Package 'teal.modules.general'

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))

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
))

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,
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_a_pca

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

• elbow_plot (ggplot2)

• circle_plot (ggplot2)

• biplot (ggplot2)

• eigenvector_plot (ggplot2)

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

tm_a_pca(
   ..., # arguments for module
   decorators = list(
     default = list(teal_transform_module(...)), # applied to all outputs
     elbow_plot = list(teal_transform_module(...)), # applied only to `elbow_plot` output
     circle_plot = list(teal_transform_module(...)) # applied only to `circle_plot` output
     biplot = list(teal_transform_module(...)) # applied only to `biplot` output
     eigenvector_plot = list(teal_transform_module(...)) # applied only to `eigenvector_plot` output
   )
)

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

Examples

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

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),
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_a_regression

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

• plot (ggplot2)

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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)
}

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 = character(0),
  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,
  decorators = NULL
)

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.

Decorating tm_data_table

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

• table (DT::datatable())

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

Examples

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

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

Open in Shinylive

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)
}

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 = FALSE
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Examples in Shinylive

example-1

Open in Shinylive

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"),
      show_metadata = TRUE
    )
  ),
  header = tags$h1("Sample Application"),
  footer = tags$p("Application footer"),
)

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

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(),
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_g_association

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

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

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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)
}

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,
  decorators = NULL
)

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 tm_g_bivariate

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

• plot (ggplot2)

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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)
}

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,
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_g_distribution

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

• histogram_plot (ggplot2)

• qq_plot (data.frame)

• summary_table (data.frame)

• test_table (data.frame)

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

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

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

Examples

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

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,
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_g_response

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

• plot (ggplot2)

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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)
}

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(),
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_g_scatterplot

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

• plot (ggplot2)

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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)
}

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,
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_g_scatterplotmatrix

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

• plot (trellis - output of lattice::splom)

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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", "ES", "PT", "GR", "NL", "BE", "LU", "AT"),
            multiple = TRUE
          ),
          select = select_spec(
            label = "Select variables:",
            choices = variable_choices(data[["sales"]], c("quantity", "costs", "profit")),
            selected = c("quantity", "costs", "profit"),
            multiple = TRUE,
            ordered = TRUE,
            fixed = FALSE
          )
        )
      )
    )
  )
)
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)
}

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,
  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,
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_missing_data

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 (ggplot2)

• table (DT::datatable())

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

tm_missing_data(
   ..., # arguments for module
   decorators = list(
     default = list(teal_transform_module(...)), # applied to all outputs
     summary_plot = list(teal_transform_module(...)), # applied only to `summary_plot` output
     combination_plot = list(teal_transform_module(...)) # applied only to `combination_plot` output
     by_subject_plot = list(teal_transform_module(...)) # applied only to `by_subject_plot` output
     table = list(teal_transform_module(...)) # applied only to `table` output
   )
)

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

Examples

# general 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()
  )
)
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)
}

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,
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_outliers

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

• box_plot (ggplot2)

• density_plot (ggplot2)

• cumulative_plot (ggplot2)

• table (DT::datatable())

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

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

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

Examples

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

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

app <- init(
  data = data,
  modules = modules(
    tm_outliers(
      outlier_var = list(
        data_extract_spec(
          dataname = "CO2",
          select = select_spec(
            label = "Select variable:",
            choices = variable_choices(data[["CO2"]], c("conc", "uptake")),
            selected = "uptake",
            multiple = FALSE,
            fixed = FALSE
          )
        )
      ),
      categorical_var = list(
        data_extract_spec(
          dataname = "CO2",
          filter = filter_spec(
            vars = vars,
            choices = value_choices(data[["CO2"]], vars$selected),
            selected = value_choices(data[["CO2"]], vars$selected),
            multiple = TRUE
          )
        )
      )
    )
  )
)
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)
}

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(),
  decorators = NULL
)

Arguments

Value

Object of class teal_module to be used in teal applications.

Decorating tm_t_crosstable

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

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

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

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

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)
}

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 = character(0),
  parent_dataname = "ADSL",
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args()
)

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

Open in Shinylive

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)
}