data_extract_spec
The role of data_extract_spec is twofold: to create a UI
component in a shiny application and to pass user input
from the UI to a custom server logic that can use this input to
transform the data. Let’s delve into how it fulfills both of these
responsibilities.
Step 1/4 - Preparing the Data
library(teal.transform)
library(teal.data)
library(shiny)
# Define data.frame objects
ADSL <- teal.transform::rADSL
ADTTE <- teal.transform::rADTTE
# create a list of reactive data.frame objects
datasets <- list(
ADSL = reactive(ADSL),
ADTTE = reactive(ADTTE)
)
# create join_keys
join_keys <- join_keys(
join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")),
join_key("ADSL", "ADTTE", c("STUDYID", "USUBJID")),
join_key("ADTTE", "ADTTE", c("STUDYID", "USUBJID", "PARAMCD"))
)Step 2/4 - Creating a data_extract_spec Object
Consider the following example, where we create two UI elements, one
to filter on a specific level from SEX variable, and a
second one to select a variable from c("BMRKR1", "AGE").
data_extract_spec object is handed over to the
shiny app and gives instructions to generate UI
components.
simple_des <- data_extract_spec(
dataname = "ADSL",
filter = filter_spec(vars = "SEX", choices = c("F", "M")),
select = select_spec(choices = c("BMRKR1", "AGE"))
)Step 3/4 - Creating the shiny UI and Server
Modules
To demonstrate different initialization options of
data_extract_spec, let’s first define a shiny
module that utilizes data_extract_ui and
data_extract_srv to handle data_extract_spec
objects. This module creates a UI component for a single
data_extract_spec and prints a list of values returned from
the data_extract_srv module. For more information about
data_extract_ui and data_extract_srv, please
refer to the package documentation.
extract_ui <- function(id, data_extract) {
ns <- NS(id)
sidebarLayout(
sidebarPanel(
h3("Encoding"),
data_extract_ui(ns("data_extract"), label = "variable", data_extract)
),
mainPanel(
h3("Output"),
verbatimTextOutput(ns("output"))
)
)
}
extract_srv <- function(id, datasets, data_extract, join_keys) {
moduleServer(id, function(input, output, session) {
reactive_extract_input <- data_extract_srv("data_extract", datasets, data_extract, join_keys)
s <- reactive({
format_data_extract(reactive_extract_input())
})
output$output <- renderPrint({
cat(s())
})
})
}Step 4/4 - Creating the shiny App
Finally, we include extract_ui in the UI of the
shinyApp, and utilize extract_srv in the
server function of the shinyApp:
shinyApp(
ui = fluidPage(extract_ui("data_extract", simple_des)),
server = function(input, output, session) {
extract_srv("data_extract", datasets, simple_des, join_keys)
}
)