Package 'visOmopResults'

Title: Graphs and Tables for OMOP Results
Description: Provides methods to transform omop_result objects into formatted tables and figures, facilitating the visualization of study results working with the Observational Medical Outcomes Partnership (OMOP) Common Data Model.
Authors: Martí Català [aut] , Núria Mercadé-Besora [aut, cre] , Yuchen Guo [aut]
Maintainer: Núria Mercadé-Besora <[email protected]>
License: Apache License (>= 2)
Version: 0.4.00001
Built: 2024-11-09 19:13:21 UTC
Source: https://github.com/darwin-eu/visomopresults

Help Index

Identify variables in additional_name column

Description

Identifies and returns the unique values in additional_name column.

Usage

additionalColumns(result)

Arguments

result

A tibble.

Value

Unique values of the additional name column.

Examples

mockSummarisedResult() |>
  additionalColumns()

Add settings columns to a ⁠<summarised_result>⁠ object

Description

Add settings columns to a ⁠<summarised_result>⁠ object

Usage

addSettings(
  result,
  settingsColumns = colnames(settings(result)),
  columns = lifecycle::deprecated()
)

Arguments

result

A ⁠<summarised_result>⁠ object.
settingsColumns

Settings to be added as columns, by default all settings will be added. If NULL or empty character vector, no settings will be added.
columns

[Deprecated]

Value

A ⁠<summarised_result>⁠ object with the added setting columns.

Examples

library(visOmopResults)
mockSummarisedResult() |>
  addSettings(settingsColumns = c("result_type"))

Create a bar plot visualisation from a ⁠<summarised_result>⁠ object

Description

[Experimental]

Usage

barPlot(result, x, y, facet = NULL, colour = NULL)

Arguments

result

A ⁠<summarised_result>⁠ object.
x

Column or estimate name that is used as x variable.
y

Column or estimate name that is used as y variable
facet

Variables to facet by, a formula can be provided to specify which variables should be used as rows and which ones as columns.
colour

Columns to use to determine the colors.

Value

A plot object.

Examples

result <- mockSummarisedResult() |> dplyr::filter(variable_name == "age")

barPlot(
  result = result,
  x = "cohort_name",
  y = "mean",
  facet = c("age_group", "sex"),
  colour = "sex")

Create a box plot visualisation from a ⁠<summarised_result>⁠ object

Description

[Experimental]

Usage

boxPlot(
  result,
  x = NULL,
  lower = "q25",
  middle = "median",
  upper = "q75",
  ymin = "min",
  ymax = "max",
  facet = NULL,
  colour = NULL
)

Arguments

result

A ⁠<summarised_result>⁠ object.
x

Columns to use as x axes.
lower

Estimate name for the lower quantile of the box.
middle

Estimate name for the middle line of the box.
upper

Estimate name for the upper quantile of the box.
ymin

Estimate name for the lower limit of the bars.
ymax

Estimate name for the upper limit of the bars.
facet

Variables to facet by, a formula can be provided to specify which variables should be used as rows and which ones as columns.
colour

Columns to use to determine the colors.

Value

A ggplot2 object.

Filter the additional_name-additional_level pair in a summarised_result

Description

Filter the additional_name-additional_level pair in a summarised_result

Usage

filterAdditional(result, ...)

Arguments

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (additionalColumns() are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

Value

A ⁠<summarised_result>⁠ object with only the rows that fulfill the required specified additional.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = "cohort_name",
  "group_level" = c("cohort1", "cohort2", "cohort3"),
  "strata_name" = "sex",
  "strata_level" = "Female",
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = c("year", "time_step", "year &&& time_step"),
  "additional_level" = c("2010", "4", "2015 &&& 5")
) |>
  newSummarisedResult()

x |>
  filterAdditional(year == "2010")

Filter the group_name-group_level pair in a summarised_result

Description

Filter the group_name-group_level pair in a summarised_result

Usage

filterGroup(result, ...)

Arguments

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (groupColumns() are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

Value

A ⁠<summarised_result>⁠ object with only the rows that fulfill the required specified group.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = c("cohort_name", "age_group &&& cohort_name", "age_group"),
  "group_level" = c("my_cohort", ">40 &&& second_cohort", "<40"),
  "strata_name" = "sex",
  "strata_level" = "Female",
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult()

x |>
  filterGroup(cohort_name == "second_cohort")

Filter a ⁠<summarised_result>⁠ using the settings

Description

Filter a ⁠<summarised_result>⁠ using the settings

Usage

filterSettings(result, ...)

