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Add two example apps to showcase DDL and Custom transformation #177

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@vedhav vedhav commented Aug 9, 2024

Adds delayed-data and custom-transform app.

Note that it will take a while for this branch to be merged to the main depending on the release of the teal_transform_module feature.

Additionally, after the UI-refactor the apps have to be re-recorded.

@vedhav vedhav added the core label Aug 9, 2024
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@@ -78,7 +93,7 @@ jobs:
- name: Checkout repo 🛎
uses: actions/checkout@v4
with:
ref: "${{ env.BRANCH_NAME }}"
ref: "669_insertUI@dev"
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Suggested change
ref: "669_insertUI@dev"
ref: "${{ env.BRANCH_NAME }}"

Should revert the branch checkout before merging. This was changed just to test the deployment.

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vedhav commented Aug 13, 2024

Both the custom-transform and delayed-data apps are deployed on dev channel for testing.

@kumamiao
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@vedhav Is it possible to make the output of the custom-transform a bit more customized so that the output panel is more insightful when merging ADSL with ADTTE? i.e., only selecting several meaningful variables (i.e., ADSL: USUBJID, ARM, SEX, BMRKR1; ADTTE: USUBJID, PARAM, AVAL, AVALU, CNSR, EVNTDESC) from both data before merging?

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vedhav commented Sep 27, 2024

Sure @kumamiao, thanks for the idea!
I have a couple of quick questions before I push the changes.

  1. Would you prefer to select the columns from a single dropdown (containing all the columns) or separate dropdowns (with columns from the respective datasets)
Screenshot 2024-09-27 at 10 36 09 AM Screenshot 2024-09-27 at 10 47 21 AM 2. Do you want to sort the column names for select options or leave them in the same order as the datasets?

CO2[factors] <- lapply(CO2[factors], as.character)
})
join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADTTE")]
teal.data::datanames(data) <- c("ADSL", "ADTTE", "iris", "CO2")
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no datanames() anymore ;)

Comment on lines +45 to +65
filters <- teal_slices(
teal_slice(dataname = "mtcars", varname = "gear", multiple = FALSE),
teal_slice(dataname = "iris", varname = "Species"),
teal_slice(dataname = "iris", varname = "Sepal.Length"),
teal_slice(
dataname = "iris", varname = "Species3",
fixed = TRUE, anchored = FALSE
),
teal_slice(
dataname = "iris", varname = "Species4",
fixed = FALSE, anchored = TRUE, multiple = TRUE
),
teal_slice(
dataname = "iris", varname = "Species5",
fixed = TRUE, anchored = TRUE
),
teal_slice(
dataname = "iris", id = "custom_expr", title = "Custom Expression",
expr = "Sepal.Width > 2.5 & Petal.Length > 1.5",
)
)
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I think filters in context of ddl are irrelevant. There are no filters in custom-transform app you've proposed in this PR and they are comparably irrelevant in both cases. I'd focus on specific functionality rather than showing combination of them - combination is showcased in apps like efficacy or others.

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3 participants