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#------------------------------------------------------------------------------#
#
# /$$
# | $$
# /$$$$$$ /$$$$$$
# /$$__ $$|_ $$_/
# | $$ \ $$ | $$
# | $$ | $$ | $$ /$$
# | $$$$$$$ | $$$$/
# \____ $$ \___/
# /$$ \ $$
# | $$$$$$/
# \______/
#
# This file is part of the 'rstudio/gt' project.
#
# Copyright (c) 2018-2026 gt authors
#
# For full copyright and license information, please look at
# https://gt.rstudio.com/LICENSE.html
#
#------------------------------------------------------------------------------#
# md() -------------------------------------------------------------------------
#' Interpret input text as Markdown-formatted text
#'
#' @description
#'
#' Markdown text can be used in certain places in a **gt** table, and this is
#' wherever new text is defined (e.g., footnotes, source notes, the table title,
#' etc.). Using Markdown is advantageous for styling text since it will be
#' rendered correctly to the output format of the **gt** table. There is
#' also the [html()] helper that allows you use HTML exclusively (for tables
#' expressly meant for HTML output) but `md()` allows for both; you get to use
#' Markdown plus any HTML fragments at the same time.
#'
#' @param text *Markdown text*
#'
#' `scalar<character>` // **required**
#'
#' The text that is understood to contain Markdown formatting.
#'
#' @return A character object of class `from_markdown`. It's tagged as being
#' Markdown text and it will undergo conversion to the desired output context.
#'
#' @section Examples:
#'
#' Use the [`exibble`] dataset to create a **gt** table. When adding a title
#' through [tab_header()], we'll use the `md()` helper to signify to **gt** that
#' we're using Markdown formatting.
#'
#' ```r
#' exibble |>
#' dplyr::select(currency, char) |>
#' gt() |>
#' tab_header(title = md("Using *Markdown*"))
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_md_1.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-1
#'
#' @section Function Introduced:
#' `v0.2.0.5` (March 31, 2020)
#'
#' @export
md <- function(text) {
# Apply the `from_markdown` class
class(text) <- "from_markdown"
text
}
# html() -----------------------------------------------------------------------
#' Interpret input text as HTML-formatted text
#'
#' @description
#'
#' For certain pieces of text (like in column labels or table headings) we may
#' want to express them as raw HTML. In fact, with HTML, anything goes so it can
#' be much more than just text. The `html()` function will guard the input HTML
#' against escaping, so, your HTML tags will come through as HTML when
#' rendered... to HTML.
#'
#' @param text *HTML text*
#'
#' `scalar<character>` // **required**
#'
#' The text that is understood to be HTML text, which is to be preserved in
#' the HTML output context.
#'
#' @param ... *Optional parameters for `htmltools::HTML()`*
#'
#' `<multiple expressions>` // (`optional`)
#'
#' The `htmltools::HTML()` function contains `...` and anything provided here
#' will be passed to that internal function call.
#'
#' @return A character object of class `html`. It's tagged as an HTML fragment
#' that is not to be sanitized.
#'
#' @section Examples:
#'
#' Use the [`exibble`] dataset to create a **gt** table. When adding a title
#' through [tab_header()], we'll use the `html()` helper to signify to **gt**
#' that we're using HTML formatting.
#'
#' ```r
#' exibble |>
#' dplyr::select(currency, char) |>
#' gt() |>
#' tab_header(title = html("<em>HTML</em>"))
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_html_1.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-2
#'
#' @section Function Introduced:
#' `v0.2.0.5` (March 31, 2020)
#'
#' @export
html <- function(text, ...) {
if (inherits(text, "shiny.tag.list")) {
text <- as.character(text)
}
htmltools::HTML(text, ...)
}
# latex() -----------------------------------------------------------------------
#' Interpret input text as LaTeX-formatted text
#'
#' @description
#'
#' For certain pieces of text (like in column labels or table headings) we may
#' want to express them as raw LaTeX. In fact, with LaTeX, so much more can be done for formatting.
#' The `latex()` function will guard the input LaTeX from being escaped.
