Manages filtering of all datasets in the application or module.
The main purpose of this class is to provide a collection of reactive datasets, each dataset having a filter state that determines how it is filtered.
For each dataset, get_filter_expr returns the call to filter the dataset according
to the filter state. The data itself can be obtained through get_data.
The datasets are filtered lazily, i.e. only when requested / needed in a shiny app.
By design, any dataname set through set_dataset cannot be removed because
other code may already depend on it. As a workaround, the underlying
data can be set to NULL.
The class currently supports variables of the following types within datasets:
choices: variable of type factor, e.g. ADSL$COUNTRY, iris$Species
zero or more options can be selected, when the variable is a factor
logical: variable of type logical, e.g. ADSL$TRT_FLAG
exactly one option must be selected, TRUE or FALSE
ranges: variable of type numeric, e.g. ADSL$AGE, iris$Sepal.Length
numerical range, a range within this range can be selected
dates: variable of type Date, POSIXlt
Other variables cannot be used for filtering the data in this class.
Common arguments are:
filtered: whether to return a filtered result or not
dataname: the name of one of the datasets in this FilteredData object
varname: one of the columns in a dataset
new()Initialize a FilteredData object.
FilteredData$new(data_objects, join_keys = teal.data::join_keys())data_objects(named list)
List of data objects.
Names of the list will be used as dataname for respective datasets.
join_keys(join_keys) optional joining keys, see teal.data::join_keys().
datanames()Gets datanames.
set_available_teal_slices()Set list of external filter states available for activation.
Unlike adding new filter from the column, these filters can come with some prespecified settings.
teal_slices are wrapped in a reactive so they can be updated from elsewhere in the app.
Filters passed in x are limited to those that can be set for this FilteredData object,
i.e. they have the correct dataname and varname (waived teal_slice_fixed as they do not have varname).
List is accessible in ui/srv_active through ui/srv_available_filters.
get_available_teal_slices()Get list of filter states available for this object.
get_call()Gets a call to filter the dataset according to the filter state.
It returns a call to filter the dataset only, assuming the
other (filtered) datasets it depends on are available.
Together with self$datanames() which returns the datasets in the correct
evaluation order, this generates the whole filter code, see the function
FilteredData$get_filter_code.
For the return type, note that rlang::is_expression returns TRUE on the
return type, both for base R expressions and calls (single expression,
capturing a function call).
The filtered dataset has the name given by self$filtered_dataname(dataname)
This can be used for the Show R Code generation.
get_data()Gets filtered or unfiltered dataset.
For filtered = FALSE, the original data set with set_data is returned including all attributes.
get_filter_overview()Creates filter overview table to be displayed in the application.
One row is created per dataset, according to the get_filter_overview methods
of the contained FilteredDataset objects.
set_dataset()Adds a dataset to this FilteredData.
data(data.frame or MultiAssayExperiment)
data to be filtered.
dataname(character(1))
the name of the dataset to be added to this object.
set_dataset creates a FilteredDataset object which keeps dataset for the filtering purpose.
If this data has a parent specified in the join_keys object stored in private$join_keys
then created FilteredDataset (child) gets linked with other FilteredDataset (parent).
"Child" dataset return filtered data then dependent on the reactive filtered data of the
"parent". See more in documentation of parent argument in DataframeFilteredDataset constructor.
set_join_keys()Set the join_keys.
join_keys(join_keys), see teal.data::join_keys().
get_filter_state()Gets states of all contained FilterState objects.
format()Returns a formatted string representing this FilteredData object.
print()Prints this FilteredData object.
set_filter_state()Sets active filter states.
remove_filter_state()Removes one or more FilterState from a FilteredData object.
clear_filter_states()Remove all FilterStates of a FilteredDataset or all FilterStates of a FilteredData object.
ui_filter_panel()top-level shiny module for the filter panel in the teal app.
Contains 1) filter overview panel, 2) filter active panel, and 3) add filters panel.
srv_filter_panel()Server function for filter panel.
id(character(1))
shiny module instance id.
active_datanames(function or reactive)
returning datanames that should be shown on the filter panel.
Must be a subset of the datanames in this FilteredData.
