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: Dawid Kaledkowski dawid.kaledkowski@roche.com

Authors:

• 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))

Call a function with a character vector for the ... argument

Description

Call a function with a character vector for the ... argument

Usage

call_fun_dots(fun, str_args)

Arguments

Value

Value of call to fun with arguments specified in str_args.

S3 generic for sparkline widget HTML

Description

Generates the sparkline HTML code corresponding to the input array. For numeric variables creates a box plot, for character and factors - bar plot. Produces an empty string for variables of other types.

Usage

create_sparklines(arr, width = 150, ...)

## S3 method for class 'logical'
create_sparklines(arr, ...)

## S3 method for class 'numeric'
create_sparklines(arr, width = 150, ...)

## S3 method for class 'character'
create_sparklines(arr, ...)

## S3 method for class 'factor'
create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...)

## S3 method for class 'Date'
create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...)

## S3 method for class 'POSIXct'
create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...)

## S3 method for class 'POSIXlt'
create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...)

## Default S3 method:
create_sparklines(arr, width = 150, ...)

Arguments

Value

Character string containing HTML code of the sparkline HTML widget.

Creates observers updating the currently selected column

Description

The created observers update the column currently selected in the left-hand side tabset panel.

Usage

establish_updating_selection(datanames, input, plot_var, columns_names)

Arguments

Note

Creates an observer for each dataset (each tab in the tabset panel).

Get stats for x-y pairs in scatterplot matrix

Description

Uses stats::cor.test() per default for all numerical input variables and converts results to character vector. Could be extended if different stats for different variable types are needed. Meant to be called from lattice::panel.text().

Usage

get_scatterplotmatrix_stats(
  x,
  y,
  .f = stats::cor.test,
  .f_args = list(),
  round_stat = 2,
  round_pval = 4
)

Arguments

Details

Presently we need to use a formula input for stats::cor.test because na.fail only gets evaluated when a formula is passed (see below).

x = c(1,3,5,7,NA)
y = c(3,6,7,8,1)
stats::cor.test(x, y, na.action = "na.fail")
stats::cor.test(~ x + y,  na.action = "na.fail")

Value

Character with stats. For stats::cor.test() correlation coefficient and p-value.

Examples

set.seed(1)
x <- runif(25, 0, 1)
y <- runif(25, 0, 1)
x[c(3, 10, 18)] <- NA

get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list(method = "pearson"))
get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list(
  method = "pearson",
  na.action = na.fail
))

Include CSS files from ⁠/inst/css/⁠ package directory to application header

Description

system.file should not be used to access files in other packages, it does not work with devtools. Therefore, we redefine this method in each package as needed. Thus, we do not export this method

Usage

include_css_files(pattern = "*")

Arguments

Value

HTML code that includes CSS files.

JavaScript condition to check if a specific tab is active

Description

JavaScript condition to check if a specific tab is active

Usage

is_tab_active_js(id, name)

Arguments

Value

JavaScript expression to be used in shiny::conditionalPanel() to determine if the specified tab is active.

Plot variable

Description

Creates summary plot with statistics relevant to data type.

Usage

plot_var_summary(
  var,
  var_lab,
  wrap_character = NULL,
  numeric_as_factor,
  display_density = is.numeric(var),
  remove_NA_hist = FALSE,
  outlier_definition,
  records_for_factor,
  ggplot2_args
)

Arguments

Value

plot

Removes the outlier observation from an array

Description

Removes the outlier observation from an array

Usage

remove_outliers_from(var, outlier_definition)

Arguments

Value

(numeric) vector without the outlier values

Renders a single tab in the left-hand side tabset panel

Description

Renders a single tab in the left-hand side tabset panel. The rendered tab contains information about one dataset out of many presented in the module.

Usage

render_single_tab(
  dataset_name,
  parent_dataname,
  output,
  data,
  input,
  columns_names,
  plot_var
)

Arguments

Renders the text headlining a single tab in the left-hand side tabset panel

Description

Renders the text headlining a single tab in the left-hand side tabset panel

Usage

render_tab_header(dataset_name, output, data)

Arguments

Renders the table for a single dataset in the left-hand side tabset panel

Description

The table contains column names, column labels, small summary about NA values and sparkline (if appropriate).

Usage

render_tab_table(
  dataset_name,
  parent_dataname,
  output,
  data,
  input,
  columns_names,
  plot_var
)

Arguments

Renders the left-hand side tabset panel of the module

Description

Renders the left-hand side tabset panel of the module

Usage

render_tabset_panel_content(
  datanames,
  parent_dataname,
  output,
  data,
  input,
  columns_names,
  plot_var
)

Arguments

Subset decorators based on the scope

Description

Subset decorators based on the scope

Usage

select_decorators(decorators, scope)

Arguments

Value

Subsetted list with all decorators to include. It can be an empty list if none of the scope exists in decorators argument.

