data_extract_multiple_srv: Creates a named list of 'data_extract_srv' output

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

Creates a named list of data_extract_srv output

Description

data_extract_multiple_srv loops over the list of data_extract given and runs data_extract_srv for each one returning a list of reactive objects.

Usage

data_extract_multiple_srv(data_extract, datasets, ...)

## S3 method for class 'reactive'
data_extract_multiple_srv(data_extract, datasets, ...)

## S3 method for class 'FilteredData'
data_extract_multiple_srv(data_extract, datasets, ...)

## S3 method for class 'list'
data_extract_multiple_srv(
  data_extract,
  datasets,
  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

data_extract	(named list of data_extract_spec objects) the list data_extract_spec objects. The names of the elements in the list need to correspond to the ids passed to data_extract_ui. See example for details.
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.
...	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 join keys per dataset in datasets.
select_validation_rule	(NULL or function or ⁠named list⁠ of function) Should there be any shinyvalidate input validation of the select parts of the data_extract_ui. If all data_extract require the same validation function then this can be used directly (i.e. select_validation_rule = shinyvalidate::sv_required()). For more fine-grained control use a list: select_validation_rule = list(extract_1 = sv_required(), extract2 = ~ if (length(.) > 2) "Error") If NULL then no validation will be added. See example for more details.
filter_validation_rule	(NULL or function or ⁠named list⁠ of function) Same as select_validation_rule but for the filter (values) part of the data_extract_ui.
dataset_validation_rule	(NULL or function or ⁠named list⁠ of function) Same as select_validation_rule but for the choose dataset part of the data_extract_ui

Value

reactive named list containing outputs from data_extract_srv(). Output list names are the same as data_extract input argument.

Examples

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

iris_select <- data_extract_spec(
  dataname = "iris",
  select = select_spec(
    label = "Select variable:",
    choices = variable_choices(iris, colnames(iris)),
    selected = "Sepal.Length",
    multiple = TRUE,
    fixed = FALSE
  )
)

iris_filter <- data_extract_spec(
  dataname = "iris",
  filter = filter_spec(
    vars = "Species",
    choices = c("setosa", "versicolor", "virginica"),
    selected = "setosa",
    multiple = TRUE
  )
)

data_list <- list(iris = reactive(iris))

ui <- fluidPage(
  useShinyjs(),
  standard_layout(
    output = verbatimTextOutput("out1"),
    encoding = tagList(
      data_extract_ui(
        id = "x_var",
        label = "Please select an X column",
        data_extract_spec = iris_select
      ),
      data_extract_ui(
        id = "species_var",
        label = "Please select 2 Species",
        data_extract_spec = iris_filter
      )
    )
  )
)

server <- function(input, output, session) {
  exactly_2_validation <- function(msg) {
    ~ if (length(.) != 2) msg
  }


  selector_list <- data_extract_multiple_srv(
    list(x_var = iris_select, species_var = iris_filter),
    datasets = data_list,
    select_validation_rule = list(
      x_var = sv_required("Please select an X column")
    ),
    filter_validation_rule = list(
      species_var = compose_rules(
        sv_required("Exactly 2 Species must be chosen"),
        exactly_2_validation("Exactly 2 Species must be chosen")
      )
    )
  )
  iv_r <- reactive({
    iv <- InputValidator$new()
    compose_and_enable_validators(
      iv,
      selector_list,
      validator_names = NULL
    )
  })

  output$out1 <- renderPrint({
    if (iv_r()$is_valid()) {
      ans <- lapply(selector_list(), function(x) {
        cat(format_data_extract(x()), "\n\n")
      })
    } else {
      "Please fix errors in your selection"
    }
  })
}

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

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