Package 'teal.slice'

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

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)
}

Description

An API class for managing filter states in a teal application's filter panel.

Details

The purpose of this class is to encapsulate the API of the filter panel in a new class FilterPanelAPI so that it can be passed and used in the server call of any module instead of passing the whole FilteredData object.

This class is supported by methods to set, get, remove filter states in the filter panel API.

Methods

Public methods

• FilterPanelAPI$new()

• FilterPanelAPI$get_filter_state()

• FilterPanelAPI$set_filter_state()

• FilterPanelAPI$remove_filter_state()

• FilterPanelAPI$clear_filter_states()

• FilterPanelAPI$clone()

Method new()

Initialize a FilterPanelAPI object.

Usage

FilterPanelAPI$new(datasets)

Arguments

Method get_filter_state()

Gets the reactive values from the active FilterState objects of the FilteredData object.

Gets all active filters in the form of a nested list. The output list is a compatible input to set_filter_state.

Usage

FilterPanelAPI$get_filter_state()

Returns

list with named elements corresponding to FilteredDataset objects with active filters.

Method set_filter_state()

Sets active filter states.

Usage

FilterPanelAPI$set_filter_state(filter)

Arguments

Returns

NULL, invisibly.

Method remove_filter_state()

Remove one or more FilterState of a FilteredDataset in the FilteredData object.

Usage

FilterPanelAPI$remove_filter_state(filter)

Arguments

Returns

NULL, invisibly.

Method clear_filter_states()

Remove all FilterStates of the FilteredData object.

Usage

FilterPanelAPI$clear_filter_states(datanames)

Arguments

Returns

NULL, invisibly.

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterPanelAPI$clone(deep = FALSE)

Arguments

Examples

library(shiny)

fd <- init_filtered_data(list(iris = iris))
fpa <- FilterPanelAPI$new(fd)

# get the actual filter state --> empty named list
isolate(fpa$get_filter_state())

# set a filter state
set_filter_state(
  fpa,
  teal_slices(
    teal_slice(dataname = "iris", varname = "Species", selected = "setosa", keep_na = TRUE)
  )
)

# get the actual filter state --> named list with filters
isolate(fpa$get_filter_state())

# remove all_filter_states
fpa$clear_filter_states()

# get the actual filter state --> empty named list
isolate(fpa$get_filter_state())

Description

To be used in ⁠Show R Code⁠ button.

Usage

get_filter_expr(datasets, datanames = datasets$datanames())

Arguments

Value

A character string containing all subset expressions.

Description

Function creates a FilteredData object.

Usage

init_filtered_data(x, join_keys = teal.data::join_keys(), code, check)

Arguments

Value

Object of class FilteredData.

Examples

datasets <- init_filtered_data(list(iris = iris, mtcars = mtcars))
datasets

Description

Create a teal_slice object that holds complete information on filtering one variable. Check out teal_slice-utilities functions for working with teal_slice object.

Usage

teal_slice(
  dataname,
  varname,
  id,
  expr,
  choices = NULL,
  selected = NULL,
  keep_na = NULL,
  keep_inf = NULL,
  fixed = FALSE,
  anchored = FALSE,
  multiple = TRUE,
  title = NULL,
  ...
)

Arguments

Details

teal_slice object fully describes filter state and can be used to create, modify, and delete a filter state. A teal_slice contains a number of common fields (all named arguments of teal_slice), some of which are mandatory, but only dataname and either varname or expr must be specified, while the others have default values.

Setting any of the other values to NULL means that those properties will not be modified (when setting an existing state) or that they will be determined by data (when creating new a new one). Entire object is FilterState class member and can be accessed with FilterState$get_state().

A teal_slice can come in two flavors:

1. teal_slice_var - this describes a typical interactive filter that refers to a single variable, managed by the FilterState class. This class is created when varname is specified. The object retains all fields specified in the call. id can be created by default and need not be specified.

2. teal_slice_expr - this describes a filter state that refers to an expression, which can potentially include multiple variables, managed by the FilterStateExpr class. This class is created when expr is specified. dataname and anchored are retained, fixed is set to TRUE, id becomes mandatory, title remains optional, while other arguments are disregarded.

A teal_slice can be passed FilterState/FilterStateExpr constructors to instantiate an object. It can also be passed to FilterState$set_state to modify the state. However, once a FilterState is created, only the mutable features can be set with a teal_slice: selected, keep_na and keep_inf.

