filter_state_api: Managing 'FilteredData' states

In teal.slice: Filter Module for 'teal' Applications

Managing FilteredData states

Description

Set, get and remove filter states of FilteredData object.

Usage

set_filter_state(datasets, filter)

get_filter_state(datasets)

remove_filter_state(datasets, filter)

clear_filter_states(datasets, force = FALSE)

Arguments

datasets	(FilteredData) object to store filter state and filtered datasets, shared across modules see FilteredData for details
filter	(teal_slices) specify filters in place on app start-up
force	(logical(1)) flag specifying whether to include anchored filter states.

Value

• ⁠set_*⁠, ⁠remove_*⁠ and clear_filter_state return NULL invisibly

• get_filter_state returns a named teal_slices object containing a teal_slice for every existing FilterState

See Also

teal_slice

Examples

datasets <- init_filtered_data(list(iris = iris, mtcars = mtcars))
fs <- teal_slices(
  teal_slice(dataname = "iris", varname = "Species", selected = c("setosa", "versicolor")),
  teal_slice(dataname = "iris", varname = "Sepal.Length", selected = c(5.1, 6.4)),
  teal_slice(dataname = "mtcars", varname = "gear", selected = c(4, 5)),
  teal_slice(dataname = "mtcars", varname = "carb", selected = c(4, 10))
)

# set initial filter state
set_filter_state(datasets, filter = fs)

# get filter state
get_filter_state(datasets)

# modify filter state
set_filter_state(
  datasets,
  teal_slices(
    teal_slice(dataname = "iris", varname = "Species", selected = "setosa", keep_na = TRUE)
  )
)

# remove specific filters
remove_filter_state(
  datasets,
  teal_slices(
    teal_slice(dataname = "iris", varname = "Species"),
    teal_slice(dataname = "mtcars", varname = "gear"),
    teal_slice(dataname = "mtcars", varname = "carb")
  )
)

# remove all states
clear_filter_states(datasets)


if (requireNamespace("MultiAssayExperiment", quietly = TRUE)) {
  # Requires MultiAssayExperiment from Bioconductor
  data(miniACC, package = "MultiAssayExperiment")

  datasets <- init_filtered_data(list(mae = miniACC))
  fs <- teal_slices(
    teal_slice(
      dataname = "mae", varname = "years_to_birth", selected = c(30, 50),
      keep_na = TRUE, keep_inf = FALSE
    ),
    teal_slice(
      dataname = "mae", varname = "vital_status", selected = "1",
      keep_na = FALSE
    ),
    teal_slice(
      dataname = "mae", varname = "gender", selected = "female",
      keep_na = TRUE
    ),
    teal_slice(
      dataname = "mae", varname = "ARRAY_TYPE", selected = "",
      keep_na = TRUE, experiment = "RPPAArray", arg = "subset"
    )
  )

  # set initial filter state
  set_filter_state(datasets, filter = fs)

  # get filter state
  get_filter_state(datasets)

  # modify filter state
  set_filter_state(
    datasets,
    teal_slices(
      teal_slice(dataname = "mae", varname = "years_to_birth", selected = c(40, 60))
    )
  )

  # remove specific filters
  remove_filter_state(
    datasets,
    teal_slices(
      teal_slice(dataname = "mae", varname = "years_to_birth"),
      teal_slice(dataname = "mae", varname = "vital_status")
    )
  )

  # remove all states
  clear_filter_states(datasets)
}

teal.slice documentation built on May 29, 2024, 1:39 a.m.