Package {teal.modules.general}

teal.modules.general: General modules to add to a teal application

Description

The modules in this package are generic modules that should work with any data set (not necessarily for clinical trials data).

Author(s)

Maintainer: Dony Unardi unardid@gene.com

Authors:

• Dony Unardi unardid@gene.com

• Dawid Kaledkowski dawid.kaledkowski@roche.com

• Pawel Rucki pawel.rucki@roche.com

• Mahmoud Hallal mahmoud.hallal@roche.com

• Maciej Nasinski

• Konrad Pagacz

• Nikolas Burkoff

Other contributors:

• Ondrej Slama ondrej.slama@roche.com [contributor]

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/teal.modules.general/

• https://github.com/insightsengineering/teal.modules.general/

• Report bugs at https://github.com/insightsengineering/teal.modules.general/issues

Add labels for facets to a ggplot2 object

Description

Enhances a ggplot2 plot by adding labels that describe the faceting variables along the x and y axes.

Usage

add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL)

Arguments

Value

Returns grid or grob object (to be drawn with grid.draw)

Examples

library(ggplot2)
library(grid)

p <- ggplot(mtcars) +
  aes(x = mpg, y = disp) +
  geom_point() +
  facet_grid(gear ~ cyl)

xfacet_label <- "cylinders"
yfacet_label <- "gear"
res <- add_facet_labels(p, xfacet_label, yfacet_label)
grid.newpage()
grid.draw(res)

grid.newpage()
grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label))
grid.newpage()
grid.draw(add_facet_labels(p, xfacet_label, yfacet_label = NULL))
grid.newpage()
grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL))

Mosaic Rectangles Layer for ggplot2

Description

Adds a mosaic-style rectangles layer to a ggplot, visualizing the joint distribution of categorical variables. Each rectangle's size reflects the proportion of observations for combinations of x and fill.

Usage

geom_mosaic(
  mapping = NULL,
  data = NULL,
  stat = "mosaic",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = TRUE,
  inherit.aes = TRUE
)

.calculate_coordinates(data)

Arguments

Value

A ggplot2 layer that adds mosaic rectangles to the plot.

Functions

• .calculate_coordinates(): Computes the coordinates for rectangles in a mosaic plot based on combinations of x and fill variables. For each unique x and fill, calculates the proportional widths and heights, stacking rectangles within each x group.

  Value

  A data frame with columns: x, fill, xmin, xmax, ymin, ymax, representing the position and size of each rectangle.

Examples

df <- data.frame(RACE = c("Black", "White", "Black", "Asian"), SEX = c("M", "M", "F", "F"))
library(ggplot2)
ggplot(df) +
  geom_mosaic(aes(x = RACE, fill = SEX))

Shared parameters documentation

Description

Defines common arguments shared across multiple functions in the package to avoid repetition by using inheritParams.

Arguments

Value

Object of class teal_module to be used in teal applications.

teal module: Principal component analysis

Description

Module conducts principal component analysis (PCA) on a given dataset and offers different ways of visualizing the outcomes, including elbow plot, circle plot, biplot, and eigenvector plot. Additionally, it enables dynamic customization of plot aesthetics, such as opacity, size, and font size, through UI inputs.

Usage

tm_a_pca(
  label = "Principal Component Analysis",
  dat = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(choices =
    tidyselect::where(~is.numeric(.x) && all(!is.na(.x))), selected =
    tidyselect::everything(), multiple = TRUE)),
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
  ggplot2_args = teal.widgets::ggplot2_args(),
  rotate_xaxis_labels = FALSE,
  font_size = c(12, 8, 20),
  alpha = c(1, 0, 1),
  size = c(2, 1, 8),
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

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:

• elbow_plot (ggplot)

• circle_plot (ggplot)

• biplot (ggplot)

• eigenvector_plot (ggplot)

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_a_pca(
   ..., # arguments for module
   decorators = list(
     elbow_plot = teal_transform_module(...), # applied to the `elbow_plot` output
     circle_plot = teal_transform_module(...), # applied to the `circle_plot` output
     biplot = teal_transform_module(...), # applied to the `biplot` output
     eigenvector_plot = teal_transform_module(...) # applied to the `eigenvector_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

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example-2

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Examples

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

app <- init(
  data = data,
  modules = modules(
    tm_a_pca(
      "PCA",
      dat = teal.picks::picks(
        datasets("USArrests"),
        teal.picks::variables(
          choices = c("Murder", "Assault", "UrbanPop", "Rape"),
          selected = c("Murder", "Assault"),
          multiple = TRUE
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}


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

app <- init(
  data = data,
  modules = modules(
    tm_a_pca(
      dat = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(
          choices = c("BMRKR1", "AGE", "EOSDY"),
          selected = c("BMRKR1", "AGE"),
          multiple = TRUE
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Scatterplot and regression analysis

Description

Module for visualizing regression analysis, including scatterplots and various regression diagnostics plots. It allows users to explore the relationship between a set of regressors and a response variable, visualize residuals, and identify outliers.

