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Title:	Applies Display Metadata to Analysis Results Datasets
Version:	0.2.1
Description:	Creates a framework to store and apply display metadata to Analysis Results Datasets (ARDs). The use of 'tfrmt' allows users to define table format and styling without the data, and later apply the format to the data.
License:	Apache License (≥ 2)
URL:	https://GSK-Biostatistics.github.io/tfrmt/,https://github.com/GSK-Biostatistics/tfrmt
BugReports:	https://github.com/GSK-Biostatistics/tfrmt/issues
Depends:	R (≥ 4.2.0)
Imports:	cli, dplyr, forcats, ggplot2, glue, gt (≥ 0.6.0), jsonlite,magrittr, purrr, rlang, stringi, stringr, tibble, tidyr,tidyselect
Suggests:	cards (≥ 0.6.0), covr, ggfortify, knitr, patchwork,pharmaverseadam, rmarkdown, survival, testthat (≥ 3.0.0),withr
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2025-10-03 21:15:57 UTC; bkrouse
Author:	Becca Krouse [aut, cre], Christina Fillmore [aut], Ellis Hughes [aut], Karima Ahmad [aut], Shannon Haughton [aut], Dragoș Moldovan-Grünfeld [aut], GlaxoSmithKline Research & Development Limited [cph, fnd], Atorus Research LLC [cph, fnd]
Maintainer:	Becca Krouse <becca.z.krouse@gsk.com>
Repository:	CRAN
Date/Publication:	2025-10-04 05:10:03 UTC

Pipe operator

Description

Seemagrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of callingrhs(lhs).

Apply formatting

Description

Apply formatting

Usage

apply_frmt(frmt_def, .data, value, mock = FALSE, ...)## S3 method for class 'frmt'apply_frmt(frmt_def, .data, value, mock = FALSE, ...)## S3 method for class 'frmt_combine'apply_frmt(  frmt_def,  .data,  value,  mock = FALSE,  param,  column,  label,  group,  ...)## S3 method for class 'frmt_when'apply_frmt(frmt_def, .data, value, mock = FALSE, ...)

Arguments

Value

formatted dataset

Examples

library(tibble)library(dplyr)# Set up datadf <- tibble(x = c(20.12,34.54,12.34))apply_frmt( frmt_def = frmt("XX.X"), .data=df, value=quo(x))

Convert tfrmt elements

Description

This collection of functions is used to create a json or json ready objects.While primarily intended for internal use these functions can be used externally

Usage

as_json(x)

Arguments

Value

as_json.tfrmt() will return a json object all other methods willreturn a list that is cleaned so it can be directly converted to a jsonobject

Big N Structure

Description

Big N structure allows you to specify which values should become the subjecttotals ("big N" values) and how they should be formatted in the table'scolumn labels. Values are specified by providing the value(s) of theparamcolumn for which the values are big N's. This will remove these from thebody of the table and place them into columns matching the values in thecolumn column(s). The default formatting isN = xx, on its own line, butthat can be changed by providing a differentfrmt() ton_frmt.

Usage

big_n_structure(param_val, n_frmt = frmt("\nN = xx"), by_page = FALSE)

Arguments

Value

big_n_structure object

See Also

Link to related article

Table Body Plan

Description

Define the formatting of the body contents of the table through a series offrmt_structures. Structures get applied in order from bottom up, so the lastadded structure is the first applied.

Usage

body_plan(...)

Arguments

Value

body_plan object

See Also

frmt_structure() defines which rows the formats will be appliedto, andfrmt(),frmt_combine(), andfrmt_when() define the formatsemantics.

Link to related article

Examples

  tfrmt_spec<- tfrmt(      title = "Table Title",      body_plan = body_plan(        frmt_structure(          group_val = c("group1"),          label_val = ".default",          frmt("XXX")        )      )     )

Do all the formatting for the GT

Description

Do all the formatting for the GT

Apply formatting to a list of tables

Apply formatting to a single table

Usage

cleaned_data_to_gt(.data, tfrmt, .unicode_ws)## S3 method for class 'list'cleaned_data_to_gt(.data, tfrmt, .unicode_ws)## Default S3 method:cleaned_data_to_gt(.data, tfrmt, .unicode_ws)

Arguments

Value

gt_group object

GT object

Define the Column Plan & Span Structures

Description

Using <tidy-select> expressions and a seriesspan_structures, define the order of the columns. The selection follows "lastselected" principals, meaning columns are moved to thelast selection asopposed to preserving the first location.

Usage

col_plan(..., .drop = FALSE)span_structure(...)

Arguments

Details

Column Selection

When col_plan gets applied and is used to create the output table, theunderlying logic sorts out which column specifically is being selected. If a columnis selected twice, thelast instance in which the column is selected will bethe location it gets rendered.

