Combining data-extract with data-merge

NEST coreDev

2022-05-03

Combining data-extract with data-merge

teal.transform provides data_merge_srv which converts data_extract_srv into R expression to transform data for analytical purposes. For example, we may wish to select AGE from ADSL and select AVAL from ADTTE filtered for rows where PARAMCD is OS and merge the results (using the primary keys) to create an analysis dataset ANL to be used in the module, as this diagram shows:

In the following code block we create a data_extract_spec object per dataset as illustrated above.

library(teal.transform)
#> Loading required package: magrittr
library(shiny)

adsl_extract <- teal.transform::data_extract_spec(
  dataname = "ADSL",
  select = select_spec(
    label = "Select variable:",
    choices = c("AGE", "BMRKR1"),
    selected = "AGE",
    multiple = TRUE,
    fixed = FALSE
  )
)

adtte_extract <- teal.transform::data_extract_spec(
  dataname = "ADTTE",
  select = select_spec(
    choices = c("AVAL", "ASEQ"),
    selected = "AVAL",
    multiple = TRUE,
    fixed = FALSE
  ),
  filter = filter_spec(
    vars = "PARAMCD",
    choices = c("CRSD", "EFS", "OS", "PFS"),
    selected = "OS"
  )
)

data_extracts <- list(adsl_extract = adsl_extract, adtte_extract = adtte_extract)

Example module

Here, we create the merge_ui and the merge_srv functions which will be used to create the ui and the srv components of the shiny app, respectively.

merge_ui <- function(id, data_extracts) {
  ns <- NS(id)
  teal.widgets::standard_layout(
    output = teal.widgets::white_small_well(
      verbatimTextOutput(ns("expr")),
      dataTableOutput(ns("data"))
    ),
    encoding = div(
      teal.transform::data_extract_ui(
        ns("adsl_extract"), # must correspond with data_extracts list names
        label = "ADSL extract",
        data_extracts[[1]]
      ),
      teal.transform::data_extract_ui(
        ns("adtte_extract"), # must correspond with data_extracts list names
        label = "ADTTE extract",
        data_extracts[[2]]
      )
    )
  )
}

merge_srv <- function(id, datasets, data_extracts) {
  moduleServer(id, function(input, output, session) {
    selector_list <- teal.transform::data_extract_multiple_srv(data_extracts, datasets)
    merged_data <- teal.transform::data_merge_srv(
      selector_list = selector_list,
      datasets = datasets,
      merge_function = "dplyr::left_join"
    )
    output$expr <- renderText(merged_data()$expr)
    output$data <- renderDataTable(merged_data()$data())
  })
}

Output from data_extract_srv (reactive) should be passed to data_merge_srv together with datasets (FilteredData). data_merge_srv returns a reactive list containing merged data and information needed to perform the transformation - see more in data_merge_srv documentation.

Example data

teal.transform functions depend on a FilteredData object from the teal.slice package. For example purposes we define a wrapper function to initialize the necessary object:

sample_filtered_data <- function() {
  # create TealData
  adsl <- teal.data::cdisc_dataset("ADSL", scda::synthetic_cdisc_data("latest")$adsl)
  adtte <- teal.data::cdisc_dataset("ADTTE", scda::synthetic_cdisc_data("latest")$adtte)
  data <- teal.data::cdisc_data(adsl, adtte)

  # covert TealData to FilteredData
  datasets <- teal.slice:::filtered_data_new(data)
  teal.slice:::filtered_data_set(data, datasets)
  datasets
}

datasets <- sample_filtered_data()

Shiny app

Finally, we pass merge_ui and merge_srv to the ui and server functions of the shinyApp using the data_extracts defined in the first code block and the datasets object:

shinyApp(
  ui = fluidPage(merge_ui("data_merge", data_extracts)),
  server = function(input, output, session) {
    merge_srv("data_merge", datasets, data_extracts)
  }
)