Usage

tm_a_regression(
  label = "Regression Analysis",
  regressor = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(choices =
    is.numeric, selected = tidyselect::last_col(), multiple = TRUE)),
  response,
  outlier,
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  alpha = c(1, 0, 1),
  size = c(2, 1, 8),
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
  ggplot2_args = teal.widgets::ggplot2_args(),
  pre_output = NULL,
  post_output = NULL,
  default_plot_type = 1,
  default_outlier_label = "USUBJID",
  label_segment_threshold = c(0.5, 0, 10),
  transformators = list(),
  decorators = list()
)

Arguments

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 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_a_regression(
   ..., # 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

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Note

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

Examples

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

app <- init(
  data = data,
  modules = modules(
    tm_a_regression(
      label = "Regression",
      response = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(choices = "uptake", selected = "uptake")
      ),
      regressor = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(choices = c("conc", "Treatment"), selected = "conc", multiple = TRUE)
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = modules(
    tm_a_regression(
      label = "Regression",
      response = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(choices = "BMRKR1", selected = "BMRKR1")
      ),
      regressor = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(choices = c("AGE", "SEX", "RACE"), selected = "AGE", multiple = TRUE)
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Data table viewer

Description

Module provides a dynamic and interactive way to view data.frames in a teal application. It uses the DT package to display data tables in a paginated, searchable, and sortable format, which helps to enhance data exploration and analysis.

Usage

tm_data_table(
  label = "Data Table",
  variables_selected = list(),
  datasets_selected = deprecated(),
  datanames = if (missing(datasets_selected)) "all" else datasets_selected,
  dt_args = list(),
  dt_options = list(searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 30, 100),
    scrollX = TRUE),
  server_rendering = FALSE,
  pre_output = NULL,
  post_output = NULL,
  transformators = list()
)

Arguments

Details

The DT package has an option DT.TOJSON_ARGS to show Inf and NA in data tables. Configure the DT.TOJSON_ARGS option via options(DT.TOJSON_ARGS = list(na = "string")) before running the module. Note though that sorting of numeric columns with NA/Inf will be lexicographic not numerical.

Value

Object of class teal_module to be used in teal applications.

Examples in Shinylive

example-1

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example-2

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Examples

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

app <- init(
  data = data,
  modules = modules(
    tm_data_table(
      variables_selected = list(
        iris = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species")
      ),
      dt_args = list(caption = "IRIS Table Caption")
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = modules(
    tm_data_table(
      variables_selected = list(ADSL = c("STUDYID", "USUBJID", "SUBJID", "SITEID", "AGE", "SEX")),
      dt_args = list(caption = "ADSL Table Caption")
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: File viewer

Description

The file viewer module provides a tool to view static files. Supported formats include text formats, PDF, PNG APNG, JPEG SVG, WEBP, GIF and BMP.

Usage

tm_file_viewer(
  label = "File Viewer Module",
  input_path = list(`Current Working Directory` = ".")
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Examples in Shinylive

example-1

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Examples

data <- teal_data()
data <- within(data, {
  data <- data.frame(1)
})

app <- init(
  data = data,
  modules = modules(
    tm_file_viewer(
      input_path = list(
        folder = system.file("sample_files", package = "teal.modules.general"),
        png = system.file("sample_files/sample_file.png", package = "teal.modules.general"),
        txt = system.file("sample_files/sample_file.txt", package = "teal.modules.general"),
        url = file.path(
          "https://www.fda.gov/files/drugs/published",
          "Portable-Document-Format-Specifications.pdf"
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Front page

Description

Creates a simple front page for teal applications, displaying introductory text, tables, additional html or shiny tags, and footnotes.