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.

Wrappers around srv_transform_teal_data that allows to decorate the data

Description

Wrappers around srv_transform_teal_data that allows to decorate the data

Usage

srv_decorate_teal_data(id, data, decorators, expr, expr_is_reactive = FALSE)

ui_decorate_teal_data(id, decorators, ...)

Arguments

Details

srv_decorate_teal_data is a wrapper around srv_transform_teal_data that allows to decorate the data with additional expressions. When original teal_data object is in error state, it will show that error first.

ui_decorate_teal_data is a wrapper around ui_transform_teal_data.

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,
  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.

Examples in Shinylive

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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 = data_extract_spec(
        dataname = "USArrests",
        select = select_spec(
          choices = variable_choices(
            data = data[["USArrests"]], c("Murder", "Assault", "UrbanPop", "Rape")
          ),
          selected = c("Murder", "Assault"),
          multiple = TRUE
        ),
        filter = NULL
      )
    )
  )
)
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(
      "PCA",
      dat = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = variable_choices(
            data = data[["ADSL"]], c("BMRKR1", "AGE", "EOSDY")
          ),
          selected = c("BMRKR1", "AGE"),
          multiple = TRUE
        ),
        filter = NULL
      )
    )
  )
)
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,
  response,
  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.

Examples in Shinylive

example-1

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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 = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = "uptake",
          selected = "uptake",
          multiple = FALSE,
          fixed = TRUE
        )
      ),
      regressor = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["CO2"]], c("conc", "Treatment")),
          selected = "conc",
          multiple = TRUE,
          fixed = FALSE
        )
      )
    )
  )
)
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 = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = "BMRKR1",
          selected = "BMRKR1",
          multiple = FALSE,
          fixed = TRUE
        )
      ),
      regressor = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
          selected = "AGE",
          multiple = TRUE,
          fixed = FALSE
        )
      )
    )
  )
)
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 = "https://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,
  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.

Examples in Shinylive

example-1

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example-2

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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 = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
          selected = "Plant",
          fixed = FALSE
        )
      ),
      vars = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
          selected = "Treatment",
          multiple = TRUE,
          fixed = FALSE
        )
      )
    )
  )
)
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 = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(
            data[["ADSL"]],
            c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2")
          ),
          selected = "RACE",
          fixed = FALSE
        )
      ),
      vars = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(
            data[["ADSL"]],
            c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2")
          ),
          selected = "BMRKR2",
          multiple = TRUE,
          fixed = FALSE
        )
      )
    )
  )
)
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,
  y,
  row_facet = NULL,
  col_facet = NULL,
  facet = !is.null(row_facet) || !is.null(col_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.

Examples in Shinylive

example-1

Open in Shinylive

example-2

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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(
    x = data_extract_spec(
      dataname = "CO2",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["CO2"]]),
        selected = "conc",
        fixed = FALSE
      )
    ),
    y = data_extract_spec(
      dataname = "CO2",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["CO2"]]),
        selected = "uptake",
        multiple = FALSE,
        fixed = FALSE
      )
    ),
    row_facet = data_extract_spec(
      dataname = "CO2",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["CO2"]]),
        selected = "Type",
        fixed = FALSE
      )
    ),
    col_facet = data_extract_spec(
      dataname = "CO2",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["CO2"]]),
        selected = "Treatment",
        fixed = FALSE
      )
    )
  )
)
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(
    x = data_extract_spec(
      dataname = "ADSL",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["ADSL"]]),
        selected = "AGE",
        fixed = FALSE
      )
    ),
    y = data_extract_spec(
      dataname = "ADSL",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["ADSL"]]),
        selected = "SEX",
        multiple = FALSE,
        fixed = FALSE
      )
    ),
    row_facet = data_extract_spec(
      dataname = "ADSL",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["ADSL"]]),
        selected = "ARM",
        fixed = FALSE
      )
    ),
    col_facet = data_extract_spec(
      dataname = "ADSL",
      select = select_spec(
        label = "Select variable:",
        choices = variable_choices(data[["ADSL"]]),
        selected = "COUNTRY",
        fixed = FALSE
      )
    )
  )
)
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,
  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 (datatables created with DT::datatable())

• test_table (datatables created with 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.

