fmt_tf: Format 'TRUE' and 'FALSE' values

In gt: Easily Create Presentation-Ready Display Tables

Format TRUE and FALSE values

Description

There can be times where logical values are useful in a gt table. You might want to express a 'yes' or 'no', a 'true' or 'false', or, perhaps use pairings of complementary symbols that make sense in a table. The fmt_tf() function has a set of tf_style presets that can be used to quickly map TRUE/FALSE values to strings (which are automatically translated according to a given locale value), or, symbols like up/down or left/right arrows and open/closed shapes.

While the presets are nice, you can provide your own mappings through the true_val and false_val arguments. With those you could provide text (perhaps a Unicode symbol?) or even a fontawesome icon by using fontawesome::fa("<icon name>"). The function will automatically handle alignment when auto_align = TRUE and try to give you the best look depending on the options chosen. For extra customization, you can also apply color to the individual TRUE, FALSE, and NA mappings. Just supply a vector of colors (up to a length of 3) to the colors argument.

Usage

fmt_tf(
  data,
  columns = everything(),
  rows = everything(),
  tf_style = "true-false",
  pattern = "{x}",
  true_val = NULL,
  false_val = NULL,
  na_val = NULL,
  colors = NULL,
  auto_align = TRUE,
  locale = NULL
)

Arguments

data	The gt table data object ⁠obj:<gt_tbl>⁠ // required This is the gt table object that is commonly created through use of the gt() function.
columns	Columns to target ⁠<column-targeting expression>⁠ // default: everything() Can either be a series of column names provided in c(), a vector of column indices, or a select helper function (e.g. starts_with(), ends_with(), contains(), matches(), num_range() and everything()).
rows	Rows to target ⁠<row-targeting expression>⁠ // default: everything() In conjunction with columns, we can specify which of their rows should undergo formatting. The default everything() results in all rows in columns being formatted. Alternatively, we can supply a vector of row captions within c(), a vector of row indices, or a select helper function (e.g. starts_with(), ends_with(), contains(), matches(), num_range(), and everything()). We can also use expressions to filter down to the rows we need (e.g., ⁠[colname_1] > 100 & [colname_2] < 50⁠).
tf_style	Predefined style for TRUE/FALSE formatting ⁠scalar<character>|scalar<numeric|integer>(1<=val<=10)⁠ // default: "true-false" The TRUE/FALSE mapping style to use. By default this is the short name "true-false" which corresponds to the words 'true' and 'false'. Two other tf_style values produce words: "yes-no" and "up-down". All three of these options for tf_style are locale-aware through the locale option, so, a "yes" value will instead be "ja" when locale = "de". Options 4 through to 10 involve pairs of symbols (e.g., "check-mark" displays a check mark for TRUE and an X symbol for FALSE).
pattern	Specification of the formatting pattern ⁠scalar<character>⁠ // default: "{x}" A formatting pattern that allows for decoration of the formatted value. The formatted value is represented by the {x} (which can be used multiple times, if needed) and all other characters will be interpreted as string literals.
true_val	Text to use for TRUE values ⁠scalar<character>⁠ // default: NULL (optional) While the choice of a tf_style will typically supply the true_val and false_val text, we could override this and supply text for any TRUE values. This doesn't need to be used in conjunction with false_val.
false_val	Text to use for FALSE values ⁠scalar<character>⁠ // default: NULL (optional) While the choice of a tf_style will typically supply the true_val and false_val text, we could override this and supply text for any FALSE values. This doesn't need to be used in conjunction with true_val.
na_val	Text to use for NA values ⁠scalar<character>⁠ // default: NULL (optional) None of the tf_style presets will replace any missing values encountered in the targeted cells. While we always have the option to use sub_missing() for NA replacement, we have the opportunity to do that here with the na_val option. This is useful because we also have the means to add color to the na_val text or symbol and doing that requires that a replacement value for NAs is specified here.
colors	Colors to use for the resulting strings or symbols ⁠vector<character>⁠ // default: NULL (optional) Providing a vector of color values to colors will progressively add color to the formatted result depending on the number of colors provided. With a single color, all formatted values will be in that color. Giving two colors results in TRUE values being the first color, and FALSE values receiving the second. With the three color option, the final color will be given to any NA values replaced through na_val.
auto_align	Automatic alignment of the formatted column ⁠scalar<logical>⁠ // default: TRUE The input values may have resulted in an alignment that is not as suitable once formatting has occurred. With auto_align = TRUE, the formatted values will be inspected and this may result in a favorable change in alignment. Typically, symbols will be center aligned whereas words will receive a left alignment (for words in LTR languages).
locale	Locale identifier ⁠scalar<character>⁠ // default: NULL (optional) An optional locale identifier that can be used for formatting values according the locale's rules. Examples include "en" for English (United States) and "fr" for French (France). We can call info_locales() for a useful reference for all of the locales that are supported. A locale ID can be also set in the initial gt() function call (where it would be used automatically by any function with a locale argument) but a locale value provided here will override that global locale.

