teal module: Outliers analysis

Module to analyze and identify outliers using different methods such as IQR, Z-score, and Percentiles, and offers visualizations including box plots, density plots, and cumulative distribution plots to help interpret the outliers.

Usage

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

Arguments

label

(character(1)) Label shown in the navigation item for the module or module group. For modules() defaults to "root". See Details.

outlier_var

(data_extract_spec or list of multiple data_extract_spec) Specifies variable(s) to be analyzed for outliers.

categorical_var

(data_extract_spec or list of multiple data_extract_spec) optional, specifies the categorical variable(s) to split the selected outlier variables on.

ggtheme

(character) optional, ggplot2 theme to be used by default. Defaults to "gray".

ggplot2_args

(ggplot2_args) optional, 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

(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.

decorators

(list of teal_transform_module, named list of teal_transform_module or NULL) optional, if not NULL, decorator for tables or plots included in the module. When a named list of teal_transform_module, the decorators are applied to the respective output objects.

Otherwise, the decorators are applied to all objects, which is equivalent as using the name default.

See section "Decorating tm_outliers" below for more details.

Value

Object of class teal_module to be used in teal applications.

Decorating `tm_outliers`

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

box_plot (ggplot2)
density_plot (ggplot2)
cumulative_plot (ggplot2)
table (DT::datatable())

Decorators can be applied to all outputs or only to specific objects using a named list of teal_transform_module objects. The "default" name is reserved for decorators that are applied to all outputs. See code snippet below:

tm_outliers(
   ..., # arguments for module
   decorators = list(
     default = list(teal_transform_module(...)), # applied to all outputs
     box_plot = list(teal_transform_module(...)), # applied only to `box_plot` output
     density_plot = list(teal_transform_module(...)) # applied only to `density_plot` output
     cumulative_plot = list(teal_transform_module(...)) # applied only to `cumulative_plot` output
     table = list(teal_transform_module(...)) # applied only to `table` output
   )
)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal::teal_transform_module() documentation.

Examples in Shinylive

example-1: Open in Shinylive
example-2: Open in Shinylive

Examples


# general data example
data <- teal_data()
data <- within(data, {
  CO2 <- CO2
  CO2[["primary_key"]] <- seq_len(nrow(CO2))
})
join_keys(data) <- join_keys(join_key("CO2", "CO2", "primary_key"))

vars <- choices_selected(variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")))

app <- init(
  data = data,
  modules = modules(
    tm_outliers(
      outlier_var = list(
        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
          )
        )
      ),
      categorical_var = list(
        data_extract_spec(
          dataname = "CO2",
          filter = filter_spec(
            vars = vars,
            choices = value_choices(data[["CO2"]], vars$selected),
            selected = value_choices(data[["CO2"]], vars$selected),
            multiple = TRUE
          )
        )
      )
    )
  )
)
#> Initializing tm_outliers
#> Initializing reporter_previewer_module
if (interactive()) {
  shinyApp(app$ui, app$server)
}


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

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



app <- init(
  data = data,
  modules = modules(
    tm_outliers(
      outlier_var = list(
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1")),
            selected = "AGE",
            multiple = FALSE,
            fixed = FALSE
          )
        )
      ),
      categorical_var = list(
        data_extract_spec(
          dataname = "ADSL",
          filter = filter_spec(
            vars = vars,
            choices = value_choices(data[["ADSL"]], vars$selected),
            selected = value_choices(data[["ADSL"]], vars$selected),
            multiple = TRUE
          )
        )
      )
    )
  )
)
#> Initializing tm_outliers
#> Initializing reporter_previewer_module
if (interactive()) {
  shinyApp(app$ui, app$server)
}