Arguments

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (columns in settings are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

Value

A ⁠<summarised_result>⁠ object with only the result_id rows that fulfill the required specified settings.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = as.integer(c(1, 2)),
  "cdm_name" = c("cprd", "eunomia"),
  "group_name" = "sex",
  "group_level" = "male",
  "strata_name" = "sex",
  "strata_level" = "male",
  "variable_name" = "Age group",
  "variable_level" = "10 to 50",
  "estimate_name" = "count",
  "estimate_type" = "numeric",
  "estimate_value" = "5",
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult(settings = tibble(
    "result_id" = c(1, 2), "custom" = c("A", "B")
  ))

x

x |> filterSettings(custom == "A")

Filter the strata_name-strata_level pair in a summarised_result

Description

Filter the strata_name-strata_level pair in a summarised_result

Usage

filterStrata(result, ...)

Arguments

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (strataColumns() are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

Value

A ⁠<summarised_result>⁠ object with only the rows that fulfill the required specified strata.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = "cohort_name",
  "group_level" = "my_cohort",
  "strata_name" = c("sex", "sex &&& age_group", "sex &&& year"),
  "strata_level" = c("Female", "Male &&& <40", "Female &&& 2010"),
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult()

x |>
  filterStrata(sex == "Female")

Formats estimate_name and estimate_value column

Description

Formats estimate_name and estimate_value columns by changing the name of the estimate name and/or joining different estimates together in a single row.

Usage

formatEstimateName(
  result,
  estimateName = NULL,
  keepNotFormatted = TRUE,
  useFormatOrder = TRUE,
  estimateNameFormat = lifecycle::deprecated()
)

Arguments

result

A ⁠<summarised_result>⁠.
estimateName

Named list of estimate name's to join, sorted by computation order. Indicate estimate_name's between <...>.
keepNotFormatted

Whether to keep rows not formatted.
useFormatOrder

Whether to use the order in which estimate names appear in the estimateName (TRUE), or use the order in the input dataframe (FALSE).
estimateNameFormat

deprecated.

Value

A ⁠<summarised_result>⁠ object.

Examples

result <- mockSummarisedResult()
result |>
  formatEstimateName(
    estimateName = c(
      "N (%)" = "<count> (<percentage>%)", "N" = "<count>"
    ),
    keepNotFormatted = FALSE
  )

Formats the estimate_value column

Description

Formats the estimate_value column of ⁠<summarised_result>⁠ object by editing number of decimals, decimal and thousand/millions separator marks.

Usage

formatEstimateValue(
  result,
  decimals = c(integer = 0, numeric = 2, percentage = 1, proportion = 3),
  decimalMark = ".",
  bigMark = ","
)

Arguments

result

A ⁠<summarised_result>⁠.
decimals

Number of decimals per estimate type (integer, numeric, percentage, proportion), estimate name, or all estimate values (introduce the number of decimals).
decimalMark

Decimal separator mark.
bigMark

Thousand and millions separator mark.

Value

A ⁠<summarised_result>⁠.

Examples

result <- mockSummarisedResult()

result |> formatEstimateValue(decimals = 1)

result |> formatEstimateValue(decimals = c(integer = 0, numeric = 1))

result |>
  formatEstimateValue(decimals = c(numeric = 1, count = 0))

Create a header for gt and flextable objects

Description

Pivots a ⁠<summarised_result>⁠ object based on the column names in header, generating specific column names for subsequent header formatting in formatTable function.

Usage

formatHeader(
  result,
  header,
  delim = "\n",
  includeHeaderName = TRUE,
  includeHeaderKey = TRUE
)

Arguments

result

A ⁠<summarised_result>⁠.
header

Names of the variables to make headers.
delim

Delimiter to use to separate headers.
includeHeaderName

Whether to include the column name as header.
includeHeaderKey

Whether to include the header key (header, header_name, header_level) before each header type in the column names.

Value

A tibble with rows pivotted into columns with key names for subsequent header formatting.