#'
#' @param text *LaTeX text*
#'
#' `scalar<character>` // **required**
#'
#' The text that is understood to be LaTeX text, which is to be preserved in
#' the LaTeX output context.
#'
#'
#' @return A character object of class `latex`. It's tagged as an latex fragment
#' that is not to be sanitized.
#'
#' @section Examples:
#'
#' Use the [`exibble`] dataset to create a **gt** table. When adding a title
#' through [tab_header()], we'll use the `latex()` helper to signify to **gt**
#' that we're using LaTeX formatting.
#'
#' ```r
#' exibble |>
#' dplyr::select(currency, char) |>
#' gt() |>
#' tab_header(title = latex("\\emph{LaTeX}"))
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_latex_1.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-2
#'
#' @section Function Introduced:
#' `v1.0.1` (May 10, 2025)
#'
#' @export
latex <- function(text) {
# Apply the `from_latex` class
class(text) <- "from_latex"
text
}
# px() -------------------------------------------------------------------------
#' Helper for providing a numeric value as pixels value
#'
#' @description
#'
#' For certain parameters, a length value is required. Examples include the
#' setting of font sizes (e.g., in [cell_text()]) and thicknesses of lines
#' (e.g., in [cell_borders()]). Setting a length in pixels with `px()` allows
#' for an absolute definition of size as opposed to the analogous helper
#' function [pct()].
#'
#' @param x *Numeric length in pixels*
#'
#' `scalar<numeric|integer>` // **required**
#'
#' The numeric value to format as a string (e.g., `"12px"`) for some
#' [tab_options()] arguments that can take values as units of pixels (e.g.,
#' `table.font.size`).
#'
#' @return A character vector with a single value in pixel units.
#'
#' @section Examples:
#'
#' Use the [`exibble`] dataset to create a **gt** table. Inside of the
#' [cell_text()] call (which is itself inside of [tab_style()]), we'll use the
#' `px()` helper function to define the font size for the column labels in units
#' of pixels.
#'
#' ```r
#' exibble |>
#' gt() |>
#' tab_style(
#' style = cell_text(size = px(20)),
#' locations = cells_column_labels()
#' )
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_px_1.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-3
#'
#' @section Function Introduced:
#' `v0.2.0.5` (March 31, 2020)
#'
#' @export
px <- function(x) {
if (mode(x) != "numeric") {
cli::cli_abort("The supplied value must be numeric.")
}
paste0(x, "px")
}
# pct() ------------------------------------------------------------------------
#' Helper for providing a numeric value as percentage
#'
#' @description
#'
#' A percentage value acts as a length value that is relative to an initial
#' state. For instance an 80 percent value for something will size the target
#' to 80 percent the size of its 'previous' value. This type of sizing is
#' useful for sizing up or down a length value with an intuitive measure. This
#' helper function can be used for the setting of font sizes (e.g., in
#' [cell_text()]) and altering the thicknesses of lines (e.g., in
#' [cell_borders()]). Should a more exact definition of size be required, the
#' analogous helper function [pct()] will be more useful.
#'
#' @param x *Numeric value in percent*
#'
#' `scalar<numeric|integer>` // **required**
#'
#' The numeric value to format as a string percentage for some [tab_options()]
#' arguments that can take percentage values (e.g., `table.width`).
#'
#' @return A character vector with a single value in percentage units.
#'
#' @section Examples:
#'
#' Use the [`exibble`] dataset to create a **gt** table. Inside of the
#' [cell_text()] call (which is itself inside of [tab_style()]), we'll use the
#' `pct()` helper function to define the font size for the column labels as a
#' percentage value.
#'
#' ```r
#' exibble |>
#' gt() |>
#' tab_style(
#' style = cell_text(size = pct(75)),
#' locations = cells_column_labels()
#' )
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_pct_1.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-4
#'
#' @section Function Introduced:
#' `v0.2.0.5` (March 31, 2020)
#'
#' @export
pct <- function(x) {
if (mode(x) != "numeric") {
cli::cli_abort("The supplied value must be numeric.")