Usage

tm_front_page(
  label = "Front page",
  header_text = character(0),
  tables = list(),
  additional_tags = tagList(),
  footnotes = character(0),
  show_metadata = deprecated(),
  datanames = if (missing(show_metadata)) NULL else "all",
  transformators = list()
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Examples in Shinylive

example-1

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Examples

data <- teal_data()
data <- within(data, {
  require(nestcolor)
  ADSL <- teal.data::rADSL
  attr(ADSL, "metadata") <- list("Author" = "NEST team", "data_source" = "synthetic data")
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

table_1 <- data.frame(Info = c("A", "B"), Text = c("A", "B"))
table_2 <- data.frame(`Column 1` = c("C", "D"), `Column 2` = c(5.5, 6.6), `Column 3` = c("A", "B"))
table_3 <- data.frame(Info = c("E", "F"), Text = c("G", "H"))

table_input <- list(
  "Table 1" = table_1,
  "Table 2" = table_2,
  "Table 3" = table_3
)

app <- init(
  data = data,
  modules = modules(
    tm_front_page(
      header_text = c(
        "Important information" = "It can go here.",
        "Other information" = "Can go here."
      ),
      tables = table_input,
      additional_tags = HTML("Additional HTML or shiny tags go here <br>"),
      footnotes = c("X" = "is the first footnote", "Y is the second footnote")
    )
  )
) |>
  modify_header(tags$h1("Sample Application")) |>
  modify_footer(tags$p("Application footer"))

if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Stack plots of variables and show association with reference variable

Description

Module provides functionality for visualizing the distribution of variables and their association with a reference variable. It supports configuring the appearance of the plots, including themes and whether to show associations.

Usage

tm_g_association(
  label = "Association",
  ref = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(choices =
    is.numeric | teal.picks::is_categorical(min.len = 2, max.len = 10), selected = 1L),
    teal.picks::values()),
  vars,
  show_association = TRUE,
  plot_height = c(600, 400, 5000),
  plot_width = NULL,
  distribution_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic",
    "void"),
  association_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic",
    "void"),
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  transformators = list(),
  decorators = list()
)

Arguments

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 (grob created with ggplot2::ggplotGrob())

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_association(
   ..., # 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 association plot" via vignette("using-association-plot", package = "teal.modules.general").

Examples

# general data example
data <- teal_data()
data <- within(data, {
  require(nestcolor)
  CO2 <- CO2
  factors <- names(Filter(isTRUE, vapply(CO2, is.factor, logical(1L))))
  CO2[factors] <- lapply(CO2[factors], as.character)
})

app <- init(
  data = data,
  modules = modules(
    tm_g_association(
      ref = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(
          choices = c("Plant", "Type", "Treatment"),
          selected = "Plant"
        )
      ),
      vars = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(
          choices = c("Plant", "Type", "Treatment"),
          selected = "Treatment",
          multiple = TRUE
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = modules(
    tm_g_association(
      ref = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(
          choices = c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2"),
          selected = "RACE"
        )
      ),
      vars = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(
          choices = c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2"),
          selected = "BMRKR2",
          multiple = TRUE
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Univariate and bivariate visualizations

Description

Module enables the creation of univariate and bivariate plots, facilitating the exploration of data distributions and relationships between two variables.

Usage

tm_g_bivariate(
  label = "Bivariate Plots",
  x = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(choices =
    is.numeric | teal.picks::is_categorical(min.len = 2, max.len = 10), selected = 1L),
    teal.picks::values()),
  y,
  row_facet,
  col_facet,
  facet,
  color = NULL,
  fill = NULL,
  size = NULL,
  use_density = FALSE,
  color_settings = FALSE,
  free_x_scales = FALSE,
  free_y_scales = FALSE,
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  rotate_xaxis_labels = FALSE,
  swap_axes = FALSE,
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
  ggplot2_args = teal.widgets::ggplot2_args(),
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

Details

This is a general module to visualize 1 & 2 dimensional data.

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 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_bivariate(
   ..., # 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 bivariate plot" via vignette("using-bivariate-plot", package = "teal.modules.general").