Avoid beginning thecol_plan() column selection with a deselection (i.e.col_plan(-col1),col_plan(-starts_with("value"))). This willresult in the table preserving all columns not "de-selected" in thestatement, and the order of the columns not changed. It is preferred whencreating thecol_plan() to identify all the columns planned onpreserving in the order they are wished to appear, or if<tidy-select> arguments - such aseverything- are used, identify the de-selection afterthe positive-selection.

Alternatively, once the gt table is produced, use thecols_hide function to remove un-wanted columns.

Value

col_plan object

span_structure object

Images

Here are some example outputs:

See Also

Link to related article

Examples

library(dplyr)## select col_1 as the first column, remove col_last, then create spanning## structures that have multiple levels#### examples also assume the tfrmt has the column argument set to c(c1, c2, c3)##spanning_col_plan_ex <- col_plan( col_1, -col_last, span_structure(   c1 = "Top Label Level 1",   c2 = "Second Label Level 1.1",   c3 = c(col_3, col_4) ), span_structure(   c1 = "Top Label Level 1",   c2 = "Second Label Level 1.2",   c3 = starts_with("B")   ), span_structure(   c1 = "Top Label Level 1",   c3 = col_5 ), span_structure(   c2 = "Top Label Level 2",   c3 = c(col_6, col_7) ))## select my_col_1 as the first column, then## rename col_2 to new_col_1 and put as the## second column, then select the rest of the columnsrenaming_col_plan_ex <- col_plan(   my_col_1,   new_col_1 = col_2,   everything() )renaming_col_plan_ex2 <- col_plan(   my_col_1,   new_col_1 = col_2,   span_structure(    c1 = c(`My Favorite span name` = "Top Label Level 1"),    c3 = c(`the results column` = col_5)   ) )

Column Style Plan

Description

Define how the columns of the table body should be aligned, whether left,right or on a specific character(s).

Usage

col_style_plan(...)

Arguments

Value

col_style_plan object

See Also

col_style_structure() for more information on how to specify how to and which columns to align.

Link to related article

Examples

 plan <- col_style_plan(    col_style_structure(col = "my_var", align = "left", width = 100),    col_style_structure(col = vars(four), align = "right"),    col_style_structure(col = vars(two, three), align = c(".", ",", " "))   )

Column Style Structure

Description

Column Style Structure

Usage

col_style_structure(  col,  align = NULL,  type = c("char", "pos"),  width = NULL,  ...)

Arguments

Details

Supports alignment and width setting of data value columns (values found in thecolumn column). Row group and label columns are left-aligned by default. Acceptable input values foralign differ by type = "char" or "pos":

Character alignment (type = "char"):

• "left" for left alignment

• "right" for right alignment"

• supply a vector of character(s) to align on. If more thanone character is provided, alignment will be based on the first occurrenceof any of the characters. For alignment based on white space, leading whitespaces will be ignored.

Positional alignment (type = "pos"):

supply a vector of strings covering all formatted cell values, with numeric values represented as x's. These values can be created manually or obtained by utilizing the helperdisplay_val_frmts(). Alignment positions will be represented by vertical bars. For example, with starting values: c("12.3", "(5%)", "2.35 (10.23)") we can align all of the first sets of decimals and parentheses by providing align = c("xx|.x", "||(x%)", "x|.xx |")

Value

col_style_structure object

See Also

col_style_plan() for more information on how to combinecol_style_structure()'s together to form a plan.

Link to related article

Examples

 plan <- col_style_plan(    col_style_structure(col = "my_var",                        align = c("xx| |(xx%)",                                  "xx|.x |(xx.x - xx.x)"),                        type = "pos", width = 100),    col_style_structure(col = vars(four), align = "right", width = 200),    col_style_structure(col = vars(two, three), align = c(".", ",", " ")),    col_style_structure(col = c(two, three), width = 25),    col_style_structure(col = two, width = 25),    col_style_structure(col = span_structure(span = value, col = val2),                        width = 25)   )

Adverse Events Analysis Results Data

Description

A dataset containing the results needed for an AE table. Using theCDISC pilot data.

Usage

data_ae

Format

A data frame with 2,794 rows and 8 variables:

AEBODSYS

highest level row labels: System Organ Class

AETERM

more specific row labels: Preferred Term

col2

higher level column names (spanners)

col1

lower level column names

param

parameter to explain each value

value

values to put in a table

ord1

controls ordering

ord2

more ordering controls

Demography Analysis Results Data

Description

A dataset containing the results needed for a demography table. Using theCDISC pilot data.

Usage

data_demog

Format

A data frame with 386 rows and 7 variables:

rowlbl1

highest level row labels

rowlbl2

more specific row labels

param

parameter to explain each value

grp

grouping column used to distinguish continuous and categorical

ord1

controls ordering

ord2

more ordering controls

column

column names

value

values to put in a table

Efficacy Analysis Results Data

Description

A dataset containing the results needed for an Efficacy table. Using theCDISC pilot data for ADAS-Cog(11).

