tm_g_scatterplotmatrix: 'teal' module: Scatterplot matrix

In teal.modules.general: General Modules for 'teal' Applications

teal module: Scatterplot matrix

Description

Generates a scatterplot matrix from selected variables from datasets. Each plot within the matrix represents the relationship between two variables, providing the overview of correlations and distributions across selected data.

Usage

tm_g_scatterplotmatrix(
  label = "Scatterplot Matrix",
  variables = list(teal.picks::picks(teal.picks::datasets(),
    teal.picks::variables(selected = seq(1L, 5L), multiple = TRUE))),
  min_n_variables = 2L,
  max_n_variables = 5L,
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

label	(character(1)) Label shown in the navigation item for the module or module group. For modules() defaults to "root". See Details.
variables	(picks or list of picks) Specifies plotting variables from an incoming dataset with filtering and selecting. In case of picks use teal.picks::variables(..., ordered = TRUE) if plot elements should be rendered according to selection order.
min_n_variables	(integer(1)) Minimum number of variables that must be selected.
max_n_variables	(integer(1)) Maximum number of variables that can be selected.
plot_height	(numeric) optional, specifies the plot height as a three-element vector of value, min, and max intended for use with a slider UI element.
plot_width	(numeric) optional, specifies the plot width as a three-element vector of value, min, and max for a slider encoding the plot width.
pre_output	(shiny.tag) optional, text or UI element to be displayed before the module's output, providing context or a title. with text placed before the output to put the output into context. For example a title.
post_output	(shiny.tag) optional, text or UI element to be displayed after the module's output, adding context or further instructions. Elements like shiny::helpText() are useful.
transformators	(list of teal_transform_module) that will be applied to transform module's data input. To learn more check vignette("transform-input-data", package = "teal").
decorators	(named list of lists of teal_transform_module) optional, decorator for tables or plots included in the module output reported. The decorators are applied to the respective output objects.

Value

Object of class teal_module to be used in teal applications.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot - a patchwork assembled from individual ggplot panels)

A Decorator is applied to the specific output using a named list of teal_transform_module objects. The name of this list corresponds to the name of the output to which the decorator is applied. See code snippet below:

tm_g_scatterplotmatrix(
   ..., # arguments for module
   decorators = list(
     plot = teal_transform_module(...) # applied to the `plot` output
   )
)

For additional details and examples of decorators, refer to the vignette vignette("decorate-module-output", package = "teal.modules.general").

To learn more please refer to the vignette vignette("transform-module-output", package = "teal") or the teal::teal_transform_module() documentation.

Reporting

This module returns an object of class teal_module, that contains a server function. Since the server function returns a teal_report object, this makes this module reportable, which means that the reporting functionality will be turned on automatically by the teal framework.

For more information on reporting in teal, see the vignettes:

• vignette("reportable-shiny-application", package = "teal.reporter")

• vignette("adding-support-for-reporting-to-custom-modules", package = "teal")

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

Note

For more examples, please see the vignette "Using scatterplot matrix" via vignette("using-scatterplot-matrix", package = "teal.modules.general").

When Add Correlation is enabled, a simple Omit NAs checkbox controls NA handling (checked = "pairwise.complete.obs", matching the historical default). Unchecking it reveals a dropdown with all five stats::cor() use options for advanced control.

Examples

# general data example
data <- teal_data()
data <- within(data, {
  countries <- data.frame(
    id = c("DE", "FR", "IT", "ES", "PT", "GR", "NL", "BE", "LU", "AT"),
    government = factor(
      c(2, 2, 2, 1, 2, 2, 1, 1, 1, 2),
      labels = c("Monarchy", "Republic")
    ),
    language_family = factor(
      c(1, 3, 3, 3, 3, 2, 1, 1, 3, 1),
      labels = c("Germanic", "Hellenic", "Romance")
    ),
    population = c(83, 67, 60, 47, 10, 11, 17, 11, 0.6, 9),
    area = c(357, 551, 301, 505, 92, 132, 41, 30, 2.6, 83),
    gdp = c(3.4, 2.7, 2.1, 1.4, 0.3, 0.2, 0.7, 0.5, 0.1, 0.4),
    debt = c(2.1, 2.3, 2.4, 2.6, 2.3, 2.4, 2.3, 2.4, 2.3, 2.4)
  )
  sales <- data.frame(
    id = 1:50,
    country_id = sample(
      c("DE", "FR", "IT", "ES", "PT", "GR", "NL", "BE", "LU", "AT"),
      size = 50,
      replace = TRUE
    ),
    year = sort(sample(2010:2020, 50, replace = TRUE)),
    venue = sample(c("small", "medium", "large", "online"), 50, replace = TRUE),
    cancelled = sample(c(TRUE, FALSE), 50, replace = TRUE),
    quantity = rnorm(50, 100, 20),
    costs = rnorm(50, 80, 20),
    profit = rnorm(50, 20, 10)
  )
})
join_keys(data) <- join_keys(
  join_key("countries", "countries", "id"),
  join_key("sales", "sales", "id"),
  join_key("countries", "sales", c("id" = "country_id"))
)

app <- init(
  data = data,
  modules = modules(
    tm_g_scatterplotmatrix(
      label = "Scatterplot matrix",
      variables = list(
        teal.picks::picks(
          datasets("countries"),
          teal.picks::variables(
            choices = tidyselect::everything(),
            selected = c("area", "gdp", "debt"),
            multiple = TRUE,
            ordered = TRUE
          ),
          teal.picks::values()
        ),
        teal.picks::picks(
          datasets("sales"),
          teal.picks::variables(
            choices = c("quantity", "costs", "profit"),
            selected = c("quantity", "costs"),
            multiple = TRUE,
            ordered = TRUE
          )
        )
      ),
      transformators = list(
        teal_transform_filter(
          teal.picks::picks(
            datasets("sales"),
            teal.picks::variables("country_id"),
            teal.picks::values()
          )
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# CDISC data example
data <- teal_data()
data <- within(data, {
  ADSL <- teal.data::rADSL
  ADRS <- teal.data::rADRS
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

app <- init(
  data = data,
  modules = modules(
    tm_g_scatterplotmatrix(
      label = "Scatterplot matrix",
      variables = list(
        teal.picks::picks(
          datasets("ADSL"),
          teal.picks::variables(
            choices = tidyselect::everything(),
            selected = c("AGE", "RACE", "SEX"),
            multiple = TRUE,
            ordered = TRUE,
            fixed = FALSE
          ),
          teal.picks::values()
        ),
        teal.picks::picks(
          datasets("ADRS"),
          teal.picks::variables(
            choices = tidyselect::everything(),
            selected = c("AVAL", "ADY"),
            multiple = TRUE,
            ordered = TRUE,
            fixed = FALSE
          )
        )
      ),
      transformators = list(
        teal_transform_filter(
          teal.picks::picks(
            teal.picks::datasets("ADRS"),
            teal.picks::variables("PARAMCD"),
            teal.picks::values(selected = "BESRSPI")
          )
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal.modules.general documentation built on Aug. 2, 2026, 1:06 a.m.