Examples

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

app <- init(
  data = data,
  modules = tm_g_bivariate(
    label = "Bivariate Plots",
    x = teal.picks::picks(
      datasets("CO2"),
      teal.picks::variables(selected = "conc")
    ),
    y = teal.picks::picks(
      datasets("CO2"),
      teal.picks::variables(selected = "uptake")
    ),
    row_facet = teal.picks::picks(
      datasets("CO2"),
      teal.picks::variables(selected = "Type")
    ),
    col_facet = teal.picks::picks(
      datasets("CO2"),
      teal.picks::variables(selected = "Treatment")
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = tm_g_bivariate(
    label = "Bivariate Plots",
    x = teal.picks::picks(
      datasets("ADSL"),
      teal.picks::variables(selected = "AGE")
    ),
    y = teal.picks::picks(
      datasets("ADSL"),
      teal.picks::variables(selected = "SEX")
    ),
    row_facet = teal.picks::picks(
      datasets("ADSL"),
      teal.picks::variables(selected = "ARM")
    ),
    col_facet = teal.picks::picks(
      datasets("ADSL"),
      teal.picks::variables(selected = "COUNTRY")
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Distribution analysis

Description

Module is designed to explore the distribution of a single variable within a given dataset. It offers several tools, such as histograms, Q-Q plots, and various statistical tests to visually and statistically analyze the variable's distribution.

Usage

tm_g_distribution(
  label = "Distribution Module",
  dist_var = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(is.numeric),
    teal.picks::values()),
  strata_var = NULL,
  group_var = NULL,
  freq = FALSE,
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
  ggplot2_args = teal.widgets::ggplot2_args(),
  bins = c(30L, 1L, 100L),
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

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::

• histogram_plot (ggplot)

• qq_plot (ggplot)

• summary_table (DT::datatable)

• test_table (DT::datatable)

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_distribution(
   ..., # arguments for module
   decorators = list(
     histogram_plot = teal_transform_module(...), # applied only to `histogram_plot` output
     qq_plot = teal_transform_module(...) # applied only to `qq_plot` output
     summary_table = teal_transform_module(...) # applied only to `summary_table` output
     test_table = teal_transform_module(...) # applied only to `test_table` 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

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example-2

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Examples

# general data example
data <- teal_data()
data <- within(data, {
  iris <- iris
})

app <- init(
  data = data,
  modules = list(
    tm_g_distribution(
      dist_var = teal.picks::picks(
        datasets("iris"),
        teal.picks::variables(is.numeric),
        teal.picks::values()
      )
    )
  )
)
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)]

app <- init(
  data = data,
  modules = modules(
    tm_g_distribution(
      dist_var = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(c("BMRKR1", "AGE")),
        values(multiple = FALSE)
      ),
      strata_var = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(c("ARM", "COUNTRY", "SEX"), selected = NULL)
      ),
      group_var = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(c("ARM", "COUNTRY", "SEX"), selected = NULL)
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Response plot

Description

Generates a response plot for a given response and x variables. This module allows users customize and add annotations to the plot depending on the module's arguments. It supports showing the counts grouped by other variable facets (by row / column), swapping the coordinates, show count annotations and displaying the response plot as frequency or density.

Usage

tm_g_response(
  label = "Response Plot",
  response = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(choices =
    teal.picks::is_categorical(min.len = 2, max.len = 10)), teal.picks::values()),
  x,
  row_facet = NULL,
  col_facet = NULL,
  coord_flip = FALSE,
  count_labels = TRUE,
  rotate_xaxis_labels = FALSE,
  freq = FALSE,
  plot_height = c(600, 400, 5000),
  plot_width = NULL,
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
  ggplot2_args = teal.widgets::ggplot2_args(),
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

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 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_response(
   ..., # 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

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example-2

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Note

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

Examples

# general data example
data <- teal_data()
data <- within(data, {
  require(nestcolor)
  mtcars <- mtcars
  for (.v in c("cyl", "vs", "am", "gear")) {
    mtcars[[.v]] <- as.factor(mtcars[[.v]])
  }
})

app <- init(
  data = data,
  modules = modules(
    tm_g_response(
      label = "Response Plots",
      response = teal.picks::picks(
        teal.picks::datasets("mtcars"),
        teal.picks::variables(
          choices = c("cyl", "gear"),
          selected = "cyl",
          multiple = FALSE,
          fixed = FALSE
        ),
        teal.picks::values()
      ),
      x = teal.picks::picks(
        datasets("mtcars"),
        teal.picks::variables(
          choices = c("vs", "am"),
          selected = "vs",
          multiple = FALSE,
          fixed = FALSE
        ),
        teal.picks::values()
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = modules(
    tm_g_response(
      label = "Response Plots",
      response = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(
          choices = c("BMRKR2", "COUNTRY"),
          selected = "BMRKR2"
        ),
        teal.picks::values()
      ),
      x = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(
          choices = c("SEX", "RACE"),
          selected = "RACE"
        ),
        teal.picks::values()
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Scatterplot

Description

Generates a customizable scatterplot using ggplot2. This module allows users to select variables for the x and y axes, color and size encodings, faceting options, and more. It supports log transformations, trend line additions, and dynamic adjustments of point opacity and size through UI controls.

