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Combining data-extract with data-merge

NEST coreDev

2022-05-03

data-extract-merge.Rmd

Combining data-extract with data-merge

teal.transform provides merge_expression_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, join_keys) {
  moduleServer(id, function(input, output, session) {
    selector_list <- teal.transform::data_extract_multiple_srv(data_extracts, datasets, join_keys)
    merged_data <- teal.transform::merge_expression_srv(
      selector_list = selector_list,
      datasets = datasets,
      join_keys = join_keys,
      merge_function = "dplyr::left_join"
    )
    ANL <- reactive({ # nolint
      eval(envir = list2env(datasets), expr = as.expression(merged_data()$expr))
    })
    output$expr <- renderText(paste(merged_data()$expr, collapse = "\n"))
    output$data <- renderDataTable(ANL())
  })
}

Output from data_extract_srv (reactive) should be passed to merge_expression_srv together with datasets (list of data.frame objects) and join_keys list. merge_expression_srv returns a reactive list containing merge expression and information needed to perform the transformation - see more in merge_expression_srv documentation.

Example data

data_extract_srv module depends on either a list of reactive or non-reactive data.frame objects. Here, we show the usage of a list of data.frame objects as input to datasets where a list of necessary join keys per data.frame object is required:

# Define data.frame objects
ADSL <- scda::synthetic_cdisc_data("latest")$adsl # nolint
ADTTE <- scda::synthetic_cdisc_data("latest")$adtte # nolint

# create a list of data.frame objects
datasets <- list(ADSL = ADSL, ADTTE = ADTTE)

# create join_keys
join_keys <- teal.data::join_keys(
  teal.data::join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")),
  teal.data::join_key("ADSL", "ADTTE", c("STUDYID", "USUBJID")),
  teal.data::join_key("ADTTE", "ADTTE", c("STUDYID", "USUBJID", "PARAMCD"))
)

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, join_keys)
  }
)