Examples

result <- mockSummarisedResult()

result |>
  formatHeader(
    header = c(
      "Study cohorts", "group_level", "Study strata", "strata_name",
      "strata_level"
    ),
    includeHeaderName = FALSE
  )

Creates a flextable or gt object from a dataframe

Description

Creates a flextable object from a dataframe using a delimiter to span the header, and allows to easily customise table style.

Usage

formatTable(
  x,
  type = "gt",
  delim = "\n",
  style = "default",
  na = "-",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  groupColumn = NULL,
  groupAsColumn = FALSE,
  groupOrder = NULL,
  merge = NULL
)

Arguments

x

A dataframe.
type

The desired format of the output table. See tableType() for allowed options. If "tibble", no formatting will be applied.
delim

Delimiter.
style

Named list that specifies how to style the different parts of the gt or flextable table generated. Accepted style entries are: title, subtitle, header, header_name, header_level, column_name, group_label, and body. Alternatively, use "default" to get visOmopResults style, or NULL for gt/flextable style. Keep in mind that styling code is different for gt and flextable. To see the "deafult" style code use tableStyle().
na

How to display missing values.
title

Title of the table, or NULL for no title.
subtitle

Subtitle of the table, or NULL for no subtitle.
caption

Caption for the table, or NULL for no caption. Text in markdown formatting style (e.g. ⁠*Your caption here*⁠ for caption in italics).
groupColumn

Specifies the columns to use for group labels. By default, the new group name will be a combination of the column names, joined by "_". To assign a custom group name, provide a named list such as: list(newGroupName = c("variable_name", "variable_level"))
groupAsColumn

Whether to display the group labels as a column (TRUE) or rows (FALSE).
groupOrder

Order in which to display group labels.
merge

Names of the columns to merge vertically when consecutive row cells have identical values. Alternatively, use "all_columns" to apply this merging to all columns, or use NULL to indicate no merging.

Value

A flextable object.

A flextable or gt object.

Examples

# Example 1
mockSummarisedResult() |>
  formatEstimateValue(decimals = c(integer = 0, numeric = 1)) |>
  formatHeader(
    header = c("Study strata", "strata_name", "strata_level"),
    includeHeaderName = FALSE
  ) |>
  formatTable(
    type = "flextable",
    style = "default",
    na = "--",
    title = "fxTable example",
    subtitle = NULL,
    caption = NULL,
    groupColumn = "group_level",
    groupAsColumn = TRUE,
    groupOrder = c("cohort1", "cohort2"),
    merge = "all_columns"
  )

# Example 2
mockSummarisedResult() |>
  formatEstimateValue(decimals = c(integer = 0, numeric = 1)) |>
  formatHeader(header = c("Study strata", "strata_name", "strata_level"),
              includeHeaderName = FALSE) |>
  formatTable(
    type = "gt",
    style = list("header" = list(
      gt::cell_fill(color = "#d9d9d9"),
      gt::cell_text(weight = "bold")),
      "header_level" = list(gt::cell_fill(color = "#e1e1e1"),
                            gt::cell_text(weight = "bold")),
      "column_name" = list(gt::cell_text(weight = "bold")),
      "title" = list(gt::cell_text(weight = "bold"),
                     gt::cell_fill(color = "#c8c8c8")),
      "group_label" = gt::cell_fill(color = "#e1e1e1")),
    na = "--",
    title = "gtTable example",
    subtitle = NULL,
    caption = NULL,
    groupColumn = "group_level",
    groupAsColumn = FALSE,
    groupOrder = c("cohort1", "cohort2"),
    merge = "all_columns"
  )

Creates a flextable object from a dataframe

Description

[Deprecated] Creates a flextable object from a dataframe using a delimiter to span the header, and allows to easily customise table style.

