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Data merge

NEST coreDev

2022-05-11

data-merge.Rmd

Combining datasets is a crucial step when using modules with more than one dataset. In the context of teal, we use the term “merge” to combine datasets where two functions are offered data_merge_module and data_merge_srv. Depending on the specific scenario, one or the other shall be used.

When no processing of the data_extract list is required, the data_merge_module function is used to read the data and the data_extract_spec’s list and apply the merging. It is a wrapper that combines data_extract_multiple_srv() and data_merge_srv() see below for more details. With additional processing of the data_extract list input, data_merge_srv() can be combined with data_extract_multiple_srv() or data_extract_srv() to customize the selector_list input.

In the coming sections, we will show examples of both scenarios.

data_merge_module

With data_merge_module solely, all you would need is a list of data_extract_spec objects for the data_extract argument and a FilteredData object for the datasets argument.

App code 

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
  )
)

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

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

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)

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

datasets <- sample_filtered_data()

Shiny app 

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

data_extract_multiple_srv + data_merge_srv

In the scenario above, if the user deselects the ADTTE variable, the merging between ADTTE and ADSL would still take place even though ADTTE is not used or needed here. Here, the developer might update the selector_list input in a reactive manner so that it gets updated based on conditions set by the developer. Below, we reuse the input from above and we update the app server so that the adtte_extract is removed from the selector_list input when no ADTTE variable is selected and the reactive_selector_list is passed to data_merge_srv:

merge_module <- function(id, datasets, data_extracts) {
  moduleServer(id, function(input, output, session) {
    selector_list <- teal.transform::data_extract_multiple_srv(data_extracts, datasets)
    reactive_selector_list <- reactive({
      if (is.null(selector_list()$adtte_extract) || length(selector_list()$adtte_extract()$select) == 0) {
        selector_list()[names(selector_list()) != "adtte_extract"]
      } else {
        selector_list()
      }
    })

    merged_data <- teal.transform::data_merge_srv(
      selector_list = reactive_selector_list,
      datasets = datasets,
      merge_function = "dplyr::left_join"
    )
    output$expr <- renderText(merged_data()$expr)
    output$data <- renderDataTable(merged_data()$data())
  })
}

Shiny app 

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

data_merge_module is replaced here with three parts:

  1. selector_list: output of data_extract_multiple_srv which loops over the list of data_extract given and runs data_extract_srv for each one returning a list of reactive objects.
  2. reactive_selector_list: intermediate reactive list updating selector_list content
  3. merged_data: output of data_merge_srv using reactive_selector_list as input

Output from merging

Both merge functions, data_merge_srv and data_merge_module, return a reactive object which contains a list of the following elements:

  • data: the merged dataset after filtering and reshaping containing selected columns
  • expr: code needed to replicate merged dataset
  • chunks: chunks R6 object (see teal.code)
  • columns_source: list of columns selected per selector
  • keys: the keys of the merged dataset
  • filter_info: filters that are applied on the data

These elements can be further used inside the server to retrieve and use information about the selections, data, filters, …

Merging of non CDISC datasets

General datasets do not share the same relationships as CDISC datasets thus these relationships must be specified by the join_keys functions. For more information, please refer to the Join Keys vignette.

The data merge module respects the relationships given by the user and in the case of multiple datasets to merge, the order is specified by the order of elements in the data_extract argument of the data_merge_module function. Merging groups of datasets with complex relationships can quickly become challenging to specify so please take extra care when setting this up.