Value

An object of class gt_tbl.

Compatibility of formatting function with data values

fmt_tf() is compatible with body cells that are of the "logical" (preferred) or "numeric" types. Any other types of body cells are ignored during formatting. This is to say that cells of incompatible data types may be targeted, but there will be no attempt to format them.

There is a special caveat when attempting to format numerical values: the values must either be exactly 1 (the analogue for TRUE) or exactly 0 (the analogue for FALSE). Any other numerical values will be disregarded and left as is. Because of these restrictions, it is recommended that only logical values undergo formatting.

Targeting cells with columns and rows

Targeting of values is done through columns and additionally by rows (if nothing is provided for rows then entire columns are selected). The columns argument allows us to target a subset of cells contained in the resolved columns. We say resolved because aside from declaring column names in c() (with bare column names or names in quotes) we can use tidyselect-style expressions. This can be as basic as supplying a select helper like starts_with(), or, providing a more complex incantation like

where(~ is.numeric(.x) && max(.x, na.rm = TRUE) > 1E6)

which targets numeric columns that have a maximum value greater than 1,000,000 (excluding any NAs from consideration).

By default all columns and rows are selected (with the everything() defaults). Cell values that are incompatible with a given formatting function will be skipped over, like character values and numeric ⁠fmt_*()⁠ functions. So it's safe to select all columns with a particular formatting function (only those values that can be formatted will be formatted), but, you may not want that. One strategy is to format the bulk of cell values with one formatting function and then constrain the columns for later passes with other types of formatting (the last formatting done to a cell is what you get in the final output).

Once the columns are targeted, we may also target the rows within those columns. This can be done in a variety of ways. If a stub is present, then we potentially have row identifiers. Those can be used much like column names in the columns-targeting scenario. We can use simpler tidyselect-style expressions (the select helpers should work well here) and we can use quoted row identifiers in c(). It's also possible to use row indices (e.g., c(3, 5, 6)) though these index values must correspond to the row numbers of the input data (the indices won't necessarily match those of rearranged rows if row groups are present). One more type of expression is possible, an expression that takes column values (can involve any of the available columns in the table) and returns a logical vector. This is nice if you want to base formatting on values in the column or another column, or, you'd like to use a more complex predicate expression.

Compatibility of arguments with the from_column() helper function

from_column() can be used with certain arguments of fmt_tf() to obtain varying parameter values from a specified column within the table. This means that each row could be formatted a little bit differently. These arguments provide support for from_column():

• tf_style

• pattern

• true_val

• false_val

• na_val

• locale

Please note that for each of the aforementioned arguments, a from_column() call needs to reference a column that has data of the correct type (this is different for each argument). Additional columns for parameter values can be generated with cols_add() (if not already present). Columns that contain parameter data can also be hidden from final display with cols_hide(). Finally, there is no limitation to how many arguments the from_column() helper is applied so long as the arguments belong to this closed set.

Formatting with the tf_style argument

We can supply a preset TRUE/FALSE style to the tf_style argument to handle the formatting of logical values. There are several such styles and the first three of them can handle localization to any supported locale (i.e., the pairs of words for each style will be translated to the language of the locale) value.

The following table provides a listing of all valid tf_style values and a description of their output values. The output from styles 4 to 10 are described in terms of the Unicode character names used for the TRUE and FALSE values.

	TF Style	Output (for TRUE and FALSE)
1	"true-false"	"true", "false" (locale-aware)
2	"yes-no"	"yes", "no" (locale-aware)
3	"up-down"	"up", "down" (locale-aware)
4	"check-mark"	⁠<Heavy Check Mark>⁠, ⁠<Heavy Ballot X>⁠
5	"circles"	⁠<Black Circle>⁠, ⁠<Heavy Circle>⁠
6	"squares"	⁠<Black Square>⁠, ⁠<White Square>⁠
7	"diamonds"	⁠<Black Diamond>⁠, ⁠<White Diamond>⁠
8	"arrows"	⁠<Upwards Arrow>⁠, ⁠<Downwards Arrow>⁠
9	"triangles"	⁠<Black Up-Pointing Triangle>⁠, ⁠<Black Down-Pointing Triangle>⁠
10	"triangles-lr"	⁠<Heavy Check Mark>⁠, ⁠<Heavy Ballot X>⁠

Adapting output to a specific locale

This formatting function can adapt outputs according to a provided locale value. Examples include "en" for English (United States) and "fr" for French (France). Note that a locale value provided here will override any global locale setting performed in gt()'s own locale argument (it is settable there as a value received by all other functions that have a locale argument). As a useful reference on which locales are supported, we can call info_locales() to view an info table.