Usage

tm_g_scatterplot(
  label = "Scatterplot",
  x = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(is.numeric),
    teal.picks::values()),
  y,
  color_by = NULL,
  size_by = NULL,
  row_facet = NULL,
  col_facet = NULL,
  plot_height = c(600, 200, 2000),
  plot_width = NULL,
  alpha = c(1, 0, 1),
  shape = shape_names,
  size = c(5, 1, 15),
  max_deg = 5L,
  rotate_xaxis_labels = FALSE,
  ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
  pre_output = NULL,
  post_output = NULL,
  table_dec = 4,
  ggplot2_args = teal.widgets::ggplot2_args(),
  transformators = list(),
  decorators = list()
)

Arguments

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 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_scatterplot(
   ..., # 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

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Note

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

Examples

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

app <- init(
  data = data,
  modules = modules(
    tm_g_scatterplot(
      label = "Scatterplot Choices",
      x = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(
          choices = c("conc", "uptake"),
          selected = "conc"
        ),
        teal.picks::values()
      ),
      y = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(
          choices = c("conc", "uptake"),
          selected = "uptake"
        ),
        teal.picks::values()
      ),
      color_by = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(
          choices = c("Plant", "Type", "Treatment", "conc", "uptake"),
          selected = NULL
        ),
        teal.picks::values()
      ),
      size_by = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(choices = c("conc", "uptake"), selected = "uptake"),
        teal.picks::values()
      ),
      row_facet = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(
          choices = c("Plant", "Type", "Treatment"),
          selected = NULL
        ),
        teal.picks::values()
      ),
      col_facet = teal.picks::picks(
        datasets("CO2"),
        teal.picks::variables(choices = c("Plant", "Type", "Treatment"), selected = NULL),
        teal.picks::values()
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = modules(
    tm_g_scatterplot(
      label = "Scatterplot Choices",
      x = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(choices = c("AGE", "BMRKR1", "BMRKR2"), selected = "AGE"),
        teal.picks::values()
      ),
      y = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(choices = c("AGE", "BMRKR1", "BMRKR2"), selected = "BMRKR1"),
        teal.picks::values()
      ),
      color_by = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(c("AGE", "BMRKR1", "BMRKR2", "RACE", "REGION1"), selected = NULL),
        teal.picks::values()
      ),
      size_by = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(choices = c("AGE", "BMRKR1"), selected = "AGE"),
        teal.picks::values()
      ),
      row_facet = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(choices = c("BMRKR2", "RACE", "REGION1"), selected = NULL),
        teal.picks::values()
      ),
      col_facet = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(choices = c("BMRKR2", "RACE", "REGION1"), selected = NULL),
        teal.picks::values()
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

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 module: Missing data analysis

Description

This module analyzes missing data in data.frames to help users explore missing observations and gain insights into the completeness of their data. It is useful for clinical data analysis within the context of CDISC standards and adaptable for general data analysis purposes.

Usage

tm_missing_data(
  label = "Missing data",
  plot_height = c(600, 400, 5000),
  plot_width = NULL,
  datanames = "all",
  parent_dataname = "ADSL",
  ggtheme = c("classic", "gray", "bw", "linedraw", "light", "dark", "minimal", "void"),
  ggplot2_args = list(`Combinations Hist` = teal.widgets::ggplot2_args(labs =
    list(caption = NULL)), `Combinations Main` = teal.widgets::ggplot2_args(labs =
    list(title = NULL))),
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

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:

• summary_plot (ggplot)

• combination_plot (grob created with ggplot2::ggplotGrob())

• by_variable_plot (ggplot)

• by_subject_plot (ggplot)

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_missing_data(
   ..., # arguments for module
   decorators = list(
     summary_plot = teal_transform_module(...), # applied only to `summary_plot` output
     combination_plot = teal_transform_module(...), # applied only to `combination_plot` output
     by_variable_plot = teal_transform_module(...) # applied only to `by_variable_plot` output
     by_subject_plot = teal_transform_module(...) # applied only to `by_subject_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

