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Using IDEAFilter

Minimal Example

Here is a minimal example using IDEAFilter_ui() and IDEAFilter() to explore a data set:

library(shiny)
library(IDEAFilter)
library(dplyr)
shinyApp(
  ui = fluidPage(
    titlePanel("Filter Data Example"),
    fluidRow(
      column(8, dataTableOutput("data_summary")),
      column(4, IDEAFilter_ui("data_filter")))),
  server = function(input, output, session) {
    filtered_data <- IDEAFilter("data_filter", data = iris, verbose = FALSE)
    output$data_summary <- 
      renderDataTable(filtered_data(), 
    options = list(scrollX = TRUE, pageLength = 5))
  }
)

The server side of the module returns the reactive ShinyDataFilter_df object which includes the filtered data frame and the code used to filter it as an attribute.

A Larger Example

With the release of IDEAFilter() to replace the deprecated shiny_data_filter(), a couple more arguments have been introduced to enhance the functionality of the filter.

Column Sub-setting: restricting the columns a user can add to the filter.
Pre-selection: pre-specifying a collection of filters to either pre-load in the filter or for users to dynamically apply.

To explore these features we can run the following example application:

library(shiny)
library(IDEAFilter)
app <- system.file("examples", "starwars_app", package = "IDEAFilter")
runApp(app)

Column Sub-setting

In the application you can freely select a subset of columns to include in the filter. The col_subset argument can be set in development of an application or can be a reactive variable in deployment. You should note these columns can still be set using pre-selection and will still be applied to the filter. For instance, you can see below that only height has been selected but gender is still being applied.

Pre-selection

The application comes with two choices to apply pre-selection:

Gender listed as feminine and height greater than 180 cm
Character is a droid (excluding NAs) and has a mass less than 50 kg

Looking at the second example is informative on how a developer can create their own pre-selections.

list(
  is_droid = list(filter_na = TRUE, filter_fn = ~ isTRUE(.x)),
  mass = list(filter_fn = ~ .x < 50))
)

The argument preselection is a named list where the names correspond to column names in the data set and the elements are lists containing the elements filter_na and filter_fn. The missing values (i.e. NAs) will be filtered if filter_na is set to TRUE. The filter_fn element can either be a formula or a function. The filter will attempt to apply the function to the data set when populating the initial values.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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