Using IDEAFilter
In IDEAFilter: Agnostic, Idiomatic Data Filter Module for Shiny

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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.

{style="position:center; width:100%"}

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.