Examples

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

  add_nas <- function(x) {
    x[sample(seq_along(x), floor(length(x) * runif(1, .05, .17)))] <- NA
    x
  }

  iris <- iris
  mtcars <- mtcars

  iris[] <- lapply(iris, add_nas)
  mtcars[] <- lapply(mtcars, add_nas)
  mtcars[["cyl"]] <- as.factor(mtcars[["cyl"]])
  mtcars[["gear"]] <- as.factor(mtcars[["gear"]])
})

app <- init(
  data = data,
  modules = modules(
    tm_missing_data(parent_dataname = "mtcars")
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = modules(
    tm_missing_data()
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Outliers analysis

Description

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 = teal.picks::picks(teal.picks::datasets(),
    teal.picks::variables(is.numeric, 1L, multiple = FALSE)),
  categorical_var = teal.picks::picks(teal.picks::datasets(),
    teal.picks::variables(choices = teal.picks::is_categorical(min.len = 1, max.len =
    10), selected = 1L, multiple = TRUE)),
  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,
  transformators = list(),
  decorators = list()
)

Arguments

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:

• box_plot (ggplot)

• density_plot (ggplot)

• cumulative_plot (ggplot)

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_outliers(
   ..., # arguments for module
   decorators = list(
     box_plot = teal_transform_module(...), # applied only to `box_plot` output
     density_plot = teal_transform_module(...), # applied only to `density_plot` output
     cumulative_plot = teal_transform_module(...) # applied only to `cumulative_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

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example-2

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Examples

# general data example
data <- teal_data()
data <- within(data, CO2 <- CO2)

app <- init(
  data = data,
  modules = modules(
    tm_outliers(
      outlier_var = teal.picks::picks(
        datasets("CO2", "CO2"),
        teal.picks::variables(c("conc", "uptake"), "uptake"),
        teal.picks::values()
      ),
      categorical_var = teal.picks::picks(
        datasets("CO2", "CO2"),
        teal.picks::variables(c("Plant", "Type", "Treatment"), "Plant"),
        teal.picks::values()
      )
    )
  )
)
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)]

app <- init(
  data = data,
  modules = modules(
    tm_outliers(
      outlier_var = teal.picks::picks(
        datasets("ADSL", "ADSL"),
        teal.picks::variables(c("AGE", "BMRKR1"), "AGE")
      ),
      categorical_var = teal.picks::picks(
        datasets("ADSL", "ADSL"),
        teal.picks::variables(teal.picks::is_categorical(min.len = 1, max.len = 10))
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: R Markdown render

Description

Module to render R Markdown files using the data provided in the teal_data object.

The R Markdown file should be designed to accept variables available in the data names of the module.

Usage

tm_rmarkdown(
  label = "RMarkdown Module",
  rmd_content,
  datanames = "all",
  allow_download = TRUE,
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  extra_transform = list()
)

Arguments

Details

For example, if the teal_data object contains datasets named mtcars and iris, the R Markdown file can use these as variables as they will be available in the R Markdown environment.

The libraries used in the R Markdown file must be available in the deployed shiny app environment.

When developing the R Markdown file, the working data can be simulated on a code chunk, which in turn can look for the presence of .raw_data object to determine if it is being run inside the teal module or not.

Example R markdown file:

---
title: "R Markdown Report"
output: html_document
---

```{r eval=!exists(".raw_data")}
mtcars <- datasets::mtcars
iris <- datasets::iris
```

```{r}
summary(mtcars) |> print()
summary(iris) |> print()
```

Value

Object of class teal_module to be used in teal applications.

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

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Examples

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

app <- init(
  data = data,
  modules = modules(
    tm_rmarkdown(
      label = "RMarkdown Module",
      rmd_content = c(
        "---",
        "title: \"R Markdown Report\"",
        "output: html_document",
        "---",
        "",
        "```{r}",
        "summary(CO2) |> print()",
        "```"
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

nrow_transform <- teal_transform_module(
  label = "N Rows selector",
  ui = function(id) {
    ns <- NS(id)
    tags$div(
      numericInput(ns("n_rows"), "Show n rows", value = 40, min = 0, max = 200, step = 5)
    )
  },
  server = function(id, data) {
    moduleServer(id, function(input, output, session) {
      reactive({
        req(data())
        within(data(),
          {
            n_rows <- n_rows_value
          },
          n_rows_value = input$n_rows
        )
      })
    })
  }
)

app <- init(
  data = data,
  modules = modules(
    tm_rmarkdown(
      label = "RMarkdown Module",
      rmd_content = readLines(
        system.file(
          file.path("sample_files", "co2_example.Rmd"),
          package = "teal.modules.general"
        )
      ),
      allow_download = FALSE,
      extra_transform = list(nrow_transform)
    )
  )
)

if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Cross-table

Description

Generates a simple cross-table of two variables from a dataset with custom options for showing percentages and sub-totals.

