Pre-release activities - vignettes review

[m7pr]

Part of #624 related to vignettes. Please make a PR to pre-release@main

[chlebowa]

The example apps in the vignettes are quite verbose, e.g.

very long example

# nolint start
data <- teal_data()
data <- within(data, {
  ADSL <- teal.modules.general::rADSL
  ADSL2 <- ADSL %>%
    mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1))
  ADRS <- teal.modules.general::rADRS
  ADTTE <- teal.modules.general::rADTTE
  ADLB <- teal.modules.general::rADLB %>%
    mutate(CHGC = as.factor(case_when(
      CHG < 1 ~ "N",
      CHG > 1 ~ "P",
      TRUE ~ "-"
    )))
})
datanames <- c("ADSL", "ADSL2", "ADRS", "ADTTE", "ADLB")
datanames(data) <- datanames
jk <- default_cdisc_join_keys[datanames]
jk_adsl2 <- jk
names(jk_adsl2)[names(jk_adsl2) == "ADSL"] <- "ADSL2"
jk <- c(jk, jk_adsl2)
jk["ADSL2", "ADSL"] <- c("USUBJID", "STUDYID")

# nolint end

app <- teal::init(
  data = data,
  modules = teal::modules(
    # tm_g_association ----
    modules(
      label = "Association plot",
      tm_g_association(
        label = "Single wide dataset",
        ref = teal.transform::data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADSL"]]),
            selected = "AGE",
            fixed = FALSE
          )
        ),
        vars = teal.transform::data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADSL"]]),
            selected = "BMRKR1",
            multiple = TRUE,
            fixed = FALSE
          )
        )
      ),
      tm_g_association(
        label = "Two wide datasets",
        ref = teal.transform::data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "STRATA1", "RACE")),
            selected = "STRATA1",
            multiple = FALSE,
            fixed = FALSE
          )
        ),
        vars = teal.transform::data_extract_spec(
          dataname = "ADSL2",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADSL2"]], c("AGE", "SEX", "RACE", "COUNTRY")),
            selected = c("AGE", "COUNTRY", "RACE"),
            multiple = TRUE,
            fixed = FALSE
          )
        )
      ),
      tm_g_association(
        label = "Multiple different long datasets",
        ref = teal.transform::data_extract_spec(
          dataname = "ADTTE",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADTTE"]]),
            selected = "AVAL",
            multiple = FALSE,
            fixed = FALSE
          ),
          filter = teal.transform::filter_spec(
            label = "Select endpoint:",
            vars = "PARAMCD",
            choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
            selected = c("PFS", "EFS"),
            multiple = TRUE
          )
        ),
        vars = teal.transform::data_extract_spec(
          dataname = "ADRS",
          reshape = TRUE,
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADRS"]], c("AVALC", "BMRKR1", "BMRKR2", "ARM")),
            selected = "AVALC",
            multiple = TRUE,
            fixed = FALSE
          ),
          filter = list(
            filter_spec(
              label = "Select endpoints:",
              vars = "PARAMCD",
              choices = value_choices(data[["ADRS"]], "PARAMCD", "PARAM"),
              selected = "BESRSPI",
              multiple = TRUE
            ),
            filter_spec(
              label = "Select endpoints:",
              vars = "AVISIT",
              choices = levels(data[["ADRS"]]$AVISIT),
              selected = "SCREENING",
              multiple = TRUE
            )
          )
        )
      ),
      tm_g_association(
        label = "Wide and long datasets",
        ref = teal.transform::data_extract_spec(
          dataname = "ADRS",
          select = select_spec(
            choices = variable_choices(data[["ADRS"]], c("AVAL", "AVALC")),
            selected = "AVALC",
            multiple = FALSE,
            fixed = FALSE,
            label = "Selected variable:"
          ),
          filter = list(
            filter_spec(
              vars = "PARAMCD",
              choices = value_choices(data[["ADRS"]], "PARAMCD", "PARAM"),
              selected = levels(data[["ADRS"]]$PARAMCD),
              multiple = TRUE,
              label = "Select response"
            ),
            filter_spec(
              vars = "AVISIT",
              choices = levels(data[["ADRS"]]$AVISIT),
              selected = levels(data[["ADRS"]]$AVISIT),
              multiple = TRUE,
              label = "Select visit:"
            )
          )
        ),
        vars = teal.transform::data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            choices = variable_choices(data[["ADSL"]], c("SEX", "AGE", "RACE", "COUNTRY", "BMRKR1", "STRATA1", "ARM")),
            selected = "AGE",
            multiple = TRUE,
            fixed = FALSE,
            label = "Select variable:"
          )
        )
      ),
      tm_g_association(
        label = "Same long datasets (same subsets)",
        ref = teal.transform::data_extract_spec(
          dataname = "ADRS",
          select = select_spec(
            choices = variable_choices(data[["ADRS"]]),
            selected = "AVALC",
            multiple = FALSE,
            fixed = FALSE,
            label = "Select variable:"
          )
        ),
        vars = teal.transform::data_extract_spec(
          dataname = "ADRS",
          select = select_spec(
            choices = variable_choices(data[["ADRS"]]),
            selected = "PARAMCD",
            multiple = TRUE,
            fixed = FALSE,
            label = "Select variable:"
          )
        )
      ),
      tm_g_association(
        label = "Same long datasets (different subsets)",
        ref = teal.transform::data_extract_spec(
          dataname = "ADLB",
          filter = list(
            filter_spec(
              vars = "PARAMCD",
              choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
              selected = levels(data[["ADLB"]]$PARAMCD)[1],
              multiple = FALSE,
              label = "Select lab:"
            ),
            filter_spec(
              vars = "AVISIT",
              choices = levels(data[["ADLB"]]$AVISIT),
              selected = levels(data[["ADLB"]]$AVISIT)[1],
              multiple = FALSE,
              label = "Select visit:"
            )
          ),
          select = select_spec(
            choices = variable_choices(data[["ADLB"]], c("AVAL", "CHG2", "PCHG2")),
            selected = "AVAL",
            multiple = FALSE
          )
        ),
        vars = teal.transform::data_extract_spec(
          dataname = "ADLB",
          filter = list(
            filter_spec(
              vars = "PARAMCD",
              choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
              selected = levels(data[["ADLB"]]$PARAMCD)[1],
              multiple = FALSE,
              label = "Select labs:"
            ),
            filter_spec(
              vars = "AVISIT",
              choices = levels(data[["ADLB"]]$AVISIT),
              selected = levels(data[["ADLB"]]$AVISIT)[1],
              multiple = FALSE,
              label = "Select visit:"
            )
          ),
          select = select_spec(
            choices = variable_choices(data[["ADLB"]]),
            selected = "STRATA1",
            multiple = TRUE
          )
        )
      )
    )
  )
)