}
paste0(x, "%")
}
# from_column() ----------------------------------------------------------------
#' Reference a column of values for certain parameters
#'
#' @description
#'
#' It can be useful to obtain parameter values from a column in a
#' **gt** for functions that operate on the table body and stub cells. For
#' example, you might want to indent row labels in the stub. You could call
#' [tab_stub_indent()] and indent different rows to various indentation levels.
#' However, each level of indentation applied necessitates a new call of that
#' function. To make this better, we can use indentation values available in a
#' table column via the `from_column()` helper function. For the
#' [tab_stub_indent()] case, you'd invoke this helper at the `indent` argument
#' and specify the column that has the values.
#'
#' @param column *Column name*
#'
#' `scalar<character>` // **required**
#'
#' A single column name in quotation marks. Values will be extracted from this
#' column and provided to compatible arguments.
#'
#' @param na_value *Default replacement for `NA` values*
#'
#' `scalar<character|numeric|logical>` // *default:* `NULL` (`optional`)
#'
#' A single value to replace any `NA` values in the `column`. Take care to
#' provide a value that is of the same type as the `column` values to avoid
#' any undesirable coercion.
#'
#' @param fn *Function to apply*
#'
#' `function|formula` // *default:* `NULL` (`optional`)
#'
#' If a function is provided here, any values extracted from the table
#' `column` (except `NA` values) can be mutated.
#'
#' @return A list object of class `gt_column`.
#'
#' @section Functions that allow the use of the `from_column()` helper:
#'
#' Only certain functions (and furthermore a subset of arguments within each)
#' support the use of `from_column()` for accessing varying parameter values.
#' These functions are:
#'
#' - [tab_stub_indent()]
#' - [fmt_number()]
#' - [fmt_integer()]
#' - [fmt_scientific()]
#' - [fmt_engineering()]
#' - [fmt_percent()]
#' - [fmt_partsper()]
#' - [fmt_fraction()]
#' - [fmt_currency()]
#' - [fmt_roman()]
#' - [fmt_index()]
#' - [fmt_spelled_num()]
#' - [fmt_bytes()]
#' - [fmt_date()]
#' - [fmt_time()]
#' - [fmt_datetime()]
#' - [fmt_url()]
#' - [fmt_image()]
#' - [fmt_flag()]
#' - [fmt_markdown()]
#' - [fmt_passthrough()]
#'
#' Within help documents for each of these functions you'll find the
#' *Compatibility of arguments with the `from_column()` helper function* section
#' and sections like these describe which arguments support the use of
#' `from_column()`.
#'
#' @section Examples:
#'
#' `from_column()` can be used in a variety of formatting functions so that
#' values for common options don't have to be static, they can change in every
#' row (so long as you have a column of compatible option values). Here's an
#' example where we have a table of repeating numeric values along with a column
#' of currency codes. We can format the numbers to currencies with
#' [fmt_currency()] and use `from_column()` to reference the column of currency
#' codes, giving us values that are each formatted as having a different
#' currency.
#'
#' ```r
#' dplyr::tibble(
#' amount = rep(30.75, 6),
#' curr = c("USD", "EUR", "GBP", "CAD", "AUD", "JPY"),
#' ) |>
#' gt() |>
#' fmt_currency(currency = from_column(column = "curr"))
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_from_column_1.png")`
#' }}
#'
#' Let's summarize the [`gtcars`] dataset to get a set of rankings of car
#' manufacturer by country of origin. The `n` column represents the number of
#' cars a manufacturer has within this dataset and we can use that column as a
#' way to size the text. We do that in the [tab_style()] call; the
#' `from_column()` function is used within the [cell_text()] statement to
#' fashion different font sizes from that `n` column. This is done in
#' conjunction with the `fn` argument of `from_column()`, which helps to tweak
#' the values in `n` to get a useful range of font sizes.