Usage

data_efficacy

Format

A data frame with 70 rows and 7 variables:

group

highest level row labels

label

more specific row labels

column

column names

param

parameter to explain each value

value

values to put in a table

ord1

controls ordering

ord2

more ordering controls

Labs Analysis Results Data

Description

A dataset containing the results needed for an labs results table. Using theCDISC pilot data.

Usage

data_labs

Format

A data frame with 4,950 rows and 7 variables:

group1

highest level row labels: Lab value class

group2

more specific row labels: Lab parameter

rowlbl

most specific row labels: Study visit

col1

higher level column names (spanners)

col2

lower level column names

param

parameter to explain each value

value

values to put in a table

ord1

controls ordering

ord2

more ordering controls

ord3

more ordering controls

Display formatting applied to each row

Description

Used when debugging formatting, it is an easy way to allow you to see whichformats are applied to each row in your dataset.

Usage

display_row_frmts(tfrmt, .data, convert_to_txt = TRUE)

Arguments

Value

formatted tibble

Examples

 library(dplyr) library(tidyr) tfrmt_spec <- tfrmt( label = label, column = column, param = param, value=value, body_plan = body_plan(   frmt_structure(group_val = ".default", label_val = ".default",                  frmt_combine(                    "{count} {percent}",                    count = frmt("xxx"),                    percent = frmt_when("==100"~ frmt(""),                                        "==0"~ "",                                        "TRUE" ~ frmt("(xx.x%)")))) )) # Create data df <- crossing(label = c("label 1", "label 2"),                column = c("placebo", "trt1"),                param = c("count", "percent")) %>%   mutate(value=c(24,19,2400/48,1900/38,5,1,500/48,100/38)) display_row_frmts(tfrmt_spec,df)

Display formatted values

Description

A helper for creating positional-alignment specifications for the col_style_plan.Returns all unique formatted values to appear in the column(s) specified. Numeric values are represented by x's.

Usage

display_val_frmts(tfrmt, .data, mock = FALSE, col = NULL)

Arguments

Value

text representing character vector of formatted values to be copied and modified in the col_style_plan

Examples

 tf_spec <- tfrmt( group = c(rowlbl1,grp), label = rowlbl2, column = column, param = param, value = value, sorting_cols = c(ord1, ord2), body_plan = body_plan( frmt_structure(group_val = ".default", label_val = ".default", frmt_combine("{n} ({pct} %)",                                                                             n = frmt("xxx"),                                                                             pct = frmt("xx.x"))), frmt_structure(group_val = ".default", label_val = "n", frmt("xxx")), frmt_structure(group_val = ".default", label_val = c("Mean", "Median", "Min","Max"),                             frmt("xxx.x")), frmt_structure(group_val = ".default", label_val = "SD", frmt("xxx.xx")), frmt_structure(group_val = ".default", label_val = ".default",                            p = frmt_when(">0.99" ~ ">0.99",                                          "<0.15" ~ "<0.15",                                          TRUE ~ frmt("x.xxx", missing = ""))))) display_val_frmts(tf_spec, data_demog, col = vars(everything())) display_val_frmts(tf_spec, data_demog, col = "p-value")

Element block

Description

Element block

Usage

element_block(post_space = c(NULL, " ", "-"), fill = TRUE)

Arguments

Value

element block object

See Also

row_grp_plan() for more details on how to group row groupstructures,row_grp_structure() for more details on how to specify row groupstructures,element_row_grp_loc() for more details on how tospecify whether row group titles span the entire table or collapse.

Examples

tfrmt_spec <- tfrmt(  group = grp1,  label = label,  param = param,  value = value,  column = column,  row_grp_plan = row_grp_plan(    row_grp_structure(group_val = ".default", element_block(post_space = "   "))  ),  body_plan = body_plan(    frmt_structure(group_val = ".default", label_val = ".default", frmt("xx"))  ))

Element Row Group Location

Description

Element Row Group Location

Usage

element_row_grp_loc(  location = c("indented", "spanning", "column", "noprint", "gtdefault"),  indent = "  ")

Arguments

Value

element_row_grp_loc object

Images

Here are some example outputs:

See Also

row_grp_plan() for more details on how to group row groupstructures,row_grp_structure() for more details on how to specify rowgroup structures,element_block() for more details on how to specifyspacing between each group.

Linkto related article

Examples

tfrmt_spec <- tfrmt(  group = c(grp1, grp2),  label = label,  param = param,  value = value,  column = column,  row_grp_plan = row_grp_plan(label_loc = element_row_grp_loc(location = "noprint")),  body_plan = body_plan(    frmt_structure(group_val = ".default", label_val = ".default", frmt("xx"))  ))

Footnote Plan

Description

Defining the location and content of footnotes with a series of footnotestructures. Each structure is a footnote and can be applied in multiple locations.

