Outliers Module

Usage

tm_outliers(
  label = "Outliers Module",
  outlier_var,
  categorical_var = NULL,
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void",
    "test"),
  ggplot2_args = teal.widgets::ggplot2_args(),
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL
)

Arguments

label

(character) Label shown in the navigation item for the module.

outlier_var

(data_extract_spec or list of multiple data_extract_spec) variable to consider for the outliers analysis.

categorical_var

(data_extract_spec or list of multiple data_extract_spec) categorical factor to split the selected outlier variables on.

ggtheme

optional, (character) ggplot2 theme to be used by default. One of c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void", "test"). Each theme can be chosen by the user during the session. Defaults to "gray".

ggplot2_args

optional, (ggplot2_args) object created by teal.widgets::ggplot2_args() with settings for all the plots or named list of ggplot2_args objects for plot-specific settings. The argument is merged with options variable teal.ggplot2_args and default module setup.

List names should match the following: c("default", "Boxplot","Density plot","Cumulative distribution plot").

For more details see the vignette: vignette("custom-ggplot2-arguments", package = "teal.widgets").

plot_height

optional, (numeric) A vector of length three with c(value, min and max) for a slider encoding the plot height.

plot_width

optional, (numeric) A vector of length three with c(value, min and max) for a slider encoding the plot width.

pre_output

(shiny.tag, optional)
with text placed before the output to put the output into context. For example a title.

post_output

(shiny.tag, optional) with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.

Examples

library(scda)

ADSL <- synthetic_cdisc_data("latest")$adsl

fact_vars_adsl <- names(Filter(isTRUE, sapply(ADSL, is.factor)))
vars <- choices_selected(variable_choices(ADSL, fact_vars_adsl))

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL, code = "ADSL <- synthetic_cdisc_data(\"latest\")$adsl"),
    check = TRUE
  ),
  modules = modules(
    tm_outliers(
      outlier_var = list(
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(ADSL, c("AGE", "BMRKR1")),
            selected = "AGE",
            multiple = FALSE,
            fixed = FALSE
          )
        )
      ),
      categorical_var = data_extract_spec(
        dataname = "ADSL",
        filter = filter_spec(
          vars = vars,
          choices = value_choices(ADSL, vars$selected),
          selected = value_choices(ADSL, vars$selected),
          multiple = TRUE
        )
      ),
      ggplot2_args = teal.widgets::ggplot2_args(labs = list(subtitle = "Plot generated by Outliers Module"))
    )
  )
)
#> [INFO] 2022-06-14 17:42:48.7637 pid:1110 token:[] teal.modules.general Initializing tm_outliers
if (FALSE) {
shinyApp(app$ui, app$server)
}