#'
#' ```r
#' gtcars |>
#' dplyr::count(mfr, ctry_origin) |>
#' dplyr::arrange(ctry_origin) |>
#' gt(groupname_col = "ctry_origin") |>
#' tab_style(
#' style = cell_text(
#' size = from_column(
#' column = "n",
#' fn = function(x) paste0(5 + (x * 3), "px")
#' )
#' ),
#' locations = cells_body()
#' ) |>
#' tab_style(
#' style = cell_text(align = "center"),
#' locations = cells_row_groups()
#' ) |>
#' cols_hide(columns = n) |>
#' tab_options(column_labels.hidden = TRUE) |>
#' opt_all_caps() |>
#' opt_vertical_padding(scale = 0.25) |>
#' cols_align(align = "center", columns = mfr)
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_from_column_2.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-5
#'
#' @section Function Introduced:
#' `v0.10.0` (October 7, 2023)
#'
#' @export
from_column <- function(
column,
na_value = NULL,
fn = NULL
) {
column_list <-
list(
column = column,
na_value = na_value[1],
fn = fn
)
# Set the `gt_currency` class
class(column_list) <- "gt_column"
column_list
}
# currency() -------------------------------------------------------------------
#' Supply a custom currency symbol to `fmt_currency()`
#'
#' @description
#'
#' The `currency()` helper function makes it easy to specify a context-aware
#' currency symbol to `currency` argument of [fmt_currency()]. Since **gt** can
#' render tables to several output formats, `currency()` allows for different
#' variations of the custom symbol based on the output context (which are
#' `html`, `latex`, `rtf`, and `default`). The number of decimal places for
#' the custom currency defaults to `2`, however, a value set for the `decimals`
#' argument of [fmt_currency()] will take precedence.
#'
#' @details
#'
#' We can use any combination of `html`, `latex`, `rtf`, and `default` as named
#' arguments for the currency text in each of the namesake contexts. The
#' `default` value is used as a fallback when there doesn't exist a dedicated
#' currency text value for a particular output context (e.g., when a table is
#' rendered as HTML and we use `currency(latex = "LTC", default = "ltc")`, the
#' currency symbol will be `"ltc"`. For convenience, if we provide only a single
#' string without a name, it will be taken as the `default` (i.e.,
#' `currency("ltc")` is equivalent to `currency(default = "ltc")`). However, if
#' we were to specify currency strings for multiple output contexts, names are
#' required each and every context.
#'
#' @param ... *Currency symbols by output context*
#'
#' `<named arguments>` // **required** (or, use `.list`)
#'
#' One or more named arguments using output contexts as the names and
#' currency symbol text as the values.
#'
#' @param .list *Alternative to `...`*
#'
#' `<list of multiple expressions>` // **required** (or, use `...`)
#'
#' Allows for the use of a list as an input alternative to `...`.
#'
#' @return A list object of class `gt_currency`.
#'
#' @section Examples:
#'
#' Use the [`exibble`] dataset to create a **gt** table. Within the
#' [fmt_currency()] call, we'll format the `currency` column to have currency
#' values in guilder (a defunct Dutch currency). We can register this custom
#' currency with the `currency()` helper function, supplying the `"ƒ"` HTML
#' entity for `html` outputs and using `"f"` for any other type of **gt**
#' output.
#'
#' ```r
#' exibble |>
#' gt() |>
#' fmt_currency(
#' columns = currency,
#' currency = currency(
#' html = "ƒ",
#' default = "f"
#' ),
#' decimals = 2
#' )
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_currency_1.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-6
#'
#' @section Function Introduced:
#' `v0.2.0.5` (March 31, 2020)
#'
#' @export
currency <- function(
...,
.list = list2(...)
) {
# Collect a named list of currencies
currency_list <- .list
# Stop function if the currency list contains no values
if (length(currency_list) == 0) {
cli::cli_abort(
"The `currency()` function must be provided with currency symbols."
)
}
# If only a single string is provided, upgrade the `currency_list`
# to have that string be the `default` value
if (length(currency_list) == 1 && !rlang::is_named(currency_list)) {
currency_list <- list(default = currency_list[[1]])
}
# Stop function if `currency_list` isn't entirely named
if (!rlang::is_named(currency_list)) {
cli::cli_abort("Names must be provided for all output contexts.")
}
# Stop function if all names are not part of the supported contexts
validate_contexts(contexts = names(currency_list))
# Stop function if there are duplicated names
if (!rlang::is_dictionaryish(currency_list)) {
cli::cli_abort("There cannot be any duplicate names for output contexts.")