Usage

fxTable(
  x,
  delim = "\n",
  style = "default",
  na = "-",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  groupColumn = NULL,
  groupAsColumn = FALSE,
  groupOrder = NULL,
  colsToMergeRows = NULL
)

Arguments

x

A dataframe.
delim

Delimiter.
style

Named list that specifies how to style the different parts of the gt or flextable table generated. Accepted style entries are: title, subtitle, header, header_name, header_level, column_name, group_label, and body. Alternatively, use "default" to get visOmopResults style, or NULL for gt/flextable style. Keep in mind that styling code is different for gt and flextable. To see the "deafult" gt style code use tableStyle().
na

How to display missing values.
title

Title of the table, or NULL for no title.
subtitle

Subtitle of the table, or NULL for no subtitle.
caption

Caption for the table, or NULL for no caption. Text in markdown formatting style (e.g. ⁠*Your caption here*⁠ for caption in italics).
groupColumn

Specifies the columns to use for group labels. By default, the new group name will be a combination of the column names, joined by "_". To assign a custom group name, provide a named list such as: list(newGroupName = c("variable_name", "variable_level"))
groupAsColumn

Whether to display the group labels as a column (TRUE) or rows (FALSE).
groupOrder

Order in which to display group labels.
colsToMergeRows

Names of the columns to merge vertically when consecutive row cells have identical values. Alternatively, use "all_columns" to apply this merging to all columns, or use NULL to indicate no merging.

Value

A flextable object.

A flextable object.

Identify variables in group_name column

Description

Identifies and returns the unique values in group_name column.

Usage

groupColumns(result)

Arguments

result

A tibble.

Value

Unique values of the group name column.

Examples

mockSummarisedResult() |>
  groupColumns()

Creates a gt object from a dataframe

Description

[Deprecated] Creates a flextable object from a dataframe using a delimiter to span the header, and allows to easily customise table style.

Usage

gtTable(
  x,
  delim = "\n",
  style = "default",
  na = "-",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  groupColumn = NULL,
  groupAsColumn = FALSE,
  groupOrder = NULL,
  colsToMergeRows = NULL
)

Arguments

x

A dataframe.
delim

Delimiter.
style

Named list that specifies how to style the different parts of the gt or flextable table generated. Accepted style entries are: title, subtitle, header, header_name, header_level, column_name, group_label, and body. Alternatively, use "default" to get visOmopResults style, or NULL for gt/flextable style. Keep in mind that styling code is different for gt and flextable. To see the "deafult" style code use tableStyle().
na

How to display missing values.
title

Title of the table, or NULL for no title.
subtitle

Subtitle of the table, or NULL for no subtitle.
caption

Caption for the table, or NULL for no caption. Text in markdown formatting style (e.g. ⁠*Your caption here*⁠ for caption in italics).
groupColumn

Specifies the columns to use for group labels. By default, the new group name will be a combination of the column names, joined by "_". To assign a custom group name, provide a named list such as: list(newGroupName = c("variable_name", "variable_level"))
groupAsColumn

Whether to display the group labels as a column (TRUE) or rows (FALSE).
groupOrder

Order in which to display group labels.
colsToMergeRows

Names of the columns to merge vertically when consecutive row cells have identical values. Alternatively, use "all_columns" to apply this merging to all columns, or use NULL to indicate no merging.

Value

gt object.

A gt table.

A ⁠<summarised_result>⁠ object filled with mock data

Description

Creates an object of the class ⁠<summarised_result>⁠ with mock data for illustration purposes.

Usage

mockSummarisedResult()

Value

An object of the class ⁠<summarised_result>⁠ with mock data.

Examples

mockSummarisedResult()

Additional table formatting options

Description

[Deprecated]

Usage

optionsVisOmopTable()

Value

list of options

Set estimates as columns

Description

Pivot the estimates as new columns in result table.

Usage

pivotEstimates(result, pivotEstimatesBy = "estimate_name", nameStyle = NULL)

Arguments

result

A ⁠<summarised_result>⁠.
pivotEstimatesBy

Names from which pivot wider the estimate values. If NULL the table will not be pivotted.
nameStyle

Name style (glue package specifications) to customise names when pivotting estimates. If NULL standard tidyr::pivot_wider formatting will be used.

Value

A tibble.

Examples

result <- mockSummarisedResult()
result |> pivotEstimates()

Create a scatter plot visualisation from a ⁠<summarised_result>⁠ object

Description

[Experimental]

Usage

scatterPlot(
  result,
  x,
  y,
  line,
  point,
  ribbon,
  ymin = NULL,
  ymax = NULL,
  facet = NULL,
  colour = NULL,
  group = colour
)

Arguments

result

A ⁠<summarised_result>⁠ object.
x

Column or estimate name that is used as x variable.
y

Column or estimate name that is used as y variable
line

Whether to plot a line using geom_line.
point

Whether to plot points using geom_point.
ribbon

Whether to plot a ribbon using geom_ribbon.
ymin

Lower limit of error bars, if provided is plot using geom_errorbar.
ymax

Upper limit of error bars, if provided is plot using geom_errorbar.
facet

Variables to facet by, a formula can be provided to specify which variables should be used as rows and which ones as columns.
colour

Columns to use to determine the colors.
group

Columns to use to determine the group.

