POC Redesign of teal.reporter

[m7pr]

Companion to

• teal.reporter/redesign@main
• teal/redesign@main
• teal.code/redesign@main

Covered modules

• tm_a_pca @m7pr
• tm_a_regression @m7pr (needs cleanup as is used to test multiple artificial outputs)
• tm_data_table
• tm_file_viewer
• tm_front_page
• tm_g_association
• tm_g_bivariate
• tm_g_distribution
• tm_g_response
• tm_g_scatterplot @m7pr
• tm_g_scatterplotmatrix
• tm_missing_data
• tm_outliers
• tm_t_crosstable
• tm_variable_browser

code

devtools::load_all('../teal.reporter')
devtools::load_all('../teal.widgets')
devtools::load_all('../teal.code')
devtools::load_all('../teal')
devtools::load_all('.')

# general data example
data <- teal_data()
data <- within(data, {
  require(nestcolor)
  CO2 <- CO2
  USArrests <- USArrests
})

app <- init(
  data = data,
  modules = modules(
    tm_a_regression(
      label = "Regression",
      response = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = "uptake",
          selected = "uptake",
          multiple = FALSE,
          fixed = TRUE
        )
      ),
      regressor = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["CO2"]], c("conc", "Treatment")),
          selected = "conc",
          multiple = TRUE,
          fixed = FALSE
        )
      )
    ),
    tm_a_pca(
      "PCA",
      dat = data_extract_spec(
        dataname = "USArrests",
        select = select_spec(
          choices = variable_choices(
            data = data[["USArrests"]], c("Murder", "Assault", "UrbanPop", "Rape")
          ),
          selected = c("Murder", "Assault"),
          multiple = TRUE
        ),
        filter = NULL
      )
    ),
    tm_g_scatterplot(
      label = "Scatterplot Choices",
      x = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("conc", "uptake")),
          selected = "conc",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      y = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("conc", "uptake")),
          selected = "uptake",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      color_by = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(
            data[["CO2"]],
            c("Plant", "Type", "Treatment", "conc", "uptake")
          ),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      size_by = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("conc", "uptake")),
          selected = "uptake",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      row_facet = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      col_facet = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      )
    ),
    tm_g_bivariate(
      x = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]]),
          selected = "conc",
          fixed = FALSE
        )
      ),
      y = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]]),
          selected = "uptake",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      row_facet = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]]),
          selected = "Type",
          fixed = FALSE
        )
      ),
      col_facet = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]]),
          selected = "Treatment",
          fixed = FALSE
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server, enableBookmarking = "server")
}

[donyunardi]

Viewing the structure of the report document for a module

Currently, users have to look at the source code or inspect the server element to understand the structure of card_fun in a module, in order to see how the report document is structured for a teal module.

Example:

r$> x <- my_custom_module()

r$> body(x$server)
{
    moduleServer(id, function(input, output, session) {
        shiny::updateSelectInput(inputId = "datasets", choices = datanames(data()))
        observeEvent(input$datasets, {
            req(input$datasets)
            only_numeric <- sapply(data()[[input$datasets]], 
                is.numeric)
            shiny::updateSelectInput(inputId = "variables", choices = names(data()[[input$datasets]])[only_numeric])
        })
        result <- reactive({
            req(input$datasets)
            req(input$variables %in% names(data()[[input$datasets]]))
            new_data <- within(data(), {
                my_plot <- ggplot(input_dataset, aes(x = input_vars)) + 
                  geom_histogram(binwidth = input_binwidth, fill = "skyblue", 
                    color = "black")
            }, input_dataset = as.name(input$datasets), input_vars = as.name(input$variables), 
                input_binwidth = input$binwidth, label = "plot")
            new_data
        })
        output$plt <- shiny::renderPlot({
            result()[["my_plot"]]
        })
        teal.widgets::verbatim_popup_srv(id = "rcode", verbatim_content = reactive(get_code(result())), 
            title = "Code to reproduce the analysis")
        plot_code_r <- pull_code(result, labels = "plot")
        card_fun <- reactive({         ###### <---- This is the part that user will have to look for
            teal.reporter::report_document("## Plot", teal.reporter::code_chunk(plot_code_r()), 
                result()[["my_plot"]])
        })
        list(report_card = card_fun)
    })
}

Is it possible to provide a better way for users to view the current structure of the reporter for a module?
Can we extend our format.teal_module method to include this information?

[gogonzo]

Suggested change

	"If you want the object to be kept in the report as loaded from .rds use keep_in_report()",
	"If you want the object to be kept in the rendered output (pdf, pptx, etc.) use keep_in_report()",

For me high-level explaination is. If head(mtcars) |> keep_in_report(FALSE):

• it will be included in the Reporter object (because it is in the list of items provided to report_document)
• will be shown when previewing the results in the report_previewer (to avoid double evaluation of the code chunk)
• will be included in rendered in pdf, pptx, ... files directly to avoid double evaluation of the code chunk
• Will be excluded from saved .Rmd file content because .Rmd doesn't need to include a rendered output - output will be rendered when runnning .Rmd file

If keep_in_report(TRUE) - then always included

[m7pr]

Yes, your assumptions are right. I wonder if that's gonna be easy to grasp for the final user 🤔