1))
  ADRS <- teal.modules.general::rADRS
  ADTTE <- teal.modules.general::rADTTE
  ADLB <- teal.modules.general::rADLB %>%
    mutate(CHGC = as.factor(case_when(
      CHG < 1 ~ "N",
      CHG > 1 ~ "P",
      TRUE ~ "-"
    )))
})
datanames <- c("ADSL", "ADRS", "ADTTE", "ADLB")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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_association() using different combinations of data sets.

# configuration for a single wide dataset
mod1 <- tm_g_association(
  label = "Single wide dataset",
  ref = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]]),
      selected = "AGE",
      fixed = FALSE
    )
  ),
  vars = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variables:",
      choices = variable_choices(data[["ADSL"]]),
      selected = "BMRKR1",
      multiple = TRUE,
      fixed = FALSE
    )
  )
)

# configuration for two wide datasets
mod2 <- tm_g_association(
  label = "Two wide datasets",
  ref = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "STRATA1", "RACE")),
      selected = "STRATA1",
      multiple = FALSE,
      fixed = FALSE
    )
  ),
  vars = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variables:",
      choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE", "COUNTRY")),
      selected = c("AGE", "COUNTRY", "RACE"),
      multiple = TRUE,
      fixed = FALSE
    )
  )
)

# configuration for multiple long datasets
mod3 <- tm_g_association(
  label = "Multiple different long datasets",
  ref = data_extract_spec(
    dataname = "ADTTE",
    select = select_spec(
      label = "Select variables:",
      choices = variable_choices(data[["ADTTE"]]),
      selected = "AVAL",
      multiple = FALSE,
      fixed = FALSE
    ),
    filter = filter_spec(
      label = "Select endpoint:",
      vars = "PARAMCD",
      choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
      selected = c("PFS", "EFS"),
      multiple = TRUE
    )
  ),
  vars = data_extract_spec(
    dataname = "ADRS",
    reshape = TRUE,
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADRS"]], c("AVALC", "BMRKR1", "BMRKR2", "ARM")),
      selected = "AVALC",
      multiple = TRUE,
      fixed = FALSE
    ),
    filter = list(
      filter_spec(
        label = "Select endpoints:",
        vars = "PARAMCD",
        choices = value_choices(data[["ADRS"]], "PARAMCD", "PARAM"),
        selected = "BESRSPI",
        multiple = TRUE
      ),
      filter_spec(
        label = "Select endpoints:",
        vars = "AVISIT",
        choices = levels(data[["ADRS"]]$AVISIT),
        selected = "SCREENING",
        multiple = TRUE
      )
    )
  )
)

# configuration for wide and long datasets
mod4 <- tm_g_association(
  label = "Wide and long datasets",
  ref = data_extract_spec(
    dataname = "ADRS",
    select = select_spec(
      choices = variable_choices(data[["ADRS"]], c("AVAL", "AVALC")),
      selected = "AVALC",
      multiple = FALSE,
      fixed = FALSE,
      label = "Selected variable:"
    ),
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADRS"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADRS"]]$PARAMCD),
        multiple = TRUE,
        label = "Select response"
      ),
      filter_spec(
        vars = "AVISIT",
        choices = levels(data[["ADRS"]]$AVISIT),
        selected = levels(data[["ADRS"]]$AVISIT),
        multiple = TRUE,
        label = "Select visit:"
      )
    )
  ),
  vars = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      choices = variable_choices(data[["ADSL"]], c("SEX", "AGE", "RACE", "COUNTRY", "BMRKR1", "STRATA1", "ARM")),
      selected = "AGE",
      multiple = TRUE,
      fixed = FALSE,
      label = "Select variable:"
    )
  )
)

# configuration for the same long dataset (same subsets)
mod5 <- tm_g_association(
  label = "Same long datasets (same subsets)",
  ref = data_extract_spec(
    dataname = "ADRS",
    select = select_spec(
      choices = variable_choices(data[["ADRS"]]),
      selected = "AVALC",
      multiple = FALSE,
      fixed = FALSE,
      label = "Select variable:"
    )
  ),
  vars = data_extract_spec(
    dataname = "ADRS",
    select = select_spec(
      choices = variable_choices(data[["ADRS"]]),
      selected = "PARAMCD",
      multiple = TRUE,
      fixed = FALSE,
      label = "Select variable:"
    )
  )
)

# configuration for the same long dataset (different subsets)
mod6 <- tm_g_association(
  label = "Same long datasets (different subsets)",
  ref = data_extract_spec(
    dataname = "ADLB",
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADLB"]]$PARAMCD)[1],
        multiple = FALSE,
        label = "Select lab:"
      ),
      filter_spec(
        vars = "AVISIT",
        choices = levels(data[["ADLB"]]$AVISIT),
        selected = levels(data[["ADLB"]]$AVISIT)[1],
        multiple = FALSE,
        label = "Select visit:"
      )
    ),
    select = select_spec(
      choices = variable_choices(data[["ADLB"]], c("AVAL", "CHG2", "PCHG2")),
      selected = "AVAL",
      multiple = FALSE
    )
  ),
  vars = data_extract_spec(
    dataname = "ADLB",
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADLB"]]$PARAMCD)[1],
        multiple = FALSE,
        label = "Select labs:"
      ),
      filter_spec(
        vars = "AVISIT",
        choices = levels(data[["ADLB"]]$AVISIT),
        selected = levels(data[["ADLB"]]$AVISIT)[1],
        multiple = FALSE,
        label = "Select visit:"
      )
    ),
    select = select_spec(
      choices = variable_choices(data[["ADLB"]]),
      selected = "STRATA1",
      multiple = TRUE
    )
  )
)

# initialize the app
app <- init(
  data = data,
  modules = modules(
    # tm_g_association ----
    modules(
      label = "Association plot",
      mod1,
      mod2,
      mod3,
      mod4,
      mod5,
      mod6
    )
  )
)

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))

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They may not be fully stable and should be used with caution. We make no claims about them.
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