Value

A plot object.

Examples

result <- mockSummarisedResult() |>
  dplyr::filter(variable_name == "age")

scatterPlot(
  result = result,
  x = "cohort_name",
  y = "mean",
  line = TRUE,
  point = TRUE,
  ribbon = FALSE,
  facet = age_group ~ sex)

Identify settings columns of a ⁠<summarised_result>⁠

Description

Identifies and returns the columns of the settings table obtained by using settings() in a ⁠<summarised_result>⁠ object.

Usage

settingsColumns(result)

Arguments

result

A ⁠<summarised_result>⁠.

Value

Vector with names of the settings columns

Examples

mockSummarisedResult() |>
  settingsColumns()

Split additional_name and additional_level columns

Description

Pivots the input dataframe so the values of the column additional_name are transformed into columns that contain values from the additional_level column.

Usage

splitAdditional(result, keep = FALSE, fill = "overall")

Arguments

result

A dataframe with at least the columns additional_name and additional_level.
keep

Whether to keep the original group_name and group_level columns.
fill

Optionally, a character that specifies what value should be filled in with when missing.

Value

A dataframe.

Examples

mockSummarisedResult() |>
  splitAdditional()

Split all pairs name-level into columns.

Description

Pivots the input dataframe so any pair name-level columns are transformed into columns (name) that contain values from the corresponding level.

Usage

splitAll(result, keep = FALSE, fill = "overall", exclude = "variable")

Arguments

result

A data.frame.
keep

Whether to keep the original name-level columns.
fill

A character that specifies what value should be filled in when missing.
exclude

Name of a column pair to exclude.

Value

A dataframe with group, strata and additional as columns.

Examples

mockSummarisedResult() |>
  splitAll()

Split group_name and group_level columns

Description

Pivots the input dataframe so the values of the column group_name are transformed into columns that contain values from the group_level column.

Usage

splitGroup(result, keep = FALSE, fill = "overall")

Arguments

result

A dataframe with at least the columns group_name and group_level.
keep

Whether to keep the original group_name and group_level columns.
fill

Optionally, a character that specifies what value should be filled in with when missing.

Value

A dataframe.

Examples

mockSummarisedResult() |>
  splitGroup()

Split name and level columns into the columns

Description

[Deprecated] Pivots the input dataframe so the values of the name columns are transformed into columns, which values come from the specified level column.

Usage

splitNameLevel(
  result,
  name = "group_name",
  level = "group_level",
  keep = FALSE,
  fill = "overall"
)

Arguments

result

A ⁠<summarised_result>⁠ object.
name

Column with the names.
level

Column with the levels.
keep

Whether to keep the original group_name and group_level columns.
fill

Optionally, a character that specifies what value should be filled in with when missing.

Value

A dataframe with the specified name column values as columns.

Split strata_name and strata_level columns

Description

Pivots the input dataframe so the values of the column strata_name are transformed into columns that contain values from the strata_level column.

Usage

splitStrata(result, keep = FALSE, fill = "overall")

Arguments

result

A dataframe with at least the columns strata_name and strata_level.
keep

Whether to keep the original group_name and group_level columns.
fill

Optionally, a character that specifies what value should be filled in with when missing.

Value

A dataframe.

Examples

mockSummarisedResult() |>
  splitStrata()

Identify variables in strata_name column

Description

Identifies and returns the unique values in strata_name column.

Usage

strataColumns(result)

Arguments

result

A tibble.

Value

Unique values of the strata name column.

Examples

mockSummarisedResult() |>
  strataColumns()

Additional table formatting options for visOmopTable() and visTable()

Description

This function provides a list of allowed inputs for the .option argument in visOmopTable() and visTable(), and their corresponding default values.

Usage

tableOptions()

Value

A named list of default options for table customization.

