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changes for filter panel constuctor #670

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Jun 22, 2022
Merged

changes for filter panel constuctor #670

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865c01a
changes for filter panel constuctor
Jun 14, 2022
c4dcfbf
update
Jun 14, 2022
eddac99
fix r cmd check
Jun 15, 2022
238696e
Merge branch 'main' into 33_filteredData_constructor@main
Jun 15, 2022
4b2179f
Update NEWS.md
Jun 15, 2022
02ec68c
Merge branch 'main' into 33_filteredData_constructor@main
Jun 15, 2022
1946b4c
Update NEWS.md
Jun 16, 2022
1767023
Update NEWS.md
Jun 17, 2022
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2 changes: 2 additions & 0 deletions NEWS.md
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@@ -1,5 +1,7 @@
# teal 0.11.1.9000

* Updated internals of `module_teal` to reflect changes in `teal.slice` and `teal.data`.

# teal 0.11.1

### Enhancements
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5 changes: 2 additions & 3 deletions R/module_teal.R
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Expand Up @@ -186,9 +186,8 @@ srv_teal <- function(id, modules, raw_data, filter = list()) {
env$progress$set(0.25, message = "Setting data")
# create the FilteredData object (here called 'datasets') whose class depends on the class of raw_data()
# this is placed in the module scope so that bookmarking can be used with FilteredData object
datasets <- teal.slice:::filtered_data_new(raw_data())
# transfer the datasets from raw_data() into the FilteredData object
teal.slice:::filtered_data_set(raw_data(), datasets)
datasets <- do.call(teal.slice::init_filtered_data, raw_data()$get_teal_inputs())
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I'm not convinced that TealData should have method to return teal-ready list. I think the "converter" should be here in teal. TealData probably have all methods needed to extract data and attributes already.

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I'm not sure turning teal_data/cdisc_data into a format we need for teal.slice will probably be quite involved (and we need to then have explicit if statement for if you are cdisc or not) for this

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back then initialization of FilteredData was like this. We might have something similar in teal (as private method ofc). Not pushing though, let's see how TealData will be impacted in your proposition.

filtered_data_set.RelationalData <- function(data, datasets) { # nolintr # nousage

  datasets$set_code(data$get_code_class())

  for (dataset in data$get_datasets()) {
    dataname <- get_dataname(dataset)
    raw_dataset <- get_raw_data(dataset)
    datasets$set_data(dataname, raw_dataset)
    datasets$set_data_attrs(dataname, get_attrs(dataset))
    datasets$set_data_attr(dataname, "check", data$get_check_result())
  }

  datasets$set_join_keys(data$get_join_keys())

  return(invisible(NULL))
}


logger::log_trace("srv_teal@4 Raw Data transferred to FilteredData.")
datasets
})
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