Introduction to teal.slice

Introduction

teal.slice provides a filter panel for the teal framework. The filter panel lets application users add simple filters to teal applications enabling them to choose which data is to be considered for further analysis and it includes a summary showing the number of records being kept after applying the filters.

Information for users of the filter panel in applications

The filter panel contains three panels:

1. top panel showing the number of records (observations) remaining after the filters have been applied. For CDISC data, the number of unique subjects remaining in the filtered dataset is also shown.
2. middle panel showing the current active filters and these can be changed/removed here
3. bottom panel to allow new columns of the datasets to be added as filters

Note that multiple filters for the same dataset are combined using AND and there is no ability to apply hierarchical filters.

An example of the filter panel in use is shown in the image below. The iris dataset has the 50 versicolor records removed leaving 100 observations. The mtcars dataset has filtered out all records other than those with (gear = 3 or gear = 5) and (disp in [205, 295]).

Depending on the data type of the selected variable, different filters are shown, for example date pickers, range sliders, checkbox inputs.

For CDISC data, filtering records in a parent dataset (i.e. ADSL) will also apply the same filter to all child datasets and in child datasets it is only possible to filter columns not found in its parent’s dataset. For general relational data the filter panel treats each dataset independently irrespective of relationships between datasets. The filter panel also supports filtering MultiAssayExperiment data where filters can be applied on the subjects data level (colData(MAE)) and on each experiment level (MAE[["experiment name"]]).

Information for R developers using the filter panel

The example app below shows how to embed the filter panel inside a shiny application.

Note if you are using the teal package to create a teal application then the FilteredData object (datasets) does not need to be explicitly created, nor its UI explicitly added and it is automatically available inside teal module server functions.

library(teal.slice)

## Loading required package: shiny

library(shiny)

# create a FilteredData object
datasets <- init_filtered_data(
  list(
    iris = list(dataset = iris),
    mtcars = list(dataset = mtcars)
  )
)

app <- shinyApp(
  ui = fluidPage(
    fluidRow(
      column(
        width = 9,
        tabsetPanel(
          tabPanel(title = "iris", dataTableOutput("iris_table")),
          tabPanel(title = "mtcars", dataTableOutput("mtcars_table"))
        )
      ),
      # ui for the filter panel
      column(width = 3, datasets$ui_filter_panel("filter_panel"))
    )
  ),
  server = function(input, output, session) {
    # this is the shiny server function for the filter panel and the datasets
    # object can now be used inside the application
    datasets$srv_filter_panel("filter_panel")

    # get the filtered datasets and put them inside reactives for analysis
    iris_filtered_data <- reactive(datasets$get_data(dataname = "iris", filtered = TRUE))
    mtcars_filtered_data <- reactive(datasets$get_data(dataname = "mtcars", filtered = TRUE))

    output$iris_table <- renderDataTable(iris_filtered_data())
    output$mtcars_table <- renderDataTable(mtcars_filtered_data())
  }
)
if (interactive()) {
  runApp(app)
}

The FilteredData object (datasets above) provides additional methods (such as datasets$get_call(<dataname>) to get the code used to filter the data) and is also possible to programmatically set the state of the filter panel using the set_filter_state function for example:

set_filter_state(
  datasets,
  list(iris = list(Species = list(selected = c("setosa", "versicolor"))))
)

For details about these and other advanced functionality see the function documentation and the example application toward the end of the “Filter panel for NEST developers” vignette.