If the function returns NULL (as opposed to character(0)),
the filter panel will be hidden.
ui_active()Server module responsible for displaying active filters.
srv_active()Server module responsible for displaying active filters.
ui_overview()Creates the UI definition for the module showing counts for each dataset contrasting the filtered to the full unfiltered dataset.
Per dataset, it displays the number of rows/observations in each dataset, the number of unique subjects.
srv_overview()Server function to display the number of records in the filtered and unfiltered data.
id(character(1))
shiny module instance id.
active_datanames(reactive)
returning datanames that should be shown on the filter panel,
must be a subset of the datanames argument provided to ui_filter_panel;
if the function returns NULL (as opposed to character(0)), the filter
panel will be hidden.
# use non-exported function from teal.slice
FilteredData <- getFromNamespace("FilteredData", "teal.slice")
library(shiny)
datasets <- FilteredData$new(list(iris = iris, mtcars = mtcars))
# get datanames
datasets$datanames()
#> [1] "iris" "mtcars"
datasets$set_filter_state(
teal_slices(teal_slice(dataname = "iris", varname = "Species", selected = "virginica"))
)
datasets$set_filter_state(
teal_slices(teal_slice(dataname = "mtcars", varname = "mpg", selected = c(15, 20)))
)
isolate(datasets$get_filter_state())
#> {
#> "slices": [
#> {
#> "dataname" : "iris",
#> "varname" : "Species",
#> "id" : "iris Species",
#> "choices" : ["setosa", "versicolor", "virgin...
#> "selected" : ["virginica"],
#> "fixed" : false,
#> "anchored" : false,
#> "multiple" : true
#> },
#> {
#> "dataname" : "mtcars",
#> "varname" : "mpg",
#> "id" : "mtcars mpg",
#> "choices" : [10.4, 34],
#> "selected" : [15, 20],
#> "fixed" : false,
#> "anchored" : false,
#> "multiple" : true
#> }
#> ],
#> "attributes": {
#> "include_varnames" : {
#> "iris" : ["Sepal.Length", "Sepal.Width", ...
#> "mtcars" : ["mpg", "cyl", "disp", "hp", "dr...
#> },
#> "count_type" : "none",
#> "allow_add" : true
#> }
#> }
isolate(datasets$get_call("iris"))
#> $filter
#> iris <- dplyr::filter(iris, Species == "virginica")
#>
isolate(datasets$get_call("mtcars"))
#> $filter
#> mtcars <- dplyr::filter(mtcars, mpg >= 15 & mpg <= 20)
#>
### set_filter_state
library(shiny)
data(miniACC, package = "MultiAssayExperiment")
datasets <- FilteredData$new(list(iris = iris, mae = miniACC))
fs <- teal_slices(
teal_slice(
dataname = "iris", varname = "Sepal.Length", selected = c(5.1, 6.4),
keep_na = TRUE, keep_inf = FALSE
),
teal_slice(
dataname = "iris", varname = "Species", selected = c("setosa", "versicolor"),
keep_na = FALSE
),
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"
)
)
datasets$set_filter_state(state = fs)
isolate(datasets$get_filter_state())
#> {
#> "slices": [
#> {
#> "dataname" : "iris",
#> "varname" : "Sepal.Length",
#> "id" : "iris Sepal.Length",
#> "choices" : [4.2999999999999998, 7.900000000...
#> "selected" : [5.0999999999999996, 6.400000000...
#> "keep_na" : true,
#> "keep_inf" : false,
#> "fixed" : false,
#> "anchored" : false,
#> "multiple" : true
#> },
#> {
#> "dataname" : "iris",
#> "varname" : "Species",
#> "id" : "iris Species",
#> "choices" : ["setosa", "versicolor", "virgin...
#> "selected" : ["setosa", "versicolor"],
#> "keep_na" : false,
#> "fixed" : false,
#> "anchored" : false,
#> "multiple" : true
#> },
#> {
#> "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" : {
#> "iris" : ["Sepal.Length", "Sepal.Width", ...
#> "mae" : ["patientID", "years_to_birth", ...
#> },
#> "count_type" : "none",
#> "allow_add" : true
#> }
#> }