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Package shinyfilter

What shinyfilter does

With shinyfilter you can link selectizeInput widgets to a reactable table and use them as filters for the columns of that table. All filters are interdependent: When you change the selection in one filter not only is the table updated, of course, but also will the available filter values in the other filters adjust to the new data selection; each filter will only show those values that are available given the current selection defined by all the filters together. This mimics the behavior of column filters in spreadsheet applications like Microsoft Excel or LibreOffice Calc.

How you install shinyfilter

Execute install.packages("shinyfilter", dependencies = TRUE) in the R console to install the package including all packages it depends on.

How you work with shinyfilter

Cookbook recipe for the impatient

In your user interface:

• add the selectizeInput widgets that will serve as filters for the reactable table; make sure they all have their onChange property set to the same input variable
• add the reactable table to present your data
• if you want to use tooltips or popovers to show the currently (un)available filter options (given the current filter selection in all filters together), call use_tooltips() (and change the appearance of the tooltips or popovers, if you like)

In your server function:

• call define_filters() to configure which selectizeInput widget will filter which column of your table
• handle the onChange event of the selectizeInput widgets with observeEvent():
  • call update_filters() to update the filter values; update_filters() will return the ‘new’, filtered dataframe. Ideally, this is captured in a reactive value so that the reactable updates automatically
  • if you want to work with tooltips or popovers, call update_tooltips()

Comprehensive tutorial

There is a couple of simple steps to run through when you use shinyfilters. In the following, the process is shown using an example with cars, a subset of the used cars dataset by Austin Reese. This is also the example used in the online help for shinyfilter. Let us start with the UI.

User interface

1. Create your UI as usual and place the reactable widget and the selectizeInput widgets for the filters on it. Make sure the selectizeInput widgets all have an event handler function for the onChange event (which is triggered everytime the selection in that widget changes). All your selectizeInput widgets should use the same event handler for the onChange event. To set up such an event binding easily you can use shinyfilter’s event() function which produces the required JavaScript code for you. The argument of event() is the name of the input value that you can process in the server function of your application using observeEvent() (more on that further down below).

   In our example, two filter widgets could then look like this:

   selectizeInput(inputId = "sel_manufacturer", label = "Manufacturer",
                  multiple = TRUE, options = list(onChange = event("ev_click")),
                  choices = sort(unique(cars$manufacturer)))
   
   selectizeInput(inputId = "sel_fuel", label = "Fuel",
                  multiple = TRUE, options = list(onChange = event("ev_click")),
                  choices = sort(unique(cars$fuel))),

2. If you want to use tooltips or popovers to show the user of your application the filter options that are currently not available (i.e. hidden) because they do not occur in the current selection that is shown in the reactable then you need to call use_tooltips() from the UI. Here you can specify the background (default: black) and foreground (default: white) colors, the textalignment (default: left), the fontsize (default: 100%) and the opacity (default: 0.8). A call of use_tooltips() could look like this:

   use_tooltips(background = "#1B3F8C", foreground = "#FFFFFF")

This is it. Now your UI is ready for shinyfilter. Let’s move on to the server function.

Server

In the server function you need to do three things:

1. Call define_filters() to bind the filters to the columns of the dataframe you are presenting in the reactable. The arguments of define_filters() are the following:

   • the input argument provided to the server function of your application
   • the inputId of the reactable
   • a named vector of the columns of the dataframe that will be filtered; the names of the vector elements are the inputIds of the selectizeInput widgets that represent the filters
   • the dataframe shown in the reactable

   A call of define_filters() in our example could look this (assuming, the dataframe which is presented in the reactable is called cars and the reactable itself is named tbl_cars):

   define_filters(input,
                 "tbl_cars",
                  c(sel_manufacturer = "manufacturer", 
                    sel_fuel = "fuel"),
                  cars)

2. An observeEvent() call to handle the filter event (ev_click in our example). In the expression to execute when the event is triggered (the handleExpr argument of observeEvent()) you need to call update_filters() with the input and session variables (the arguments of the server function), and the inputId of the reactable as arguments. update_filters() will return a filtered dataframe that can be used to update your reactable.

