The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Using regression plots

NEST CoreDev

`teal` application to use regression 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 regression plot module tm_a_regression():

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_a_regression() using different combinations of data sets.

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

# configuration for the two wide datasets
mod2 <- tm_a_regression(
  label = "Two wide datasets",
  default_plot_type = 2,
  response = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2")),
      selected = "BMRKR1",
      multiple = FALSE,
      fixed = FALSE
    )
  ),
  regressor = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variables:",
      choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
      selected = c("AGE", "RACE"),
      multiple = TRUE,
      fixed = FALSE
    )
  )
)

# configuration for the same long datasets (same subset)
mod3 <- tm_a_regression(
  label = "Same long datasets (same subset)",
  default_plot_type = 2,
  response = data_extract_spec(
    dataname = "ADTTE",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADTTE"]], c("AVAL", "CNSR")),
      selected = "AVAL",
      multiple = FALSE,
      fixed = FALSE
    ),
    filter = filter_spec(
      label = "Select parameter:",
      vars = "PARAMCD",
      choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
      selected = "PFS",
      multiple = FALSE
    )
  ),
  regressor = data_extract_spec(
    dataname = "ADTTE",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADTTE"]], c("AGE", "CNSR", "SEX")),
      selected = c("AGE", "CNSR", "SEX"),
      multiple = TRUE
    ),
    filter = filter_spec(
      label = "Select parameter:",
      vars = "PARAMCD",
      choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
      selected = "PFS",
      multiple = FALSE
    )
  )
)

# configuration for the wide and long datasets
mod4 <- tm_a_regression(
  label = "Wide and long datasets",
  response = data_extract_spec(
    dataname = "ADLB",
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADLB"]]$PARAMCD)[2],
        multiple = TRUE,
        label = "Select measurement:"
      ),
      filter_spec(
        vars = "AVISIT",
        choices = levels(data[["ADLB"]]$AVISIT),
        selected = levels(data[["ADLB"]]$AVISIT)[2],
        multiple = TRUE,
        label = "Select visit:"
      )
    ),
    select = select_spec(
      label = "Select variable:",
      choices = "AVAL",
      selected = "AVAL",
      multiple = FALSE,
      fixed = TRUE
    )
  ),
  regressor = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variables:",
      choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2", "AGE")),
      selected = "AGE",
      multiple = TRUE,
      fixed = FALSE
    )
  )
)

# configuration for the same long datasets (different subsets)
mod5 <- tm_a_regression(
  label = "Same long datasets (different subsets)",
  default_plot_type = 2,
  response = data_extract_spec(
    dataname = "ADLB",
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADLB"]]$PARAMCD)[1],
        multiple = TRUE,
        label = "Select lab:"
      ),
      filter_spec(
        vars = "AVISIT",
        choices = levels(data[["ADLB"]]$AVISIT),
        selected = levels(data[["ADLB"]]$AVISIT)[1],
        multiple = TRUE,
        label = "Select visit:"
      )
    ),
    select = select_spec(
      choices = "AVAL",
      selected = "AVAL",
      multiple = FALSE,
      fixed = TRUE
    )
  ),
  regressor = data_extract_spec(
    dataname = "ADLB",
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADLB"]]$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"]], c("AVAL", "AGE", "BMRKR1", "BMRKR2", "SEX", "ARM")),
      selected = c("AVAL", "BMRKR1"),
      multiple = TRUE
    )
  )
)

# initialize the app
app <- init(
  data = data,
  modules = modules(
    modules(
      label = "Regression plots",
      mod1,
      mod2,
      mod3,
      mod4,
      mod5
    )
  )
)

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))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.