Examples

tableOptions()

Supported predefined styles for formatted tables

Description

Supported predefined styles for formatted tables

Usage

tableStyle(type = "gt", styleName = "default")

Arguments

type

Character string specifying the formatted table class. See tableType() for supported classes. Default is "gt".
styleName

A character string specifying the style name. Currently, the package supports only one predefined style: "default".

Value

A code expression for the selected style and table type.

Examples

tableStyle("gt")
tableStyle("flextable")

Supported table classes

Description

This function returns the supported table classes that can be used in the type argument of visOmopTable(), visTable(), and formatTable() functions.

Usage

tableType()

Value

A character vector of supported table types.

Examples

tableType()

Turn a ⁠<summarised_result>⁠ object into a tidy tibble

Description

[Experimental] Provides tools for obtaining a tidy version of a ⁠<summarised_result>⁠ object. This tidy version will include the settings as columns, estimate_value will be pivotted into columns using estimate_name as names, and group, strata, and additional will be splitted. If you want to customise these tidy operations, please use tidySummarisedResult().

Usage

## S3 method for class 'summarised_result'
tidy(x, ...)

Arguments

x

A ⁠<summarised_result>⁠.
...

For compatibility (not used).

Value

A tibble.

Examples

result <- mockSummarisedResult()
result |> tidy()

Identify tidy columns of a ⁠<summarised_result>⁠

Description

Identifies and returns the columns that the tidy version of the ⁠<summarised_result>⁠ will have.

Usage

tidyColumns(result)

Arguments

result

A ⁠<summarised_result>⁠.

Value

Table columns after applying tidy() function to a ⁠<summarised_result>⁠.

Examples

mockSummarisedResult() |>
  tidyColumns()

Unite one or more columns in additional_name-additional_level format

Description

Unites targeted table columns into additional_name-additional_level columns.

Usage

uniteAdditional(
  x,
  cols = character(0),
  keep = FALSE,
  ignore = c(NA, "overall")
)

Arguments

x

Tibble or dataframe.
cols

Columns to aggregate.
keep

Whether to keep the original columns.
ignore

Level values to ignore.

Value

A tibble with the new columns.

Examples

x <- dplyr::tibble(
  variable = "number subjects",
  value = c(10, 15, 40, 78),
  sex = c("Male", "Female", "Male", "Female"),
  age_group = c("<40", ">40", ">40", "<40")
)

x |>
  uniteAdditional(c("sex", "age_group"))

Unite one or more columns in group_name-group_level format

Description

Unites targeted table columns into group_name-group_level columns.

Usage

uniteGroup(x, cols = character(0), keep = FALSE, ignore = c(NA, "overall"))

Arguments

x

Tibble or dataframe.
cols

Columns to aggregate.
keep

Whether to keep the original columns.
ignore

Level values to ignore.

Value

A tibble with the new columns.

Examples

x <- dplyr::tibble(
  variable = "number subjects",
  value = c(10, 15, 40, 78),
  sex = c("Male", "Female", "Male", "Female"),
  age_group = c("<40", ">40", ">40", "<40")
)

x |>
  uniteGroup(c("sex", "age_group"))

Unite one or more columns in name-level format

Description

[Deprecated] Unites targeted table columns into a pair of name-level columns.

Usage

uniteNameLevel(
  x,
  cols = character(0),
  name = "group_name",
  level = "group_level",
  keep = FALSE,
  ignore = c(NA, "overall")
)

Arguments

x

A dataframe.
cols

Columns to aggregate.
name

Column name of the name column.
level

Column name of the level column.
keep

Whether to keep the original columns.
ignore

Level values to ignore.

Value

A tibble with the new columns.

Unite one or more columns in strata_name-strata_level format

Description

Unites targeted table columns into strata_name-strata_level columns.

Usage

uniteStrata(x, cols = character(0), keep = FALSE, ignore = c(NA, "overall"))

Arguments

x

Tibble or dataframe.
cols

Columns to aggregate.
keep

Whether to keep the original columns.
ignore

Level values to ignore.

Value

A tibble with the new columns.

Examples

x <- dplyr::tibble(
  variable = "number subjects",
  value = c(10, 15, 40, 78),
  sex = c("Male", "Female", "Male", "Female"),
  age_group = c("<40", ">40", ">40", "<40")
)

x |>
  uniteStrata(c("sex", "age_group"))

Format a ⁠<summarised_result>⁠ object into a gt, flextable, or tibble object

Description

This function combines the functionalities of formatEstimateValue(), estimateName(), formatHeader(), and formatTable() into a single function specifically for ⁠<summarised_result>⁠ objects.

