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Filter Panel

NEST CoreDev

teal apps with the filter panel

The filter panel is an integral part of all teal applications and is included on the right side. Based on the selections made in the filter panel, filter expressions are executed before passing data to teal modules. The technical details of the filter panel are extensively described in teal.slice documentation.

By default, init initializes the filter panel without any active filters but allows the user to add filters on any column. To start a teal application with predefined filters, one must specify the filter argument. In the following example four filters are specified using the teal_slice function and wrapped together with teal_slices.

library(teal)

app <- init(
  data = teal_data(IRIS = iris, CARS = mtcars),
  modules = example_module(),
  filter = teal_slices(
    teal_slice(dataname = "IRIS", varname = "Sepal.Length"),
    teal_slice(dataname = "IRIS", varname = "Species", selected = "setosa"),
    teal_slice(dataname = "CARS", varname = "mpg", selected = c(20, Inf)),
    teal_slice(dataname = "CARS", expr = "qsec < 20", title = "1/4 mile under 20 sec", id = "qsec_20")
  )
)

if (interactive()) {
  shinyApp(app$ui, app$server)
}

Extending teal.slice

Filter panel respective to teal_module

Each teal_module (see ?module) object contains the datanames attribute that determines which data sets are to be sent to that module. The filter panel will display only those data sets and hide the rest when this module is active.

library(teal)

app <- init(
  data = teal_data(IRIS = iris, CARS = mtcars),
  modules = modules(
    example_module(label = "all datasets"),
    example_module(label = "IRIS only", datanames = "IRIS"),
    example_module(label = "CARS only", datanames = "CARS"),
    example_module(label = "no filter panel", datanames = NULL)
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Global and module specific filter panel

teal contains the teal_slices function that extends the original teal_slices found in teal.slice by adding two arguments: module_specific and mapping. By default init initializes the app with a “global” filter panel, where all modules use the same filters. Setting module_specific = TRUE switches to a “module-specific” filter panel, where each module can have a different set of filters active at any time. It is still possible to set global filters that will be shared among modules.

One possible scenario is depicted in the figure below:

To achieve the described setup, one must set the module_specific argument to TRUE and use the mapping argument to match filters to modules. mapping takes a named list where element names correspond to module labels, and elements are vectors of teal_slice ids applied to that module at startup. teal_slices listed the element called "global_filters" will be applied to all modules.

For a detailed explanation about the filter states, see this teal.slice vignette.

library(teal)

app <- init(
  data = teal_data(mtcars = mtcars),
  modules = modules(
    example_module(label = "module 1"),
    example_module(label = "module 2"),
    example_module(label = "module 3"),
    example_module(label = "module 4")
  ),
  filter = teal_slices(
    # filters created with id
    teal_slice(dataname = "mtcars", varname = "mpg", id = "filter 1"),
    teal_slice(dataname = "mtcars", varname = "cyl", id = "filter 2"),
    teal_slice(dataname = "mtcars", varname = "disp", id = "filter 3"),
    teal_slice(dataname = "mtcars", varname = "hp", id = "filter 4"),
    teal_slice(dataname = "mtcars", varname = "drat", id = "filter 5"),
    teal_slice(dataname = "mtcars", varname = "wt", id = "filter 6"),
    # module-specific filtering enabled
    module_specific = TRUE,
    # filters mapped to modules
    mapping = list(
      "module 1" = c("filter 2", "filter 4"),
      "module 2" = "filter 3",
      "module 3" = "filter 2",
      global_filters = "filter 1"
    )
  )
)

if (interactive()) {
  shinyApp(app$ui, app$server)
}

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.
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