Extraction of the selector(s) details — data_extract

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

Usage

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

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

# S3 method for 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 teal.slice::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: (JoinKeys 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)

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

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

app <- shinyApp(
  ui = fluidPage(
    teal.widgets::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()) {
  runApp(app)
}

# Using FilteredData - Note this method will be deprecated
datasets <- teal.slice::init_filtered_data(
  list(ADSL = list(dataset = ADSL, keys = c("STUDYID", "USUBJID"), parent = character(0))),
  join_keys = teal.data::join_keys(
    teal.data::join_key("ADSL", "ADSL", c("USUBJID", "STUDYID"))
  ),
  cdisc = TRUE
)

app <- shinyApp(
  ui = fluidPage(
    teal.widgets::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 = datasets,
      data_extract_spec = adsl_extract
    )

    output$out1 <- renderPrint(adsl_reactive_input())
  }
)
if (interactive()) {
  runApp(app)
}