Usage

visOmopTable(
  result,
  estimateName = character(),
  header = character(),
  settingsColumns = character(),
  groupColumn = character(),
  rename = character(),
  type = "gt",
  hide = character(),
  showMinCellCount = TRUE,
  .options = list(),
  split = lifecycle::deprecated(),
  excludeColumns = lifecycle::deprecated(),
  formatEstimateName = lifecycle::deprecated(),
  renameColumns = lifecycle::deprecated()
)

Arguments

result

A ⁠<summarised_result>⁠ object.
estimateName

A named list of estimate names to join, sorted by computation order. Use ⁠<...>⁠ to indicate estimate names.
header

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. The input vector elements can be:

  1. Column names from the split summarised result generated by splitAll()

  2. Settings specified in the settings argument

  3. group, strata, additional, variable, estimate, and/or settings to refer to all columns within these groups

  4. Any other input to create overall header labels at the specified location.

settingsColumns

A character vector with the names of settings to include in the table.
groupColumn

Columns to use as group labels. By default, the name of the new group will be the tidy* column names separated by ";". To specify a custom group name, use a named list such as: list("newGroupName" = c("variable_name", "variable_level")).

*tidy: The tidy format applied to column names replaces "_" with a space and converts to sentence case. Use rename to customize specific column names.
rename

A named vector to customize column names, e.g., c("Database name" = "cdm_name"). The function renames all column names not specified here into a tidy* format.
type

The desired format of the output table. See tableType() for allowed options.
hide

Columns to drop from the output table. By default, result_id and estimate_type are always dropped.
showMinCellCount

If TRUE, suppressed estimates will be indicated with "<{min_cell_count}", otherwise, the default na defined in .options will be used.
.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.
split

[Deprecated]
excludeColumns

[Deprecated]
formatEstimateName

[Deprecated]
renameColumns

[Deprecated]

Value

A tibble, gt, or flextable object.

Examples

result <- mockSummarisedResult()
result |>
  visOmopTable(
    estimateName = c("N%" = "<count> (<percentage>)",
                     "N" = "<count>",
                     "Mean (SD)" = "<mean> (<sd>)"),
    header = c("group"),
    rename = c("Database name" = "cdm_name"),
    groupColumn = strataColumns(result)
  )

Generate a formatted table from a ⁠<data.table>⁠

Description

[Experimental] This function combines the functionalities of formatEstimateValue(), formatEstimateName(), formatHeader(), and formatTable() into a single function. While it does not require the input table to be a ⁠<summarised_result>⁠, it does expect specific fields to apply some formatting functionalities.

Usage

visTable(
  result,
  estimateName = character(),
  header = character(),
  groupColumn = character(),
  rename = character(),
  type = "gt",
  hide = character(),
  .options = list()
)

Arguments

result

A table to format.
estimateName

A named list of estimate names to join, sorted by computation order. Use ⁠<...>⁠ to indicate estimate names. This argument requires that the table has estimate_name and estimate_value columns.
header

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. The vector elements can be column names or labels for overall headers. The table must contain an estimate_value column to pivot the headers.
groupColumn

Columns to use as group labels. By default, the name of the new group will be the tidy* column names separated by ";". To specify a custom group name, use a named list such as: list("newGroupName" = c("variable_name", "variable_level")).

*tidy: The tidy format applied to column names replaces "_" with a space and converts them to sentence case. Use rename to customize specific column names.
rename

A named vector to customize column names, e.g., c("Database name" = "cdm_name"). The function will rename all column names not specified here into a tidy* format.
type

The desired format of the output table. See tableType() for allowed options.
hide

Columns to drop from the output table.
.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A tibble, gt, or flextable object.

Examples

result <- mockSummarisedResult()
result |>
  visTable(
    estimateName = c("N%" = "<count> (<percentage>)",
                     "N" = "<count>",
                     "Mean (SD)" = "<mean> (<sd>)"),
    header = c("Estimate"),
    rename = c("Database name" = "cdm_name"),
    groupColumn = c("strata_name", "strata_level"),
    hide = c("additional_name", "additional_level", "estimate_type", "result_type")
  )