Usage

tm_t_crosstable(
  label = "Cross Table",
  x = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(choices =
    teal.picks::is_categorical(min.len = 2, max.len = 10), selected = 1L, multiple =
    TRUE, ordered = TRUE)),
  y,
  show_percentage = TRUE,
  show_total = TRUE,
  remove_zero_columns = FALSE,
  pre_output = NULL,
  post_output = NULL,
  basic_table_args = teal.widgets::basic_table_args(),
  transformators = list(),
  decorators = list()
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Table Settings

The module provides several table settings that can be adjusted:

• Show column percentage: Shows column percentages when enabled

• Show total column: Shows a total column when enabled

• Remove zero-only columns: Removes columns that contain only zeros from the output table

Decorating Module

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

• table (ElementaryTable - output of rtables::build_table)

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_t_crosstable(
   ..., # arguments for module
   decorators = list(
     table = teal_transform_module(...) # applied to the `table` 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

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example-2

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Note

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

Examples

# general data example
data <- teal_data()
data <- within(data, {
  mtcars <- mtcars
  for (v in c("cyl", "vs", "am", "gear")) {
    mtcars[[v]] <- as.factor(mtcars[[v]])
  }
  mtcars[["primary_key"]] <- seq_len(nrow(mtcars))
})
join_keys(data) <- join_keys(join_key("mtcars", "mtcars", "primary_key"))

app <- init(
  data = data,
  modules = modules(
    tm_t_crosstable(
      label = "Cross Table",
      x = teal.picks::picks(
        datasets("mtcars"),
        teal.picks::variables(
          choices = variable_choices(data[["mtcars"]], c("cyl", "vs", "am", "gear")),
          selected = c("cyl", "gear"),
          multiple = TRUE,
          ordered = TRUE,
          fixed = FALSE
        ),
        teal.picks::values()
      ),
      y = teal.picks::picks(
        datasets("mtcars"),
        teal.picks::variables(
          choices = variable_choices(data[["mtcars"]], c("cyl", "vs", "am", "gear")),
          selected = "vs",
          multiple = FALSE,
          fixed = FALSE
        ),
        teal.picks::values()
      )
    )
  )
)
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)]

app <- init(
  data = data,
  modules = modules(
    tm_t_crosstable(
      label = "Cross Table",
      x = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(
          choices = variable_choices(data[["ADSL"]], subset = function(data) {
            idx <- !vapply(data, inherits, logical(1), c("Date", "POSIXct", "POSIXlt"))
            return(names(data)[idx])
          }),
          selected = "COUNTRY",
          multiple = TRUE,
          ordered = TRUE,
          fixed = FALSE
        ),
        teal.picks::values()
      ),
      y = teal.picks::picks(
        datasets("ADSL"),
        teal.picks::variables(
          choices = variable_choices(data[["ADSL"]], subset = function(data) {
            idx <- vapply(data, is.factor, logical(1))
            return(names(data)[idx])
          }),
          selected = "SEX",
          multiple = FALSE,
          fixed = FALSE
        ),
        teal.picks::values()
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: GT Summary table

Description

Summary table from a given dataset, using gtsummary.

Usage

tm_tbl_summary(
  label = "Summary table",
  by = NULL,
  include = teal.picks::picks(teal.picks::datasets(), teal.picks::variables(selected =
    dplyr::everything(), multiple = TRUE)),
  dataname = NULL,
  ...,
  col_label = NULL,
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

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:

• table (gtsummary - output of gtsummary::tbl_summary())

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_tbl_summary(
   ..., # arguments for module
   decorators = list(
     table = teal_transform_module(...) # applied to the `table` 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.

statistic argument

The statistic argument specifies the statistics presented in the table. The input dictates the summary statistics presented in the table. For example, statistic = list(age ~ "{mean} ({sd})") would report the mean and standard deviation for age; statistic = list(all_continuous() ~ "{mean} ({sd})") would report the mean and standard deviation for all continuous variables.

The values are interpreted using glue::glue() syntax: a name that appears between curly brackets will be interpreted as a function name and the formatted result of that function will be placed in the table.