}
# Set the `gt_currency` class
class(currency_list) <- "gt_currency"
currency_list
}
# unit_conversion() ------------------------------------------------------------
#' Get a conversion factor across two measurement units of a given class
#'
#' @description
#'
#' The `unit_conversion()` helper function gives us a conversion factor for
#' transforming a value from one form of measurement units to a target form.
#' For example if you have a length value that is expressed in miles you could
#' transform that value to one in kilometers through multiplication of the value
#' by the conversion factor (in this case `1.60934`).
#'
#' For `unit_conversion()` to understand the source and destination units, you
#' need to provide a keyword value for the `from` and `to` arguments. To aid as
#' a reference for this, call [info_unit_conversions()] to display an
#' information table that contains all of the keywords for every conversion
#' type.
#'
#' @param from *Units for the input value*
#'
#' `scalar<character>` // **required**
#'
#' The keyword representing the units for the value that requires unit
#' conversion. In the case where the value has units of miles, the necessary
#' input is `"length.mile"`.
#'
#' @param to *Desired units for the value*
#'
#' `scalar<character>` // **required**
#'
#' The keyword representing the target units for the value with units defined
#' in `from`. In the case where input value has units of miles and we would
#' rather want the value to be expressed as kilometers, the `to` value should
#' be `"length.kilometer"`.
#'
#' @return A single numerical value.
#'
#' @section Examples:
#'
#' Let's use a portion of the [`towny`] dataset and create a table showing
#' population, density, and land area for 10 municipalities. The `land_area_km2`
#' values are in units of square kilometers, however, we'd rather the values
#' were in square miles. We can convert the numeric values while formatting the
#' values with [`fmt_number()`] by using `unit_conversion()` in the `scale_by`
#' argument since the return value of that is a conversion factor (which is
#' applied to each value by multiplication). The same is done for converting the
#' 'people per square kilometer' values in `density_2021` to 'people per square
#' mile', however, the units to convert are in the denominator so the inverse
#' of the conversion factor must be used.
#'
#' ```r
#' towny |>
#' dplyr::slice_max(density_2021, n = 10) |>
#' dplyr::select(name, population_2021, density_2021, land_area_km2) |>
#' gt(rowname_col = "name") |>
#' fmt_integer(columns = population_2021) |>
#' fmt_number(
#' columns = land_area_km2,
#' decimals = 1,
#' scale_by = unit_conversion(
#' from = "area.square-kilometer",
#' to = "area.square-mile"
#' )
#' ) |>
#' fmt_number(
#' columns = density_2021,
#' decimals = 1,
#' scale_by = 1 / unit_conversion(
#' from = "area.square-kilometer",
#' to = "area.square-mile"
#' )
#' ) |>
#' cols_label(
#' land_area_km2 = "Land Area,<br>sq. mi",
#' population_2021 = "Population",
#' density_2021 = "Density,<br>ppl / sq. mi",
#' .fn = md
#' )
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_unit_conversion_1.png")`
#' }}
#'
#' With a small slice of the [`gibraltar`] dataset, let's display the
#' temperature values in terms of degrees Celsius (present in the data) *and* as
#' temperatures in degrees Fahrenheit (achievable via conversion). We can
#' duplicate the `temp` column through [cols_add()] (naming the new column as
#' `temp_f`) and when formatting through [fmt_integer()] we can call
#' `unit_conversion()` within the `scale_by` argument to perform this
#' transformation while formatting the values as integers.