Special consideration is given to two fields: fixed and anchored. These are always immutable logical flags that default to FALSE. In a FilterState instantiated with fixed = TRUE the features selected, keep_na, keep_inf cannot be changed. Note that a FilterStateExpr is always considered to have fixed = TRUE. A FilterState instantiated with anchored = TRUE cannot be removed.

Value

A teal.slice object. Depending on whether varname or expr was specified, the resulting teal_slice also receives class teal_slice_var or teal_slice_expr, respectively.

Filters in SumarizedExperiment and MultiAssayExperiment objects

To establish a filter on a column in a data.frame, dataname and varname are sufficient. MultiAssayExperiment objects can be filtered either on their colData slot (which contains subject information) or on their experiments, which are stored in the experimentList slot. For filters referring to colData no extra arguments are needed. If a filter state is created for an experiment, that experiment name must be specified in the experiment argument. Furthermore, to specify filter for an SummarizedExperiment one must also set arg ("subset" or "select", arguments in the subset() function for SummarizedExperiment) in order to determine whether the filter refers to the SE's rowData or colData.

Note

Date time objects of POSIX*t classes are printed as strings after converting to UTC timezone.

See Also

teal_slices, is.teal_slice, as.teal_slice, as.list.teal_slice, print.teal_slice, format.teal_slice

Examples

x1 <- teal_slice(
  dataname = "data",
  id = "Female adults",
  expr = "SEX == 'F' & AGE >= 18",
  title = "Female adults"
)
x2 <- teal_slice(
  dataname = "data",
  varname = "var",
  choices = c("F", "M", "U"),
  selected = "F",
  keep_na = TRUE,
  keep_inf = TRUE,
  fixed = FALSE,
  anchored = FALSE,
  multiple = TRUE,
  id = "Gender",
  extra_arg = "extra"
)

is.teal_slice(x1)
as.list(x1)
as.teal_slice(list(dataname = "a", varname = "var"))
format(x1)
format(x1, show_all = TRUE, trim_lines = FALSE)
print(x1)
print(x1, show_all = TRUE, trim_lines = FALSE)

Description

Create teal_slices object to package multiple filters and additional settings. Check out teal_slices-utilities functions for working with teal_slices object.

Usage

teal_slices(
  ...,
  exclude_varnames = NULL,
  include_varnames = NULL,
  count_type = NULL,
  allow_add = TRUE
)

Arguments

Details

teal_slices() collates multiple teal_slice objects into a teal_slices object, a complete filter specification. This is used by all classes above FilterState as well as filter_panel_api wrapper functions. teal_slices has attributes that modify the behavior of the filter panel, which are resolved by different classes.

include_varnames and exclude_varnames determine which variables can have filters assigned. The former enumerates allowed variables, the latter enumerates forbidden values. Since these could be mutually exclusive, it is impossible to set both allowed and forbidden variables for one data set in one teal_slices.

Value

teal_slices, which is an unnamed list of teal_slice objects.

See Also

• teal_slice for creating constituent elements of teal_slices

• teal::slices_store for robust utilities for saving and loading teal_slices in JSON format

• is.teal_slices, as.teal_slices, as.list.teal_slices, [⁠[.teal_slices⁠], c.teal_slices print.teal_slices, format.teal_slices

Examples

filter_1 <- teal_slice(
  dataname = "dataname1",
  varname = "varname1",
  choices = letters,
  selected = "b",
  keep_na = TRUE,
  fixed = FALSE,
  extra1 = "extraone"
)
filter_2 <- teal_slice(
  dataname = "dataname1",
  varname = "varname2",
  choices = 1:10,
  keep_na = TRUE,
  selected = 2,
  fixed = TRUE,
  anchored = FALSE,
  extra2 = "extratwo"
)
filter_3 <- teal_slice(
  dataname = "dataname2",
  varname = "varname3",
  choices = 1:10 / 10,
  keep_na = TRUE,
  selected = 0.2,
  fixed = TRUE,
  anchored = FALSE,
  extra1 = "extraone",
  extra2 = "extratwo"
)

all_filters <- teal_slices(
  filter_1,
  filter_2,
  filter_3,
  exclude_varnames = list(
    "dataname1" = "varname2"
  )
)

is.teal_slices(all_filters)
all_filters[1:2]
c(all_filters[1], all_filters[2])
print(all_filters)
print(all_filters, trim_lines = FALSE)