Implementing custom GUI filter layer

input

input is the function returning the UI structure of filter input controllers (the content visible when filter is enrolled in filtering panel).

It takes two argument:

• input_id - Concatenation of step_id and filter_id (plus namespace). Can be used as an input controller id (but doesn’t have to).
• cohort - The Cohort object.

Example of implementation (simplified version used for “discrete” filter):

.gui_filter.discrete <- function(filter, ...) {
  list(
    input = function(input_id, cohort) {
      shiny::tagList(
        .cb_input(
          do.call(
            shiny::checkboxGroupInput,
            discrete_input_params(filter, input_id, cohort, ...)
          ),
          filter$input_param
        ),
        .cb_input(
          keep_na_input(input_id, filter, cohort),
          "keep_na"
        )
      )
    },
    # other objects
  )
}

What we can realize, based on the construction, is that inside of the input function we can use not only its arguments but also filter object from the parent environment. This way we can access all the methods and metadata originally attached to the filter (i.e. its parameters via filter$get_params()).

As we can see the function returns a list (tagList) of two input controllers:

• checkboxGroupInput - responsible for setting up filter value,
• keep_na_input - helper function returning a checkbox responsible for inclusion/exclusion of NA values (value of keep_na filter argument).

The more important both input controllers are wrapped into .cb_input function.

Let’s first describe to meaning of .cb_input.

The function is responsible for registering any changes in input controller and updating the corresponding filter argument (with the value of controller). You may spot, we pass filter$input_param and "keep_na" as the second argument of .cb_input for checkboxGroupInput and keep_na_input respectively. The second argument informs shinyCohortBuilder via .cb_input which argument exactly should be updated in filter while changing the controller input.

This is the reason why do we keep the name of filter parameter responsible for data filtering (input_param) inside of the cohortBuilder’s filter (the name may differ for various filter types i.e. “value” for discrete filter, “range” for range filter).

The second effect of .cb_input is that input controllers wrapped into the function are sensitive only browser-site actions. That means filter arguments are updated only when user changes its value in the filtering panel - updating the input from server with update* function will affect only the controller visually.

Such effect is taken to assure no unneeded reactivity triggers are run. To make it more clear let’s consider a situation where filter panel consists of one discrete filter and we display post statistics nearby each checkbox option.

When changing selection in the filter we:

1. Update the filter value in Cohort.
2. Trigger data calculations.
3. Update post statistics for the filter in Cohort.
4. Update input controller choices having new statistics included.

The last step is taken by using update* function, what triggers shiny:inputchanged JS event that shinyCohortBuilder uses for updating filter arguments. Such behavior results with another round of the above steps what could end up with having reactivity loop. Using .cb_input assures shiny:inputchanged JS event doesn’t trigger another filter parameters update when called using update* method.

Let’s move now to describe further assumptions while writing custom input method for .gui_filter.

Looking at the below code sample:

do.call(
  shiny::checkboxGroupInput,
  discrete_input_params(filter, input_id, cohort, ...)
)

We can see internal function discrete_input_params is used to create a list of parameters sent to shiny::checkboxGroupInput.

While writing the function you should take into consideration the below assumptions:

1. When no data exists in the previous step the parameters should return an empty input controller.

For discrete filter (checkboxGroupInput) we achieve this with:

if (!cohort$get_cache(step_id, filter_id, state = "pre")$n_data) {
  return(
    list(inputId = input_id, choices = character(0), selected = character(0), label = NULL)
  )
}

where:

step_id <- filter$step_id
filter_id <- filter$id

It’s recommended to use cached statistics saved in the Cohort object to minimize the amount of operations. All the cached information for the specific filter are computed with get_stats filter method.