Usage

footnote_plan(..., marks = c("numbers", "letters", "standard", "extended"))

Arguments

Value

footnote plan object

Examples

# Adds a footnote indicated by letters rather than numbers to Group 1footnote_plan <- footnote_plan(    footnote_structure(footnote_text = "Source Note", group_val = "Group 1"),    marks="letters")# Adds a footnote to the 'Placebo' columnfootnote_plan <- footnote_plan(    footnote_structure(footnote_text = "footnote", column_val = "Placebo"),    marks="numbers")

Footnote Structure

Description

Footnote Structure

Usage

footnote_structure(  footnote_text,  column_val = NULL,  group_val = NULL,  label_val = NULL)

Arguments

Value

footnote structure object

Examples

# Adds a source note aka a footnote without a symbol in the tablefootnote_structure <- footnote_structure(footnote_text = "Source Note")# Adds a footnote to the 'Placebo' columnfootnote_structure <- footnote_structure(footnote_text = "Text",                               column_val = "Placebo")# Adds a footnote to either 'Placebo' or 'Treatment groups' depending on which# which is last to appear in the column vectorfootnote_structure <- footnote_structure(footnote_text = "Text",      column_val = list(col1 = "Placebo", col2= "Treatment groups"))# Adds a footnote to the 'Adverse Event' labelfootnote_structure <- footnote_structure("Text", label_val = "Adverse Event")

Table Value Formatting

Description

These functions provide an abstracted way to approach to define formatting of tablecontents. By defining in this way, the formats can belayered to be more specific and general cell styling can be done first.

frmt() is the base definition of a format. This defines spacing, rounding,and missing behaviour.

frmt_combine() is used when two or more rows need to be combined into asingle cell in the table. Each of the rows needs to have a definedfrmt()and need to share a label.

frmt_when() is used when a rows format behaviour is dependent on the value itself and is written similarly todplyr::case_when().The left hand side of the equation is a"TRUE"for the default case or the right hand side of a boolean expression">50".

Usage

frmt(expression, missing = NULL, scientific = NULL, transform = NULL, ...)frmt_combine(expression, ..., missing = NULL)frmt_when(..., missing = NULL)

Arguments

Details

expression

• frmt() All numbers are represented by "x". Any additional character areprinted as-is. If additional X's present to the left of the decimal pointthan the value, they will be represented as spaces.

• frmt_combine() defines how the parameters will be combined as aglue::glue() statement. Parameters need to be equal to the values in theparam column and defined in the expression as"{param1} {param2}".

missing

• frmt() Value to enter when the value is missing. When NULL, the valueis "".

• frmt_combine() defines how when all values to be combined are missing.When NULL the value is "".

...

• frmt() These dots are for future extensions and must beempty.

• frmt_combine() accepts named arguments defining thefrmt() tobe applied to which parameters before being combined.

• frmt_when()accepts a series of equations separated by commas, similartodplyr::case_when(). The left hand side of the equation is a"TRUE"for thedefault case or the right hand side of a boolean expression">50". Theright hand side of the equation is thefrmt() to apply when the leftside evaluates toTRUE.

Value

frmt object

See Also

body_plan() combines the frmt_structures to be applied to thetable body, andfrmt_structure() defines which rows the formats will be appliedto.

Link to related article

Examples

frmt("XXX %")frmt("XX.XXX")frmt("xx.xx", scientific = "x10^xx")frmt_combine( "{param1} {param2}", param1 = frmt("XXX %"), param2 = frmt("XX.XXX"))frmt_when(  ">3" ~ frmt("(X.X%)"),  "<=3" ~ frmt("Undetectable")  )frmt_when(  "==100"~ frmt(""),  "==0"~ "",  "TRUE" ~ frmt("(XXX.X%)")  )

Format Structure Object

Description

Function needed to create a frmt_structure object, which is a building blockofbody_plan(). This specifies the rows the format will be applied to.

Usage

frmt_structure(group_val = ".default", label_val = ".default", ...)

Arguments

Value

frmt_structure object

Images

Here are some example outputs:

See Also

body_plan() combines the frmt_structures to be applied to thetable body, andfrmt(),frmt_combine(), andfrmt_when() define theformat semantics.

Link to related article

Examples

sample_structure <- frmt_structure(          group_val = c("group1"),          label_val = ".default",          frmt("XXX")        )## multiple group columnssample_structure <- frmt_structure(          group_val = list(grp_col1 = "group1", grp_col2 = "subgroup3"),          label_val = ".default",          frmt("XXX")        )

Check if input is a frmt

Description

Check if input is a frmt

Check if input is a frmt_combine

Check if input is a frmt_when

Check if input is a frmt_structure

Check if input is a row_grp_structure

Usage

is_frmt(x)is_frmt_combine(x)is_frmt_when(x)is_frmt_structure(x)is_row_grp_structure(x)

Arguments

Value

'TRUE' if yes, 'FALSE' if no

Examples

x1 <- frmt("XXX.XX")is_frmt(x1)x2 <- frmt_combine("XXX %","XX,XXX")is_frmt_combine(x2)x2 <- frmt_when(">3" ~ frmt("(X.X%)"),"<=3" ~ frmt("Undetectable"))is_frmt_when(x2)x3 <- frmt_structure( group_val = c("group1"), label_val = ".default",frmt("XXX"))is_frmt_structure(x3)x4 <- row_grp_structure(group_val = c("A","C"), element_block(post_space = "---"))is_row_grp_structure(x4)

json to tfrmt

Description

Reader to read JSON files/objects into tfrmt objects

Usage

json_to_tfrmt(path = NULL, json = NULL)

