data_extract_srv: Extraction of the selector(s) details

In teal.transform: Functions for Extracting and Merging Data in the 'teal' Framework

Extraction of the selector(s) details

Description

Extracting details of the selection(s) in data_extract_ui elements.

Usage

data_extract_srv(id, datasets, data_extract_spec, ...)

## S3 method for class 'FilteredData'
data_extract_srv(id, datasets, data_extract_spec, ...)

## S3 method for class 'list'
data_extract_srv(
  id,
  datasets,
  data_extract_spec,
  join_keys = NULL,
  select_validation_rule = NULL,
  filter_validation_rule = NULL,
  dataset_validation_rule = if (is.null(select_validation_rule) &&
    is.null(filter_validation_rule)) {
     NULL
 } else {
    
    shinyvalidate::sv_required("Please select a dataset")
 },
  ...
)

Arguments

id	An ID string that corresponds with the ID used to call the module's UI function.
datasets	(FilteredData or list of reactive or non-reactive data.frame) object containing data either in the form of FilteredData or as a list of data.frame. When passing a list of non-reactive data.frame objects, they are converted to reactive data.frames internally. When passing a list of reactive or non-reactive data.frame objects, the argument join_keys is required also.
data_extract_spec	(data_extract_spec or a list of data_extract_spec) A list of data filter and select information constructed by data_extract_spec.
...	An additional argument join_keys is required when datasets is a list of data.frame. It shall contain the keys per dataset in datasets.
join_keys	(join_keys or NULL) of keys per dataset in datasets.
select_validation_rule	(NULL or function) Should there be any shinyvalidate input validation of the select parts of the data_extract_ui. You can use a validation function directly (i.e. select_validation_rule = shinyvalidate::sv_required()) or for more fine-grained control use a function: select_validation_rule = ~ if (length(.) > 2) "Error". If NULL then no validation will be added. See example for more details.
filter_validation_rule	(NULL or function) Same as select_validation_rule but for the filter (values) part of the data_extract_ui.
dataset_validation_rule	(NULL or function) Same as select_validation_rule but for the choose dataset part of the data_extract_ui

Value

A reactive list containing following fields:

• filters: A list with the information on the filters that are applied to the data set.

• select: The variables that are selected from the dataset.

• always_selected: The column names from the data set that should always be selected.

• reshape: Whether reshape long to wide should be applied or not.

• dataname: The name of the data set.

• internal_id: The id of the corresponding shiny input element.

• keys: The names of the columns that can be used to merge the data set.

• iv: A shinyvalidate::InputValidator containing validator for this data_extract.

References

data_extract_srv

Examples

library(shiny)
library(shinyvalidate)
library(teal.data)
library(teal.widgets)

# Sample ADSL dataset
ADSL <- data.frame(
  STUDYID = "A",
  USUBJID = LETTERS[1:10],
  SEX = rep(c("F", "M"), 5),
  AGE = rpois(10, 30),
  BMRKR1 = rlnorm(10)
)

# Specification for data extraction
adsl_extract <- data_extract_spec(
  dataname = "ADSL",
  filter = filter_spec(vars = "SEX", choices = c("F", "M"), selected = "F"),
  select = select_spec(
    label = "Select variable:",
    choices = variable_choices(ADSL, c("AGE", "BMRKR1")),
    selected = "AGE",
    multiple = TRUE,
    fixed = FALSE
  )
)

# Using reactive list of data.frames
data_list <- list(ADSL = reactive(ADSL))

join_keys <- join_keys(join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")))

# App: data extraction with validation
ui <- fluidPage(
  standard_layout(
    output = verbatimTextOutput("out1"),
    encoding = tagList(
      data_extract_ui(
        id = "adsl_var",
        label = "ADSL selection",
        data_extract_spec = adsl_extract
      )
    )
  )
)
server <- function(input, output, session) {
  adsl_reactive_input <- data_extract_srv(
    id = "adsl_var",
    datasets = data_list,
    data_extract_spec = adsl_extract,
    join_keys = join_keys,
    select_validation_rule = sv_required("Please select a variable.")
  )

  iv_r <- reactive({
    iv <- InputValidator$new()
    iv$add_validator(adsl_reactive_input()$iv)
    iv$enable()
    iv
  })

  output$out1 <- renderPrint({
    if (iv_r()$is_valid()) {
      cat(format_data_extract(adsl_reactive_input()))
    } else {
      "Please fix errors in your selection"
    }
  })
}

if (interactive()) {
  shinyApp(ui, server)
}

# App: simplified data extraction
ui <- fluidPage(
  standard_layout(
    output = verbatimTextOutput("out1"),
    encoding = tagList(
      data_extract_ui(
        id = "adsl_var",
        label = "ADSL selection",
        data_extract_spec = adsl_extract
      )
    )
  )
)

server <- function(input, output, session) {
  adsl_reactive_input <- data_extract_srv(
    id = "adsl_var",
    datasets = data_list,
    data_extract_spec = adsl_extract
  )

  output$out1 <- renderPrint(adsl_reactive_input())
}

if (interactive()) {
  shinyApp(ui, server)
}

teal.transform documentation built on May 29, 2024, 5:06 a.m.