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Managing FilteredData states

Source: R/filter_panel_api.R
filter_state_api.Rd

[Experimental]

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)
#> {
#>   "slices": [
#>     {
#>       "dataname"       : "iris",
#>       "varname"        : "Species",
#>       "id"             : "iris Species",
#>       "choices"        : ["setosa", "versicolor", "virgin...
#>       "selected"       : ["setosa", "versicolor"],
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true
#>     },
#>     {
#>       "dataname"       : "iris",
#>       "varname"        : "Sepal.Length",
#>       "id"             : "iris Sepal.Length",
#>       "choices"        : [4.2999999999999998, 7.900000000...
#>       "selected"       : [5.0999999999999996, 6.400000000...
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true
#>     },
#>     {
#>       "dataname"       : "mtcars",
#>       "varname"        : "gear",
#>       "id"             : "mtcars gear",
#>       "choices"        : ["3", "4", "5"],
#>       "selected"       : ["4", "5"],
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true
#>     },
#>     {
#>       "dataname"       : "mtcars",
#>       "varname"        : "carb",
#>       "id"             : "mtcars carb",
#>       "choices"        : [1, 8],
#>       "selected"       : [4, 8],
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true
#>     }
#>   ],
#>   "attributes": {
#>     "include_varnames" : {
#>       "iris"           : ["Sepal.Length", "Sepal.Width", ...
#>       "mtcars"         : ["mpg", "cyl", "disp", "hp", "dr...
#>     },
#>     "count_type"       : "none",
#>     "allow_add"        : true
#>   }
#> } 

# 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)


# 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)
#> {
#>   "slices": [
#>     {
#>       "dataname"       : "mae",
#>       "varname"        : "years_to_birth",
#>       "id"             : "mae years_to_birth",
#>       "choices"        : [14, 83],
#>       "selected"       : [30, 50],
#>       "keep_na"        : true,
#>       "keep_inf"       : false,
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true
#>     },
#>     {
#>       "dataname"       : "mae",
#>       "varname"        : "vital_status",
#>       "id"             : "mae vital_status",
#>       "choices"        : ["0", "1"],
#>       "selected"       : ["1"],
#>       "keep_na"        : false,
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true
#>     },
#>     {
#>       "dataname"       : "mae",
#>       "varname"        : "gender",
#>       "id"             : "mae gender",
#>       "choices"        : ["female", "male"],
#>       "selected"       : ["female"],
#>       "keep_na"        : true,
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true
#>     },
#>     {
#>       "dataname"       : "mae",
#>       "varname"        : "ARRAY_TYPE",
#>       "id"             : "mae ARRAY_TYPE RPPAArray subset..
#>       "choices"        : ["", "protein_level"],
#>       "selected"       : [""],
#>       "keep_na"        : true,
#>       "fixed"          : false,
#>       "anchored"       : false,
#>       "multiple"       : true,
#>       "arg"            : "subset",
#>       "experiment"     : "RPPAArray"
#>     }
#>   ],
#>   "attributes": {
#>     "include_varnames" : {
#>       "mae"            : ["patientID", "years_to_birth", ...
#>     },
#>     "count_type"       : "none",
#>     "allow_add"        : true
#>   }
#> } 

# 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)