Arguments

Layer tfrmt objects together

Description

Provide utility for layering tfrmt objects together. If both tfrmt's havevalues, it will preferentially choose the second tfrmt by default. This is analternative to piping together tfrmt's

Usage

layer_tfrmt(x, y, ..., join_body_plans = TRUE)

Arguments

Details

join_body_plan

When combining two body_plans, the body plans will stack together, first thebody plan from x tfrmt then y tfrmt. This means that frmt_structures in ywill take priority over those in x.

Combining two tfrmt with large body_plans can lead to slow table evaluation.Consider settingjoin_body_plan toFALSE. Only the ybody_plan will bepreserved.

Value

tfrmt object

Examples

tfrmt_1 <- tfrmt(title = "title1")tfrmt_2 <- tfrmt(title = "title2",subtitle = "subtitle2")layered_table_format <- layer_tfrmt(tfrmt_1, tfrmt_2)

Make mock data for display shells

Description

Make mock data for display shells

Usage

make_mock_data(tfrmt, .default = 1:3, n_cols = NULL)

Arguments

Value

tibble containing mock data

Examples

tfrmt_spec <- tfrmt(  label = label,  column = column,  param = param,  value=value,  body_plan = body_plan(    frmt_structure(group_val=".default", label_val=".default", frmt("xx.x"))    )  )make_mock_data(tfrmt_spec)

Page Plan

Description

Defining the location and/or frequency of page splits with a series ofpage_structure's and the row_every_n argument, respectively.

Usage

page_plan(  ...,  note_loc = c("noprint", "preheader", "subtitle", "source_note"),  max_rows = NULL)

Arguments

Value

page_plan object

Examples

 # use of page_struct page_plan(    page_structure(group_val = "grp1", label_val = "lbl1") ) # use of #  rows page_plan(    max_rows = 5 )

Page structure

Description

Page structure

Usage

page_structure(group_val = NULL, label_val = NULL)

Arguments

Value

page structure object

Examples

# split page after every unique level of the grouping variable page_structure(group_val = ".default", label_val = NULL) # split page after specific levels page_structure(group_val = "grp1", label_val = "lbl3")

Set custom parameter-level significant digits rounding

Description

Set custom parameter-level significant digits rounding

Usage

param_set(...)

Arguments

Details

Typeparam_set() in console to view package defaults. Use of thefunction will add to the defaults and/or override included defaults of thesame name. For values that are integers, useNA so no decimal places willbe added.

Value

list of default parameter-level significant digits rounding

Examples

# View included defaultsparam_set()# Update the defaultsparam_set("{mean} ({sd})" = c(2,3), "pct" = 1)# Separate mean and SD to different linesparam_set("mean" = 2, "sd" = 3)# Add formatting using the glue syntaxparam_set("{pct} %" = 1)

PreparebigN stat variables

Description

prep_big_n():

• recodes the"n"stat_name intobigN for the desired variables,and

• drops all otherstat_names for the same variables.

If yourtfrmt contains abig_n_structure() you pass the tfrmtcolumn toprep_big_n() viavars.

Usage

prep_big_n(df, vars)

Arguments

Value

a data.frame with the same columns as the input. Thestat_namecolumn is modified.

Examples

df <- data.frame(  stat_name = c("n", "max", "min", rep(c("n", "N", "p"), times = 2)),  context = rep(c("continuous", "hierarchical", "categorical"), each = 3),  stat_variable = rep(c("a", "b", "c"), each = 3)) |>  dplyr::bind_rows(    data.frame(      stat_name = "n",      context = "total_n",      stat_variable = "d"    )  )prep_big_n(  df,  vars = c("b", "c"))

Combine variables

Description

A wrapper aroundtidyr::unite() which pastes several columns into one.In addition it checks the output is identical todplyr::coalesce(). If notidentical, the input data.frame is returned unchanged. Useful for unitingsparsely populated columns, for example when processing an ard that wascreated withcards::ard_stack() then shuffled with⁠[shuffle_card()]⁠.

If the data is the result of a hierarchical ard stack (withcards::ard_stack_hierarchical() orcards::ard_stack_hierarchical_count()), the input is returned unchanged.This is assessed from the information in thecontext column which needs tobe present. If the input data does not have acontext column, the inputwill be returned unmodified.

Usage

prep_combine_vars(df, vars, remove = TRUE)

Arguments

Value

a data.frame with an additional column, calledvariable_level orthe input unchanged.