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 = data_extract_spec(
        dataname = "iris",
        select = select_spec(variable_choices("iris"), "Petal.Length")
      )
    )
  )
)
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)]

vars1 <- choices_selected(
  variable_choices(data[["ADSL"]], c("ARM", "COUNTRY", "SEX")),
  selected = NULL
)

app <- init(
  data = data,
  modules = modules(
    tm_g_distribution(
      dist_var = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1")),
          selected = "BMRKR1",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      strata_var = data_extract_spec(
        dataname = "ADSL",
        filter = filter_spec(
          vars = vars1,
          multiple = TRUE
        )
      ),
      group_var = data_extract_spec(
        dataname = "ADSL",
        filter = filter_spec(
          vars = vars1,
          multiple = TRUE
        )
      )
    )
  )
)
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,
  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.

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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 = data_extract_spec(
        dataname = "mtcars",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["mtcars"]], c("cyl", "gear")),
          selected = "cyl",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      x = data_extract_spec(
        dataname = "mtcars",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["mtcars"]], c("vs", "am")),
          selected = "vs",
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  )
)
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 = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("BMRKR2", "COUNTRY")),
          selected = "BMRKR2",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      x = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("SEX", "RACE")),
          selected = "RACE",
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  )
)
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,
  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.

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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 = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("conc", "uptake")),
          selected = "conc",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      y = 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
        )
      ),
      color_by = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(
            data[["CO2"]],
            c("Plant", "Type", "Treatment", "conc", "uptake")
          ),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      size_by = 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
        )
      ),
      row_facet = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      col_facet = data_extract_spec(
        dataname = "CO2",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  )
)
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 = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1", "BMRKR2")),
          selected = "AGE",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      y = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1", "BMRKR2")),
          selected = "BMRKR1",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      color_by = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(
            data[["ADSL"]],
            c("AGE", "BMRKR1", "BMRKR2", "RACE", "REGION1")
          ),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      size_by = 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
        )
      ),
      row_facet = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("BMRKR2", "RACE", "REGION1")),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      col_facet = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("BMRKR2", "RACE", "REGION1")),
          selected = NULL,
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  )
)
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,
  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 (trellis - output of lattice::splom)

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.

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").

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(
        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", "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
          )
        )
      )
    )
  )
)
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(
        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
          )
        )
      )
    )
  )
)
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 (grob created with ggplot2::ggplotGrob())

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

• by_subject_plot (ggplot)

• table (datatables created with 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_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_subject_plot = teal_transform_module(...), # applied only to `by_subject_plot` output
     table = teal_transform_module(...) # applied only to `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.

Examples in Shinylive

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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,
  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,
  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)

• table (datatables created with 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_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
     table = teal_transform_module(...) # applied only to `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.

Examples in Shinylive

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example-2

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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
          )
        )
      )
    )
  )
)
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
          )
        )
      )
    )
  )
)
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,
  y,
  show_percentage = TRUE,
  show_total = TRUE,
  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.

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.

Examples in Shinylive

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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 = data_extract_spec(
        dataname = "mtcars",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["mtcars"]], c("cyl", "vs", "am", "gear")),
          selected = c("cyl", "gear"),
          multiple = TRUE,
          ordered = TRUE,
          fixed = FALSE
        )
      ),
      y = data_extract_spec(
        dataname = "mtcars",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["mtcars"]], c("cyl", "vs", "am", "gear")),
          selected = "vs",
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  )
)
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 = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          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
        )
      ),
      y = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          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
        )
      )
    )
  )
)
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

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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)
}

Validates the variable browser inputs

Description

Validates the variable browser inputs

Usage

validate_input(input, plot_var, data)

Arguments

Value

logical TRUE if validations pass; a shiny validation error otherwise

Summarize NAs.

Description

Summarizes occurrence of missing values in vector.

Usage

var_missings_info(x)

Arguments

Value

Character string describing NA occurrence.

Summarizes variable

Description

Creates html summary with statistics relevant to data type. For numeric values it returns central tendency measures, for factor returns level counts, for Date date range, for other just number of levels.

Usage

var_summary_table(x, numeric_as_factor, dt_rows, outlier_definition)

Arguments

Value

text with simple statistics.

Get icons to represent variable types in dataset

Description

Get icons to represent variable types in dataset

Usage

variable_type_icons(var_type)

Arguments

Value

(character) vector of HTML icons corresponding to data type in each column.

Generate a string for a variable including its label

Description

Generate a string for a variable including its label

Usage

varname_w_label(
  var_names,
  dataset,
  wrap_width = 80,
  prefix = NULL,
  suffix = NULL
)

Arguments

Value

(character) String with variable name and label.