   In our example, the data for the reactable is stored in a reactive object r which had been created with:

   r <- reactiveValues(mycars = cars)

   The reactable is rendered based on this data:

   output$tbl_cars <- renderReactable({
     reactable(data = r$mycars,
               filterable = TRUE,
               rownames = FALSE,
               selection = "multiple",
               showPageSizeOptions = TRUE,
               paginationType = "jump",
               showSortable = TRUE,
               highlight = TRUE,
               resizable = TRUE,
               rowStyle = list(cursor = "pointer"),
               onClick = "select"
     )
   })    

   To update the reactable we only need to assign the return value of update_filters() to the reactive variable:

   r$mycars <- update_filters(input, session, "tbl_cars")

   So far, the observeEvent() call looks like this:

   observeEvent(input$ev_click, {
     r$mycars <- update_filters(input, session, "tbl_cars")
   })

3. If you want to use tooltips or popovers to show the hidden (currently not available) filter options then you need an additional call of update_tooltips() in observeEvent(). Here, you can specify if you want to show not only the unavailable but the available filter options as well (argument show_avail), how many filter options you want to show at most (arguments max.avail and max.nonavail - default for both is NULL which means all filter values are shown), how the available (title_avail) and unavailable (title_unavail) filter options shall be captioned, and what to show if the list of filter values exceeds max.avail/max.nonavail; default for the latter arguments (more.nonavail and more.avail) is "... (# more)" where # is a placeholder for the number of values not shown any more. You can provide any text you like and use # to show the number of filter options not listed in the tooltip/popover.

   If you want to show popovers instead of tooltips you need to set the tooltips argument of update_tooltips() to FALSE. In this case you can specify an additional popover_title.

   In our example, embedded in the observeEvent() call, this could look like this:

   observeEvent(input$ev_click, {
     r$mycars <- update_filters(input, session, "tbl_cars")
     update_tooltips("tbl_cars", 
                     session, 
                     tooltip = TRUE, 
                     title_avail = "Available is:", 
                     title_nonavail = "Currently not available is:",
                     max_avail = 10,
                     max_nonavail = 10)      
   })

Full code of the example application

This is how the application looks like (here, we use some more filters than just the two from above):

And here is the code:

library(shiny)
library(reactable)
library(shinyfilter)

cars_csv <- system.file("cars.csv", package="shinyfilter")

cars <- read.csv(cars_csv, stringsAsFactors = FALSE, header = TRUE, encoding = "UTF-8")

ui <- fluidPage(
                titlePanel("Cars Database"),
                sidebarLayout(
                  sidebarPanel(
                    width = 2,
                    
                    selectizeInput(inputId = "sel_manufacturer", label = "Manufacturer",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$manufacturer))),
                    
                    selectizeInput(inputId = "sel_year", label = "Year",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$year))),
                    
                    selectizeInput(inputId = "sel_fuel", label = "Fuel",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$fuel))),
                    
                    selectizeInput(inputId = "sel_condition", label = "Condition",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$condition))),
                    
                    selectizeInput(inputId = "sel_size", label = "Size",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$size))),
                    
                    selectizeInput(inputId = "sel_transmission", label = "Transmission",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$transmission))),
                    
                    selectizeInput(inputId = "sel_color", label = "Color",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$paint_color))),
                    
                    selectizeInput(inputId = "sel_type", label = "Type",
                                   multiple = TRUE, options = list(onChange = event("ev_click")),
                                   choices = sort(unique(cars$type))),
                   use_tooltips(background = "#1B3F8C", foreground = "#FFFFFF")
                  ),
                  mainPanel(
                    reactableOutput(outputId = "tbl_cars")
                  )
                )
)



server <- function(input, output, session) {
  
  r <- reactiveValues(mycars = cars)
  
  define_filters(input,
                 "tbl_cars",
                 c(sel_manufacturer = "manufacturer", 
                   sel_year = "year",
                   sel_fuel = "fuel",
                   sel_condition = "condition",
                   sel_size = "size",
                   sel_transmission = "transmission",
                   sel_color = "paint_color",
                   sel_type = "type"), 
                 cars)
  
  
  observeEvent(input$ev_click, {
    r$mycars <- update_filters(input, session, "tbl_cars")
    update_tooltips("tbl_cars", 
                    session, 
                    tooltip = TRUE, 
                    title_avail = "Available is:", 
                    title_nonavail = "Currently not available is:",
                    popover_title = "My filters",
                    max_avail = 10,
                    max_nonavail = 10)
  })
 

  output$tbl_cars <- renderReactable({
    reactable(data = r$mycars,
              filterable = TRUE,
              rownames = FALSE,
              selection = "multiple",
              showPageSizeOptions = TRUE,
              paginationType = "jump",
              showSortable = TRUE,
              highlight = TRUE,
              resizable = TRUE,
              rowStyle = list(cursor = "pointer"),
              onClick = "select"
    )
  })

}

shinyApp(ui = ui, server = server)

Contact the author

Joachim Zuckarelli

Twitter: [@jsugarelli](https://twitter.com/jsugarelli)

GitHub: https://github.com/jsugarelli/shinyfiler

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.