Examples

df <- data.frame(  a = 1:6,  context = rep("categorical", 6),  b = c("a", rep(NA, 5)),  c = c(NA, "b", rep(NA, 4)),  d = c(NA, NA, "c", rep(NA, 3)),  e = c(NA, NA, NA, "d", rep(NA, 2)),  f = c(NA, NA, NA, NA, "e", NA),  g = c(rep(NA, 5), "f"))prep_combine_vars(  df,  vars = c("b", "c", "d", "e", "f", "g"))

Fill missing values in hierarchical variables

Description

ReplaceNA values in one column conditional on the same row having anon-NA value in a different column.

The user supplies a vector of columns from which the pairs will be extractedwith a rolling window. For examplevars <- c("A", "B", "C") will generate2 pairs⁠("A", "B")⁠ and⁠("B", "C")⁠. Therefore the order of the variablesmatters.

In each pair the second columnB will be filled ifA is not missing. Onecan choose the value to fill with:

• "Any {colname}", in this case evaluating to"Any B" is the default.

• Any other value. For example"Any event" for an adverse effects table.

• the value of pair's first column. In this case, the value ofA.

Usage

prep_hierarchical_fill(  df,  vars,  fill = "Any {colname}",  fill_from_left = FALSE)

Arguments

Value

a data.frame with the same columns as the input, but in which somethe desired columns have been filled pairwise.

Examples

df <- data.frame(  x = c(1, 2, NA),  y = c("a", NA, "b"),  z = rep(NA, 3))prep_hierarchical_fill(  df,  vars = c("x", "y"))prep_hierarchical_fill(  df,  vars = c("x", "y"),  fill = "foo")prep_hierarchical_fill(  df,  vars = c("x", "y", "z"),  fill_from_left = TRUE)

Prepare label

Description

Adds alabel column which is a combination ofstat_label (for continuousvariables) andvariable_level (for categorical ones) if these 2 columns arepresent in the input data frame.

Usage

prep_label(df)

Arguments

Value

a data.frame with alabel column (if the input has the requiredcolumns) or the input unchanged.

Examples

df <- data.frame(  variable_level = c("d", "e", "f"),  stat_label = c("a", "b", "c"),  stat_name = c("n", "N", "n"),  context = c("categorical", "continuous", "hierarchical"))prep_label(df)

Print mock table to GT

Description

Print mock table to GT

Usage

print_mock_gt(  tfrmt,  .data = NULL,  .default = 1:3,  n_cols = NULL,  .unicode_ws = TRUE)

Arguments

Value

a stylized gt object

Examples

  # Create tfrmt specification  tfrmt_spec <- tfrmt( label = label, column =  column, param = param, body_plan = body_plan( frmt_structure(group_val =  ".default", label_val = ".default", frmt_combine( "{count} {percent}",  count = frmt("xxx"), percent = frmt_when("==100"~ frmt(""), "==0"~ "",  "TRUE" ~ frmt("(xx.x%)")))) ))  # Print mock table using default  print_mock_gt(tfrmt = tfrmt_spec)

  # Create mock data  df <- crossing(label = c("label 1", "label 2",  "label 3"), column = c("placebo", "trt1", "trt2"), param = c("count",  "percent"))  # Print mock table using mock data  print_mock_gt(tfrmt_spec, df)

Print to ggplot

Description

Print to ggplot

Usage

print_to_ggplot(tfrmt, .data, ...)

Arguments

Value

a stylized ggplot object

Examples

# Create datarisk<-tibble(time=c(rep(c(0,1000,2000,3000),3)),            label=c(rep("Obs",4),rep("Lev",4),rep("Lev+5FU",4)),            value=c(630,372,256,11,620,360,266,8,608,425,328,14),            param=rep("n",12))table<-tfrmt( label = label , column = time, param = param, value = value) %>%  print_to_ggplot(risk)table

Print to gt

Description

Print to gt

Usage

print_to_gt(tfrmt, .data, .unicode_ws = TRUE)

Arguments

Value

a stylized gt object

Examples

library(dplyr)# Create tfrmt specificationtfrmt_spec <- tfrmt(  label = label,  column = column,  param = param,  value=value,  body_plan = body_plan(    frmt_structure(group_val = ".default", label_val = ".default",                   frmt_combine(                     "{count} {percent}",                     count = frmt("xxx"),                     percent = frmt_when("==100"~ frmt(""),                                         "==0"~ "",                                         "TRUE" ~ frmt("(xx.x%)"))))  ))# Create datadf <- crossing(label = c("label 1", "label 2"),               column = c("placebo", "trt1"),               param = c("count", "percent")) %>%      mutate(value=c(24,19,2400/48,1900/38,5,1,500/48,100/38))print_to_gt(tfrmt_spec,df)

Row Group Plan

Description

Define the look of the table groups on the output. This function allows you toadd spaces after blocks and allows you to control how the groups are viewedwhether they span the entire table or are nested as a column.

Usage

row_grp_plan(..., label_loc = element_row_grp_loc(location = "indented"))

Arguments

Value

row_grp_plan object

See Also

row_grp_structure() for more details on how to specify row groupstructures,element_block() for more details on how to specify spacingbetween each group,element_row_grp_loc() for more details on how tospecify whether row group titles span the entire table or collapse.

