teal module: Scatterplot matrix — tm_g_scatterplotmatrix • teal.modules.general

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,
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL
)

Arguments

label: (character(1)) Label shown in the navigation item for the module or module group. For modules() defaults to "root". See Details.
variables: (data_extract_spec or list of multiple data_extract_spec) Specifies plotting variables from an incoming dataset with filtering and selecting. In case of data_extract_spec use select_spec(..., ordered = TRUE) if plot elements should be rendered according to selection order.
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.

Value

Object of class teal_module to be used in teal applications.

Note

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

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)
  )
})
datanames(data) <- c("countries", "sales")
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(
        data_extract_spec(
          dataname = "countries",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["countries"]]),
            selected = c("area", "gdp", "debt"),
            multiple = TRUE,
            ordered = TRUE,
            fixed = FALSE
          )
        ),
        data_extract_spec(
          dataname = "sales",
          filter = filter_spec(
            label = "Select variable:",
            vars = "country_id",
            choices = value_choices(data[["sales"]], "country_id"),
            selected = c("DE", "FR", "IT", "ES", "PT", "GR", "NL", "BE", "LU", "AT"),
            multiple = TRUE
          ),
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["sales"]], c("quantity", "costs", "profit")),
            selected = c("quantity", "costs", "profit"),
            multiple = TRUE,
            ordered = TRUE,
            fixed = FALSE
          )
        )
      )
    )
  )
)
#> [INFO] 2024-03-01 20:55:30.6371 pid:1280 token:[] teal.modules.general Initializing tm_g_scatterplotmatrix
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# CDISC data example
data <- teal_data()
data <- within(data, {
  ADSL <- rADSL
  ADRS <- rADRS
})
datanames(data) <- c("ADSL", "ADRS")
join_keys(data) <- default_cdisc_join_keys[datanames(data)]

app <- init(
  data = data,
  modules = modules(
    tm_g_scatterplotmatrix(
      label = "Scatterplot matrix",
      variables = list(
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADSL"]]),
            selected = c("AGE", "RACE", "SEX"),
            multiple = TRUE,
            ordered = TRUE,
            fixed = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADRS",
          filter = filter_spec(
            label = "Select endpoints:",
            vars = c("PARAMCD", "AVISIT"),
            choices = value_choices(data[["ADRS"]], c("PARAMCD", "AVISIT"), c("PARAM", "AVISIT")),
            selected = "INVET - END OF INDUCTION",
            multiple = TRUE
          ),
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["ADRS"]]),
            selected = c("AGE", "AVAL", "ADY"),
            multiple = TRUE,
            ordered = TRUE,
            fixed = FALSE
          )
        )
      )
    )
  )
)
#> [INFO] 2024-03-01 20:55:30.7331 pid:1280 token:[] teal.modules.general Initializing tm_g_scatterplotmatrix
if (interactive()) {
  shinyApp(app$ui, app$server)
}