#'
#' ```r
#' gibraltar |>
#' dplyr::filter(
#' date == "2023-05-15",
#' time >= "06:00",
#' time <= "12:00"
#' ) |>
#' dplyr::select(time, temp) |>
#' gt() |>
#' tab_header(
#' title = "Air Temperature During Late Morning Hours at LXGB Stn.",
#' subtitle = "May 15, 2023"
#' ) |>
#' cols_add(temp_f = temp) |>
#' cols_move(columns = temp_f, after = temp) |>
#' tab_spanner(
#' label = "Temperature",
#' columns = starts_with("temp")
#' ) |>
#' fmt_number(
#' columns = temp,
#' decimals = 1
#' ) |>
#' fmt_integer(
#' columns = temp_f,
#' scale_by = unit_conversion(
#' from = "temperature.C",
#' to = "temperature.F"
#' )
#' ) |>
#' cols_label(
#' time = "Time",
#' temp = "{{degC}}",
#' temp_f = "{{degF}}"
#' ) |>
#' cols_width(
#' starts_with("temp") ~ px(80),
#' time ~ px(100)
#' ) |>
#' opt_horizontal_padding(scale = 3) |>
#' opt_vertical_padding(scale = 0.5) |>
#' opt_align_table_header(align = "left") |>
#' tab_options(heading.title.font.size = px(16))
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_unit_conversion_2.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-7
#'
#' @section Function Introduced:
#' `v0.11.0` (July 9, 2024)
#'
#' @export
unit_conversion <- function(from, to) {
force(from)
force(to)
if (from %in% temperature_keywords() && to %in% temperature_keywords()) {
from <- normalize_temp_keyword(from)
to <- normalize_temp_keyword(to)
return(temperature_conversions(from = from, to = to))
}
if (!(from %in% conversion_factors[["from"]])) {
cli::cli_abort("The unit supplied in {.arg from} is not known.")
}
if (!(to %in% conversion_factors[["to"]])) {
cli::cli_abort("The unit supplied in {.arg to} is not known.")
}
if (from == to) {
return(1.0)
}
row_conversion <-
vctrs::vec_slice(
conversion_factors,
conversion_factors$from == from &
conversion_factors$to == to
)
# In the case where units are valid and available in the internal dataset,
# they may be across categories; such pairings do not allow for a conversion
# to take place
if (nrow(row_conversion) == 0L) {
cli::cli_abort("The conversion specified cannot be performed.")
}
row_conversion[["conv_factor"]]
}
temperature_keywords <- function() {
c(
"temperature.celsius",
"temp.celsius",
"celsius",
"temperature.C",
"temp.C",
"C",
"temperature.fahrenheit",
"temp.fahrenheit",
"fahrenheit",
"temperature.F",
"temp.F",
"F",
"temperature.kelvin",
"temp.kelvin",
"kelvin",
"temperature.K",
"temp.K",
"K",
"temperature.rankine",
"temp.rankine",
"rankine",
"temperature.R",
"temp.R",
"R"
)
}
normalize_temp_keyword <- function(keyword) {
switch(
keyword,
temperature.celsius = ,
temp.celsius = ,
celsius = ,
temperature.C = ,
temp.C = ,
C = "C",
temperature.fahrenheit = ,
temp.fahrenheit = ,
fahrenheit = ,
temperature.F = ,
temp.F = ,
`F` = "F",
temperature.kelvin = ,
temp.kelvin = ,
kelvin = ,
temperature.K = ,
temp.K = ,
K = "K",
temperature.rankine = ,
temp.rankine = ,
rankine = ,
temperature.R = ,
temp.R = ,
R = "R"
)
}
temperature_conversions <- function(from, to) {
from_to <- paste0(from, to)
switch(
from_to,
"CF" = function(x) (1.8 * x) + 32,
"CK" = function(x) x + 273.15,
"CR" = function(x) (1.8 * x) + 491.67,
"FC" = function(x) (x - 32) * 5/9,
"FK" = function(x) (x + 459.67) / 1.8,
"FR" = function(x) x + 459.67,
"KC" = function(x) x - 273.15,
"KF" = function(x) ((x - 273.15) * 1.8) + 32,
"KR" = function(x) x * 1.8,
"RC" = function(x) (x - 32 - 459.67) / 1.8,
"RF" = function(x) x - 459.67,
"RK" = function(x) x / 1.8,
"CC" = ,
"FF" = ,
"KK" = ,
"RR" = 1
)
}
# adjust_luminance() -----------------------------------------------------------
#' Adjust the luminance for a palette of colors
#'
#' @description
#'
#' The `adjust_luminance()` function can brighten or darken a palette of colors
#' by an arbitrary number of steps, which is defined by a real number between
#' -2.0 and 2.0. The transformation of a palette by a fixed step in this
#' function will tend to apply greater darkening or lightening for those colors
#' in the midrange compared to any very dark or very light colors in the input
#' palette.