For categorical variables, the following statistics are available to display: {n} (frequency), {N} (denominator), {p} (percent).

For continuous variables, any univariate function may be used. The most commonly used functions are {median}, {mean}, {sd}, {min}, and {max}. Additionally, ⁠{p##}⁠ is available for percentiles, where ⁠##⁠ is an integer from 0 to 100. For example, p25: quantile(probs=0.25, type=2).

When the summary type is "continuous2", pass a vector of statistics. Each element of the vector will result in a separate row in the summary table.

For both categorical and continuous variables, statistics on the number of missing and non-missing observations and their proportions are available to display.

• {N_obs} total number of observations

• {N_miss} number of missing observations

• {N_nonmiss} number of non-missing observations

• {p_miss} percentage of observations missing

• {p_nonmiss} percentage of observations not missing

digits argument

The digits argument specifies the the number of digits (or formatting function) statistics are rounded to.

The values passed can either be a single integer, a vector of integers, a function, or a list of functions. If a single integer or function is passed, it is recycled to the length of the number of statistics presented. For example, if the statistic is "{mean} ({sd})", it is equivalent to pass 1, c(1, 1), label_style_number(digits=1), and list(label_style_number(digits=1), label_style_number(digits=1)).

Named lists are also accepted to change the default formatting for a single statistic, e.g. list(sd = label_style_number(digits=1)).

type and value arguments

There are four summary types. Use the type argument to change the default summary types.

• "continuous" summaries are shown on a single row. Most numeric variables default to summary type continuous.

• "continuous2" summaries are shown on 2 or more rows

• "categorical" multi-line summaries of nominal data. Character variables, factor variables, and numeric variables with fewer than 10 unique levels default to type categorical. To change a numeric variable to continuous that defaulted to categorical, use type = list(varname ~ "continuous")

• "dichotomous" categorical variables that are displayed on a single row, rather than one row per level of the variable. Variables coded as TRUE/FALSE, 0/1, or yes/no are assumed to be dichotomous, and the TRUE, 1, and yes rows are displayed. Otherwise, the value to display must be specified in the value argument, e.g. value = list(varname ~ "level to show")

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

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Examples

# General example
data <- teal_data()
data <- within(data, CO2 <- CO2)
app <- init(
  data = data,
  modules = modules(
    tm_tbl_summary(
      by = teal.picks::picks(
        datasets("CO2", "CO2"),
        variables(selected = "Plant")
      ),
      include = teal.picks::picks(
        datasets("CO2", "CO2"),
        variables(selected = c("Type", "Treatment"), multiple = TRUE)
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

# CDISC data example
data <- within(teal.data::teal_data(), {
  ADSL <- teal.data::rADSL
  ADTTE <- teal.data::rADTTE
})
join_keys(data) <- default_cdisc_join_keys[names(data)]
app <- init(
  data = data,
  modules = modules(
    tm_tbl_summary(
      by = teal.picks::picks(
        datasets(c("ADSL", "ADTTE"), "ADTTE"),
        variables(c("SEX", "COUNTRY", "SITEID", "ACTARM", "CNSR", "PARAMCD"), "SEX")
      ),
      include = teal.picks::picks(
        datasets(c("ADSL", "ADTTE"), "ADSL"),
        variables(c("SITEID", "COUNTRY", "ACTARM", "SEX"), "SITEID", multiple = TRUE)
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal module: Variable browser

Description

Module provides provides a detailed summary and visualization of variable distributions for data.frame objects, with interactive features to customize analysis.

Usage

tm_variable_browser(
  label = "Variable Browser",
  datasets_selected = deprecated(),
  datanames = if (missing(datasets_selected)) "all" else datasets_selected,
  parent_dataname = "ADSL",
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  transformators = list()
)

Arguments

Details

Numeric columns with fewer than 30 distinct values can be treated as either discrete or continuous with a checkbox allowing users to switch how they are treated(if < 6 unique values then the default is discrete, otherwise it is continuous).

Value

Object of class teal_module to be used in teal applications.

Examples in Shinylive

example-1

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example-2

Open in Shinylive

Examples

# general data example
data <- teal_data()
data <- within(data, {
  iris <- iris
  mtcars <- mtcars
  women <- women
  faithful <- faithful
  CO2 <- CO2
})

app <- init(
  data = data,
  modules = modules(
    tm_variable_browser(
      label = "Variable browser"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

app <- init(
  data = data,
  modules = modules(
    tm_variable_browser(
      label = "Variable browser"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}