What do you think about assigning modules as separate variables to make the init call more concise?

mod1 <- tm_g_association(label = "Single wide dataset" [...])
mod2 <- tm_g_association(label = "Two wide datasets" [...])

[...]

app <- init(
  data = data,
  modules = modules(
    # tm_g_association ----
    modules(
      label = "Association plot",
      mod1,
      mod2
[...]

[m7pr]

Yes, this makes a huge sense! +1

[chlebowa]

Another thought: do you think it would be beneficial to add options = list("launch.browser" = TRUE) to running the apps. Alternatively use runApp(app, launch.browser = TRUE)?

[m7pr]

I think runApp(app, launch.browser = TRUE) is cleaner. Good idea

[vedhav]

When running the example app from the vignette using-association-plot.Rmd (This is also noticed in examples from other vignettes too) Possibly due to incorrect data spec as @gogonzo mentioned.
There are a few modules that do not show output and display this error:

Cannot merge at least two dataset extracts. Make sure all datasets used for merging have appropriate keys.

[gogonzo]

When running the example app from the vignette using-association-plot.Rmd (This is also noticed in examples from other vignettes too) Possibly due to incorrect data spec as @gogonzo mentioned. There are a few modules that do not show output and display this error:

Cannot merge at least two dataset extracts. Make sure all datasets used for merging have appropriate keys.

Definitely data-extract/data-merge problem. We have something to debug here

[vedhav]

Changing the ADSL2 to ADSL fixes this. Looks like some parent-child relationship mapping is causing it.

[vedhav]

🤦🏽 The created join_keys were not being bounded to the teal_data object so the example app was not working.