Introduction to teal.slice

Introduction

The teal.slice package provides a filter panel for shiny applications. teal.slice creates a filter panel module that allows for interactive filtering of data stored in data.frame and MultiAssayExperiment objects. It also displays filtered and unfiltered observation counts.

Information for users of the filter panel in applications

The filter panel contains three panels:

1. The top panel displays the number of records (observations) that remain after the filters have been applied. For relational data (where join_keys is specified), the number of unique subjects remaining in the filtered data set is also shown.
2. The middle panel displays the current active filters and allows the user to change their state.
3. The bottom panel allows to add new filters on columns of the data sets in the app.

Note that multiple filters for the same data set are combined with the AND operator and it is not possible to apply hierarchical filters.

An example of the filter panel in use is shown in the image below. The iris data set has the 50 versicolor records removed, leaving 100 observations. The mtcars data set 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 relational data (when join_keys are specified), filters specified on a parent data set (i.e. ADSL) will apply to all child data sets, whereas filters for a child data set can only be specified on columns not found in that data set’s parent. For general relational data the filter panel treats each data set independently, irrespective of relationships between data sets.

The filter panel also supports filtering MultiAssayExperiment objects, where filters can be applied to the subject data, which is stored in the colData slot and accessed with colData(MAE), as well as to experiments, which are stored in the ExperimentList slot and accessed with 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.

The filter panel is entirely contained within a FilteredData object. FilteredData is an R6 class that stores data sets, tracks filter states, constructs and applies filter calls, and can output filtered data. It also contains shiny modules (both UI and server functions) that make up the panel itself.

In a teal application the FilteredData object (datasets) is created automatically by teal’s init function and filter panel components are automatically placed on the right-hand side of the page.

Note that this example uses shiny::dataTableOutput and shiny::renderDataTable. This can lead to issues with Bootstrap, especially version 4. We recommend using DT::dataTableOutput and DT::renderDataTable if possible. See the shiny and DT documentation for more information.

To facilitate communication with FilteredData, we provide the filter panel API. The example below uses the set_filter_state function to set state and the state is specified with teal_slices and teal_slice functions. For details please see the Filter panel for developers vignette.

library(shiny)
library(teal.slice)

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

# setting initial state
set_filter_state(
  datasets = datasets,
  filter = teal_slices(
    teal_slice(dataname = "iris", varname = "Species", selected = "virginica", keep_na = FALSE),
    teal_slice(dataname = "mtcars", id = "4 cyl", title = "4 Cylinders", expr = "cyl == 4"),
    teal_slice(dataname = "mtcars", varname = "mpg", selected = c(20.0, 25.0), keep_na = FALSE, keep_inf = FALSE),
    include_varnames = list(iris = c("Species", "Sepal.Length")),
    exclude_varnames = list(mtcars = "cyl"),
    count_type = "all",
    allow_add = TRUE
  )
)

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"))
  )
)

## `shiny::dataTableOutput()` is deprecated as of shiny 1.8.1.
## Please use `DT::DTOutput()` instead.
## Since you have a suitable version of DT (>= v0.32.1), shiny::dataTableOutput() will automatically use DT::DTOutput() under-the-hood.
## If this happens to break your app, set `options(shiny.legacy.datatable = TRUE)` to get the legacy datatable implementation (or `FALSE` to squelch this message).
## See <https://rstudio.github.io/DT/shiny.html> for more information.
## `shiny::dataTableOutput()` is deprecated as of shiny 1.8.1.
## Please use `DT::DTOutput()` instead.
## Since you have a suitable version of DT (>= v0.32.1), shiny::dataTableOutput() will automatically use DT::DTOutput() under-the-hood.
## If this happens to break your app, set `options(shiny.legacy.datatable = TRUE)` to get the legacy datatable implementation (or `FALSE` to squelch this message).
## See <https://rstudio.github.io/DT/shiny.html> for more information.

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()) {
  shinyApp(ui, server)
}