#'
#' @details
#'
#' This function can be useful when combined with the [data_color()] function's
#' `palette` argument, which can use a vector of colors or any of the `col_*`
#' functions from the **scales** package (all of which have a `palette`
#' argument).
#'
#' @param colors *Color vector*
#'
#' `vector<character>` // **required**
#'
#' This is the vector of colors that will undergo an adjustment in luminance.
#' Each color value provided must either be a color name (in the set of colors
#' provided by `grDevices::colors()`) or a hexadecimal string in the form of
#' "#RRGGBB" or "#RRGGBBAA".
#'
#' @param steps *Adjustment level*
#'
#' `scalar<numeric|integer>(-2>=val>=2)` // **required**
#'
#' A positive or negative factor by which the luminance of colors in the
#' `colors` vector will be adjusted. Must be a number between `-2.0` and
#' `2.0`.
#'
#' @return A vector of color values.
#'
#' @section Examples:
#'
#' Get a palette of 8 pastel colors from the **RColorBrewer** package.
#'
#' ```r
#' pal <- RColorBrewer::brewer.pal(8, "Pastel2")
#' ```
#'
#' Create lighter and darker variants of the base palette (one step lower, one
#' step higher).
#'
#' ```r
#' pal_darker <- pal |> adjust_luminance(-1.0)
#' pal_lighter <- pal |> adjust_luminance(+1.0)
#' ```
#'
#' Create a tibble and make a **gt** table from it. Color each column in order
#' of increasingly darker palettes (with [data_color()]).
#'
#' ```r
#' dplyr::tibble(a = 1:8, b = 1:8, c = 1:8) |>
#' gt() |>
#' data_color(
#' columns = a,
#' colors = scales::col_numeric(
#' palette = pal_lighter,
#' domain = c(1, 8)
#' )
#' ) |>
#' data_color(
#' columns = b,
#' colors = scales::col_numeric(
#' palette = pal,
#' domain = c(1, 8)
#' )
#' ) |>
#' data_color(
#' columns = c,
#' colors = scales::col_numeric(
#' palette = pal_darker,
#' domain = c(1, 8)
#' )
#' )
#' ```
#'
#' \if{html}{\out{
#' `r man_get_image_tag(file = "man_adjust_luminance_1.png")`
#' }}
#'
#' @family helper functions
#' @section Function ID:
#' 8-9
#'
#' @section Function Introduced:
#' `v0.2.0.5` (March 31, 2020)
#'
#' @export
adjust_luminance <- function(
colors,
steps
) {
# Stop if steps is beyond an acceptable range
check_number_decimal(steps, min = -2, max = 2)
# Get a matrix of values in the RGB color space
rgb_matrix <- t(grDevices::col2rgb(colors, alpha = TRUE)) / 255
# Obtain the alpha values
alpha <- rgb_matrix[, "alpha"]
# Get a matrix of values in the Luv color space
luv_matrix <- grDevices::convertColor(rgb_matrix[, 1:3], "sRGB", "Luv")
# Apply calculations to obtain values in the HCL color space
h <- atan2(luv_matrix[, "v"], luv_matrix[, "u"]) * 180 / pi
c <- sqrt(luv_matrix[, "u"]^2 + luv_matrix[, "v"]^2)
l <- luv_matrix[, "L"]
# Scale luminance to occupy [0, 1]
y <- l / 100.
# Obtain `x` positions of luminance values along a sigmoid function
x <- log(-(y / (y - 1)))
# Calculate new luminance values based on a fixed step-change in `x`
y_2 <- 1 / (1 + exp(-(x + steps)))
# Rescale the new luminance values to [0, 100]
l <- y_2 * 100.
# Obtain hexadecimal colors from the modified HCL color values
grDevices::hcl(h, c, l, alpha = alpha)
}
#' Select helper for targeting the stub column
#'
#' @description
#'
#' Should you need to target only the stub column for formatting or other
#' operations, the `stub()` select helper can be used. This obviates the need
#' to use the name of the column that was selected as the stub column.