Link to related article

Examples

  ## single grouping variable example  sample_grp_plan <- row_grp_plan(    row_grp_structure(group_val = c("A","C"), element_block(post_space = "---")),    row_grp_structure(group_val = c("B"), element_block(post_space = " ")),    label_loc = element_row_grp_loc(location = "column")  )  ## example with multiple grouping variables  sample_grp_plan <- row_grp_plan(     row_grp_structure(group_val = list(grp1 = "A", grp2 = "b"), element_block(post_space = " ")),     label_loc = element_row_grp_loc(location = "spanning")     )

Row Group Structure Object

Description

Function needed to create a row_grp_structure object, which is a building blockofrow_grp_plan()

Usage

row_grp_structure(group_val = ".default", element_block)

Arguments

Value

row_grp_structure object

See Also

row_grp_plan() for more details on how to group row groupstructures,element_block() for more details on how to specify spacingbetween each group.

Link to related article

Examples

## single grouping variable examplerow_grp_structure(group_val = c("A","C"), element_block(post_space = "---"))## example with multiple grouping variablesrow_grp_structure(group_val = list(grp1 = "A", grp2 = "b"), element_block(post_space = " "))

Shufflecards

Description

This function ingests an ARD object of classcard and shuffles the information to preparefor analysis. Helpful for streamlining across multiple ARDs.

Usage

shuffle_card(  x,  by = NULL,  trim = TRUE,  order_rows = TRUE,  fill_overall = "Overall {colname}",  fill_hierarchical_overall = "Any {colname}")

Arguments

Value

a tibble

Examples

## Not run: cards::bind_ard(  cards::ard_categorical(cards::ADSL, by = "ARM", variables = "AGEGR1"),  cards::ard_categorical(cards::ADSL, variables = "ARM")) |>  shuffle_card()## End(Not run)

Table Format

Description

tfrmt, or "table format" is a way to pre-define the non-data components ofyour tables, and how the data will be handled once added: i.e. title,footers, headers, span headers, and cell formats. In addition, tfrmt's can belayered, building from one table format to the next. For cases where only onevalue can be used, the newly defined tfrmt accepts the latest tfrmt

Usage

tfrmt(  tfrmt_obj,  group = vars(),  label = quo(),  param = quo(),  value = quo(),  column = vars(),  title,  subtitle,  row_grp_plan,  body_plan,  col_style_plan,  col_plan,  sorting_cols,  big_n,  footnote_plan,  page_plan,  ...)

Arguments

Details

NSE and Argument Evaluation

• tfrmt allows users to passvars,quo, and unquoted expressions to avariety of arguments, such asgroup,label,param,value,column, andsorting_cols. Users accustomed to tidyverse semanticsshould be familiar with this behaviour. However, there is an importantbehaviour difference between tfrmt and normal tidyverse functions. Becausethe data are not a part of tfrmt, it does not know when a value beingpassed to it is intended to be an unquoted expression representing a columnname or an object from the environment. As such, it preferentially uses thevalue from the environment over preserving the entry as an expression. Forexample, if you have an object "my_object" in yourenvironment with the value "Hello world", and try to create a tfrmt astfrmt(column = my_object), it will take the value of "my_object" overassuming the column argument is an unquoted expression and view the entrytocolumn as "Hello World". To pass "my_object" to tfrmt as a column name, usequotes around the value:tfrmt(columnn = "my_object").

• Additionally, unquoted expressions that matchtfrmt's otherargument names can cause unexpected results. It is recommendedto put quotes around the value as such:tfrmt(label = "group"). In this case, the quoting will preventtfrmtfrom assigning itsgroup input value to thelabel value.

Value

tfrmt object

Images

Here are some example outputs:

See Also

Link to related article

Examples

tfrmt_spec <- tfrmt(  label = label,  column = column,  param = param,  value=value)tfrmt_spec <- tfrmt(  label = label,  column = column,  param = param,  value=value,# Set the formatting for values  body_plan = body_plan(    frmt_structure(      group_val = ".default",      label_val = ".default",      frmt_combine("{n} {pct}",           n = frmt("xxx"),           pct = frmt_when(                "==100" ~ "(100%)",                "==0" ~ "",                TRUE ~ frmt("(xx.x %)")                )           )    )  ),# Specify column styling plan  col_style_plan = col_style_plan(    col_style_structure(col = vars(everything()), align = c(".",","," "))  ))tfrmt_spec <- tfrmt(  group = group,  label = label,  column = column,  param = param,  value=value,  sorting_cols = c(ord1, ord2),  # specify value formatting  body_plan = body_plan(    frmt_structure(      group_val = ".default",      label_val = ".default",      frmt_combine("{n} {pct}",           n = frmt("xxx"),           pct = frmt_when(                "==100" ~ "(100%)",                "==0" ~ "",                TRUE ~ frmt("(xx.x %)")                )           )    ),    frmt_structure(        group_val = ".default",        label_val = "n",        frmt("xxx")   ),    frmt_structure(        group_val = ".default",        label_val = c("Mean", "Median", "Min","Max"),        frmt("xxx.x")     ),    frmt_structure(        group_val = ".default",        label_val = "SD",        frmt("xxx.xx")    ),    frmt_structure(        group_val = ".default",        label_val = ".default",        p = frmt("")    ),    frmt_structure(        group_val = ".default",        label_val = c("n","<65 yrs","<12 months","<25"),        p = frmt_when(            ">0.99" ~ ">0.99",            "<0.001" ~ "<0.001",            TRUE ~ frmt("x.xxx", missing = "")        )    )  ),  # remove extra cols  col_plan = col_plan(-grp,                      -starts_with("ord") ),  # Specify column styling plan  col_style_plan = col_style_plan(    col_style_structure(col = vars(everything()), align = c(".",","," "))  ),  # Specify row group plan  row_grp_plan = row_grp_plan(    row_grp_structure(         group_val = ".default",         element_block(post_space = " ")    ),    label_loc = element_row_grp_loc(location = "column")  ))

