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CDISC Analysis Results Data

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**What changes are proposed in this pull request?** * Bug fix where `ard_stack(by)` argument was not passed to `ard_missing()` when `ard_stack(.missing=TRUE)`. (#244) Provide more detail here as needed. **Reference GitHub...

When we specify a `by` variable and `.missing=TRUE`, the missingness is not tabulated with the `by` arg specified. TODO: If `.overall=TRUE, .missing=TRUE`, check that the overall missing are also included...

It's useful to use `ard_stack()` without a `by` argument for the `.attributes` and `.missing` arguments. ``` r library(cards) packageVersion("cards") #> [1] '0.1.0.9023' ard_stack( data = ADSL, ard_categorical(variables = "AGEGR1"), ard_continuous(variables...

I think it may be a scoping/env issue? ``` r # first without attaching cards cards::ard_stack( data = cards::ADSL, by = "ARM", cards::ard_categorical(variables = "AGEGR1"), cards::ard_continuous(variables = "AGE") ) #>...

This can definitely be inferred from the ard_* results but requires post processing that users will have to do each time. For example, `ard_categorical(ADSL, variables = TRTA)` gives treatment group-level...

**What changes are proposed in this pull request?** * Style this entry in a way that can be copied directly into `NEWS.md`. (#, @) Provide more detail here as needed....

We have some code in `shuffle_ard()` to populate levels with "Overall < varname >" if a grouping variable is present but its level is NA (like with for overall stat...

If these are generics, then they could be extended to work with `survey` objects (currently, `cardx::ard_svycategorical()`. This could make downstream tabling would be easier. And we could also later extend...

I was recently QCing a table using cards. I am using `shuffle_ard()` because I like the way it renames the grouping variables and makes one column out of two (easier...