In case of discrete filter (for “tblist” source data type) it computes:

• choices - named list storing counts for each variable level,
• n_data - integer storing number of non-missing values,
• n_missing - integer storing number of NAs

2. When filter value is NA, the controller should select all the available options (or range).

You may get the current filter parameters with:

filter_params <- filter$get_params()

and value with:

filter_params[[filter$input_param]]

Extracting all the possible options can be done with:

names(
  cohort$get_cache(step_id, filter_id, state = "pre")$choices  
)

3. Filter controller choices should be based on the previous step.

This can be easily extracted using previous step cache:

names(
  cohort$get_cache(step_id, filter_id, state = "pre")$choices  
)

4. When value mapping is defined for the filter it should be used to convert choices labels accordingly.

You can check is value mapping is defined with:

!is.null(filter_params$value_mapping)

and access the value mapping function with:

cohort$get_source()$attributes$value_mappings[[filter_params$value_mapping]]

5. When the filters displays pre/post statistics (i.e. discrete filter does) you should assure the ones are included in according filter labels.

For this case you may use a helper .pre_post_stats (or .pre_post_stats_text) function:

.pre_post_stats_text(
  name = <choices labels names vector>,
  current = <current step statistics vector>,
  previous = <previous step statistics vector>,
  stats = <"pre, post", both or NULL> # it's recommended to use `stats = cohort$attributes$stats` to inherit the option from Cohort configuration
)

which returns labels with the selected statistics and brackets added.

The function returns labels in an HTML structure attaching proper classes to the current step labels. Such (HTML) class is used to grey out the label when working with Run Button feature.

.pre_post_stats_text(
  name = c("A", "B"),
  current = 1:2,
  previous = 3:4,
  stats = c("pre", "post")
)
#> <span>A (<span class = 'cb_delayed'>1</span> / 3)</span>
#> <span>B (<span class = 'cb_delayed'>2</span> / 4)</span>

6. The filter controller should not have any label attached.

For checkboxGroupInput (and most of shiny controllers) this can be achieved with label = NULL.

feedback

input is the function returning the UI structure of filter input controllers (the content visible when filter is enrolled in filtering panel).

It takes two argument:

• input_id - Concatenation of step_id and filter_id (plus namespace). Can be used as an input controller id (but doesn’t have to).
• cohort - The Cohort object.
• empty - FALSE by default. When NULL (feedback plot are turned off) feedback should return no value. When TRUE (no valid data exists in the previous filtering step, i.e. number of rows is zero) an empty plot should be returned (preferable with very small height).

The function should return list of three objects:

• plot_id - id of feedback plot output (preferably modification of input_id including namespace),
• output_fun - plot output UI placeholder function,
• render_fun - plot output rendering logic (excluding output assignment).

Example of implementation (simplified version used for “discrete” filter):

.gui_filter.discrete <- function(filter, ...) {
  list(
    feedback = function(input_id, cohort, empty = FALSE) {
      list(
        plot_id = shiny::NS(input_id, "feedback_plot") ,
        output_fun = ggiraph::girafeOutput,
        render_fun = if (!is.null(empty)) {
          ggiraph::renderGirafe({
            if(empty) { # when no data in parent step
              return(
                ggiraph::girafe(
                  ggobj      = ggplot2::ggplot(),
                  width_svg  = 10,
                  height_svg = 0.1
                )
              )
            }
            step_id <- filter$step_id
            filter_id <- filter$id

            filter_cache <- cohort$get_cache(step_id, filter_id, state = "pre")
            filter_value <- extract_selected_value(filter$get_params()$value, filter_cache$choices, FALSE)
            plot_data <- filter_cache$choices[filter_value]
            n_missing <- filter_cache$n_missing
            if (identical(filter$get_params()$keep_na, FALSE)) {
              n_missing <- 0
            }

            plot_feedback_bar(plot_data, n_missing)
          })
        }
      )
    },
    # other methods
  )
}

Details:

• render_fun should be wrapped within if (!is.null(empty)) {} if claim.
• Inside the rendering function body, return an empty plot when empty = TRUE.
• It’s recommended to generate plot based on cached Cohort statistics. The filter cache can be accessed with: cohort$get_cache(step_id, filter_id, state = "pre"), where:

step_id <- filter$step_id
filter_id <- filter$id

server

This is an optional method providing extra logic to filter controller in the application server. The function is called only once when filter controller is rendered.