N Percent Template

Description

This function creates an tfrmt for an n % table, so count based table. Theparameter values for n and percent can be provided (by default it will assumen andpct). Additionally thefrmt_when for formatting the percent canbe specified. By default 100% and 0% will not appear and everything between99% and 100% and 0% and 1% will be rounded using greater than (>) and lessthan (<) signs respectively.

Usage

tfrmt_n_pct(  n = "n",  pct = "pct",  pct_frmt_when = frmt_when("==100" ~ frmt(""), ">99" ~ frmt("(>99%)"), "==0" ~ "", "<1"    ~ frmt("(<1%)"), "TRUE" ~ frmt("(xx.x%)")),  tfrmt_obj = NULL)

Arguments

Value

tfrmt object

Examples

print_mock_gt(tfrmt_n_pct())

Create tfrmt object from significant digits spec

Description

This function creates a tfrmt based on significant digits specifications forgroup/label values. The input data spec provided tosigdig_df will containgroup/label value specifications.tfrmt_sigdig assumes that these columnsare group columns unless otherwise specified. The user may optionally chooseto pass the names of the group and/or label columns as arguments to thefunction.

Usage

tfrmt_sigdig(  sigdig_df,  group = vars(),  label = quo(),  param_defaults = param_set(),  missing = NULL,  tfrmt_obj = NULL,  ...)

Arguments

Details

Formats covered

Currently covers specifications forfrmt andfrmt_combine.frmt_when not supported and must be supplied in additionaltfrmt that is layered on.

Group/label variables

If the group/label variables are not provided to the arguments, the body_planwill be constructed from the input data with the following behaviour:

• If no group or label are supplied, it will be assumed that all columns in the inputdata are group columns.

• If a label variable is provided, but nothing isspecified for group, any leftover columns (i.e. not matchingsigdig or thesupplied label variable name) in the input data will be assumed to be groupcolumns.

• If any group variable is provided, any leftover columns (i.e. notmatchingsigdig or the supplied group/label variable) will be disregarded.

Value

tfrmt object with abody_plan constructed based on thesignificant digits data spec and param-level significant digits defaults.

Examples

sig_input <- tibble::tribble(  ~group1,   ~group2, ~sigdig,  "CHEMISTRY",   ".default", 3,  "CHEMISTRY",   "ALBUMIN",  1,  "CHEMISTRY",   "CALCIUM",   1,  ".default",    ".default",  2)# Subset data for the exampledata <- dplyr::filter(data_labs, group2 == "BASOPHILS", col1 %in% c("Placebo", "Xanomeline Low Dose"))tfrmt_sigdig(sigdig_df = sig_input,             group = vars(group1, group2),             label = rowlbl,             param_defaults = param_set("[{n}]" = NA)) %>%  tfrmt(column = vars(col1, col2),        param = param,        value = value,        sorting_cols = vars(ord1, ord2, ord3),        col_plan = col_plan(-starts_with("ord"))) %>%  print_to_gt(.data = data)

Print to JSON

Description

Print to JSON

Usage

tfrmt_to_json(tfrmt, path = NULL)

Arguments

Value

JSON

Examples

tfrmt(  label = label,  column = column,  param = param,  value=value) %>%  tfrmt_to_json()

Remap group values in a tfrmt

Description

Remap group values in a tfrmt

Usage

update_group(tfrmt, ...)

Arguments

Value

Atfrmt with thegroup variables updated in all places

tfrmt object with updated groups#'

Examples

tfrmt_spec <- tfrmt(    group = c(group1, group2),    body_plan  = body_plan(      frmt_structure(         group_val = list(group2 = "value"),         label_val = ".default",         frmt("XXX")         ),     frmt_structure(         group_val = list(group1 = "value", group2 = "value"),         label_val = ".default",         frmt("XXX")       )    ))tfrmt_spec %>%  update_group(New_Group = group1)