[Bug]: `choices_labelled` are poorly encapsulated

## Faulty delayed filter_spec

It is not clear how des-engine handles dynamic specs. Output is unpredictable, see multiple scenarios:

### filter_spec(vars , selected )

In the code below valid des-code is presented. Following code produces filter:
- for dataset "ADRS"
- any factor variable (first selected on init)
- `choices` (factor levels) are resolved automatically when `vars ` `choices `

code

```r
data_extract_spec(
dataname = "ADRS",
filter = filter_spec(
label = "Select endpoints:",
vars = choices_selected(
choices = variable_choices("ADRS", subset = function(data) names(Filter(is.factor, data))),
selected = first_choice()
),
choices = NULL,
selected = NULL,
multiple = TRUE
)
)
```

### filter_spec(vars , selected )

When `choices` or `selected` is specified, then module fails to produce input, including input for `vars`, for example following code will produce following:

code

```r
data_extract_spec(
dataname = "ADRS",
filter = filter_spec(
label = "Select endpoints:",
vars = choices_selected(
choices = variable_choices("ADRS", subset = function(data) names(Filter(is.factor, data))),
selected = first_choice()
),
choices = value_choices(
"ADRS",
var_choices = "AGEU",
subset = function(data) unique(data$AGEU)[1]
),
selected = NULL,

selected = value_choices(
"ADRS",
var_choices = variable_choices("ADRS", subset = function(data) names(Filter(is.factor, data))[2])
),
multiple = TRUE
),
select = select_spec(
choices = variable_choices("ADRS", c("AVALC", "AVAL")),
selected = "AVALC",
multiple = FALSE
)
)
```

IMO this hierarchical resolve is unpredictable. I understand that `filter_spec(choices = NULL, selected = NULL)` means "default" specification.

-----

## Repeating dataset/variables is redundant and buggy

For example I can set `data_extract_spec(dataname = "ADSL", filter = select_spec(choices = variable_choices("ADRS")))`. This is crazy, that `variable_choices("ADRS")` "pulls" dataset instead of being given by "parent". This means that one can select different datasets in `data_extract_spec()` and in `variable_choices()`

```r
a = data_extract_spec(
dataname = "ADSL",
select = select_spec(
choices = variable_choices("ADRS"),
selected = NULL,
multiple = FALSE
)
)

```

## What to do

1. `variable_choices(data)` should be empty for delayed choices
2. `values_choices(data, var_choices)` both should be empty for delayed choices
3. `values_choices(subset)` should be a function of `column` not a data (resolve should hand over current variable set in `filter_spec(vars)`

-----

Check the app below and test locally different specifications of filter_spec/select_spec

testing app

```r
tm_extract <- function(label = "PR merge",
info = NULL,
dataname = NULL,
data_extract,
pre_output = NULL,
post_output = NULL) {
args <- as.list(environment())
args$data_extract_call <- styler::style_text(
strsplit(
# Line break after every brace
gsub(
"\\((d|s|f)", "(\n\\1",
# Line break after each function input value definition
gsub(
"\\,\\s([^\\=]+\\s\\=\\s)", ",\n\\1",
paste(capture.output(match.call()$data_extract), collapse = " ")
)
),
split = "\n"
)[[1]]
) |> paste(collapse = "\n")

mod <- module(
label = label,
server = srv_extract,
ui = ui_extract,
ui_args = args,
server_args = list(dataname = dataname, data_extract = data_extract, data_extract_call = args$data_extract_call),
datanames = "all"
)
attr(mod, "teal_bookmarkable") <- TRUE
mod
}

ui_extract <- function(id, ...) {
arguments <- list(...)
ns <- NS(id)
teal.widgets::standard_layout(
output = teal.widgets::white_small_well(
h4("data_extract_spec"),
verbatimTextOutput(ns("data_extract_spec")),
h4("data_extract output"),
verbatimTextOutput(ns("data_extract_out")),
h4("data merge expr"),
verbatimTextOutput(ns("merge_expr"))
),
encoding = div(
lapply(
names(arguments$data_extract),
function(i) {
data_extract_ui(
id = ns(i),
label = paste0("Selector ", i),
data_extract_spec = list(arguments$data_extract[[i]])
)
}
)
)
)
}

srv_extract <- function(input, output, session, data, dataname, data_extract, data_extract_call) {
selector_list <- teal.transform::data_extract_multiple_srv(data_extract, datasets = data)
merged_expr <- teal.transform::merge_expression_srv(selector_list = selector_list, data = data)
output$data_extract_spec <- renderText(data_extract_call)
output$data_extract_out <- renderText({
yaml::as.yaml(
lapply(
selector_list(),
function(x) x()[names(x()) != "iv"]
)
)
})
output$merge_expr <- renderText(paste(merged_expr()$expr, collapse = "\n"))
}

library(teal)
library(teal.transform)
# pkgload::load_all("temp/teal.transform")
library(scda)
library(dplyr)

data <- teal_data() |> within({
library(scda)
ADSL <- synthetic_cdisc_data("latest")$adsl
ADLB <- synthetic_cdisc_data("latest")$adlb
ADTTE <- synthetic_cdisc_data("latest")$adtte
ADRS <- synthetic_cdisc_data("latest")$adrs
ADLB <- mutate(ADLB, AVAL2 = 2 * AVAL)
ADTTE <- mutate(ADTTE, AVAL2 = 2 * AVAL)
})
join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADLB", "ADTTE", "ADRS")]

app <- init(
data = data,
modules = modules(
# single wide ----
modules(
label = "Single wide dataset",
tm_extract(
label = "Dynamic filter",
dataname = "ADSL",
data_extract = list(
a = data_extract_spec(
dataname = "ADSL",
filter = filter_spec(
label = "Select endpoints:",
vars = choices_selected(
choices = variable_choices("ADRS", subset = function(data) names(Filter(is.factor, data))),
selected = first_choice() # first is AGEU
),
choices = NULL,
selected = NULL,
# choices = value_choices(
# "ADRS",
# var_choices = "AGEU",
# subset = function(data) unique(data$AGEU)[1]
# ),
# choices = value_choices(
# "ADRS",
# var_choices = variable_choices("ADRS", subset = function(data) names(Filter(is.factor, data))[2])
# ),

# selected = value_choices(
# "ADRS",
# var_choices = variable_choices("ADRS", subset = function(data) names(Filter(is.factor, data))[1])
# ),
multiple = TRUE
),
select = select_spec(
choices = variable_choices("ADRS"),
selected = NULL,
multiple = FALSE
)
)
)
)
)
)
)

runApp(app)

```