In case of discrete filter, the function is used to extend logic for feedback plot. Whenever a field related to specific value is clicked on the plot it result with selecting the value in input controller.

So we define there an observer, that listens to plot click action and triggers accordingly “update_filter” action:

.gui_filter.discrete <- function(filter, ...) {
  list(
    server = function(input_id, input, output, session, cohort) {
      shiny::observeEvent(input[[shiny::NS(input_id, "feedback_plot_selected")]], {
        value <- input[[shiny::NS(input_id, "feedback_plot_selected")]]

        if (!is.na(value)) {
          .trigger_action(session, "update_filter", params = list(
            step_id = filter$step_id, filter_id = filter$id,
            input_name = filter$input_param, input_value = value,
            run_flow = FALSE
          ))
        }
      }, ignoreInit = TRUE) %>% .save_observer(input_id, session)
    },
    # other methods
  )
}

You may spot here a usage of .save_observer, to which the observer is passed. The method is used to save the observer in application memory, so that the observer is destroyed whenever the filter is removed.

The action is taken to prevent observers accumulation.

update

update is the function defining how filter input controllers should be updated based on the previous and current step data.

The method is called in the below scenarios:

• when previous filtering step was modified,
• when filter displays post statistics (i.e. discrete filer) and the current data changed (for example by making any changes in the current step),
• when filter GUI panel is using multiple input controllers and one of them has changed,
• when “Clear Step” button is pressed,
• when update_filter action was triggered directly (advanced case)

update function is strongly related to the input one. The parameters passed to related update* methods should follow the same assumptions as stated for input. More to that there is one extra rule regarding “Clear Step” scenario which we describe below.

The update function should have the below parameters defined:

• session - Shiny session object passed to update* methods.
• input_id - The same ID as passed to input.
• cohort - Cohort object.
• reset - If TRUE, the update method is called due to click of “Clear Step” button.

Below we present the sample of update method used for discrete filter:

.gui_filter.discrete <- function(filter, ...) {
  list(
    update = function(session, input_id, cohort, reset = FALSE, ...) {
      update_params <- discrete_input_params(filter, input_id, cohort, reset = reset, update = TRUE, ...)
      do.call(
        shiny::updateCheckboxGroupInput,
        append(
          list(session = session),
          update_params
        )
      )
      .update_keep_na_input(session, input_id, filter, cohort)
    },
    # other methods
  )
}

As you can see, we reuse discrete_input_params function for generating the parameters, with two extra arguments passed to it:

• reset - the information that states “Clear Step” was clicked,
• update - an indicator that states we generate parameters for update method.

Now let’s highlight how reset and update argument affect the returned parameters:

1. When reset = TRUE controller should select all the available options (or range).

Again, all the possible options can be extracted with:

names(
  cohort$get_cache(step_id, filter_id, state = "pre")$choices  
)

2. When update = TRUE the filter controller label should be cleared.

There is a small difference comparing to case when label is defined for initializing the controller. In the case label = NULL but for update the valid value id label = character(0).

So we only need to add:

label = if (update) character(0) else NULL

Looking at the code sample for update above we may realize, the usage of .update_keep_na_input. You can use the function whenever you use .keep_na_input in input function.

post_stats

Static logical value informing whether the filter can display post statistics. When TRUE, the filter will be updated (using attached update method) whenever any other filter is changed (to assure up to date value of post statistic).

multi_input

Logical value informing whether the filter GUI panel uses multiple input controllers to specify the filter value (range filter case when both slider and numeric input are used). When TRUE, shinyCohortBuilder will be informed to update input controller whenever the other one changes.