Examples

Let's use a subset of the sp500 dataset to create a small gt table containing opening and closing price data for a week in 2013. We can add a logical column (dir) with cols_add(); the expression used determines whether the close value is greater than the open value. That new column is inserted between open and close. Then, we use fmt_tf() to generate up and down arrows in the dir column. We elect to use green upward arrows and red downward arrows (through the colors option). With a little numeric formatting and changes to the column labels, the table becomes more presentable.

sp500 |>
  dplyr::filter(date >= "2013-01-07" & date <= "2013-01-12") |>
  dplyr::arrange(date) |>
  dplyr::select(-c(adj_close, volume, high, low)) |>
  gt(rowname_col = "date") |>
  cols_add(dir = close > open, .after = open) |>
  fmt_tf(
    columns = dir,
    tf_style = "arrows",
    colors = c("green", "red")
  ) |>
  fmt_currency(columns = c(open, close)) |>
  cols_label(
    open = "Opening",
    close = "Closing",
    dir = ""
  )

The reactions dataset contains chemical kinetic information on a wide variety of atmospherically-relevant compounds. It might be interesting to get a summary (for a small subset of compounds) for which rate constants are available for the selected compounds. We first start by selecting the relevant rows and columns. Then we generate logical columns for each of the reaction types (i.e., if a value is NA then there's no measurement, so that's FALSE). Once the gt table has been created, we can use fmt_tf() to provide open and filled circles to indicate whether a particular reaction has been measured and presented in the literature.

reactions |>
  dplyr::filter(cmpd_type %in% c("carboxylic acid", "alkyne", "allene")) |>
  dplyr::select(cmpd_name, cmpd_type, ends_with("k298")) |>
  dplyr::mutate(across(ends_with("k298"), is.na)) |>
  gt(rowname_col = "cmpd_name", groupname_col = "cmpd_type") |>
  tab_spanner(
    label = "Has a measured rate constant",
    columns = ends_with("k298")
  ) |>
  tab_stub_indent(
    rows = everything(),
    indent = 2
  ) |>
  fmt_tf(
    columns = ends_with("k298"),
    tf_style = "circles"
  ) |>
  cols_label(
    OH_k298 = "OH",
    O3_k298 = "Ozone",
    NO3_k298 = "Nitrate",
    Cl_k298 = "Chlorine"
  ) |>
  cols_width(
    stub() ~ px(200),
    ends_with("k298") ~ px(80)
  ) |>
  opt_vertical_padding(scale = 0.35)

There are census-based population values in the towny dataset and quite a few small towns within it. Let's look at the ten smallest towns (according to the 2021 figures) and work out whether their populations have increased or declined since 1996. Also, let's determine which of these towns even have a website. After that data preparation, the data is made into a gt table and fmt_tf() can be used in the website and pop_dir columns (which both have TRUE/FALSE values). Each of these fmt_tf() calls will either produce "yes"/"no" or "up"/"down" strings (set via the tf_style option).

towny |>
  dplyr::arrange(population_2021) |>
  dplyr::mutate(website = !is.na(website))  |>
  dplyr::mutate(pop_dir = population_2021 > population_1996) |>
  dplyr::select(name, website, population_1996, population_2021, pop_dir) |>
  dplyr::slice_head(n = 10) |>
  gt(rowname_col = "name") |>
  tab_spanner(
    label = "Population",
    columns = starts_with("pop")
  ) |>
  tab_stubhead(label = "Town") |>
  fmt_tf(
    columns = website,
    tf_style = "yes-no",
    auto_align = FALSE
  ) |>
  fmt_tf(
    columns = pop_dir,
    tf_style = "up-down",
    pattern = "It's {x}."
  ) |>
  cols_label_with(
    columns = starts_with("population"),
    fn = function(x) sub("population_", "", x)
  ) |>
  cols_label(
    website = md("Has a  \n website?"),
    pop_dir = "Pop. direction?"
  ) |>
  opt_horizontal_padding(scale = 2)

If formatting to words instead of symbols (with the hyphenated tf_style keywords), the words themselves can be translated to different languages if providing a locale value. In this next example, we're manually creating a tibble with locale codes and their associated languages. The yes and up columns all receive TRUE whereas no and down will all be FALSE. With two calls of fmt_tf() for each of these pairings, we get the columns' namesake words. To have these words translated, the locale argument is pointed toward values in the code column by using from_column().

dplyr::tibble(
  code = c("de", "fr", "is", "tr", "ka", "lt", "ca", "bg", "lv"),
  lang = c(
    "German", "French", "Icelandic", "Turkish", "Georgian",
    "Lithuanian", "Catalan", "Bulgarian", "Latvian"
  ),
  yes = TRUE,
  no = FALSE,
  up = TRUE,
  down = FALSE
) |>
  gt(rowname_col = "lang") |>
  tab_header(title = "Common words in a few languages") |>
  fmt_tf(
    columns = c(yes, no),
    tf_style = "yes-no",
    locale = from_column("code")
  ) |>
  fmt_tf(
    columns = c(up, down),
    tf_style = "up-down",
    locale = from_column("code")
  ) |>
  cols_merge(
    columns = c(lang, code),
    pattern = "{1} ({2})"
  ) |>
  cols_width(
    stub() ~ px(150),
    everything() ~ px(80)
  )

Function ID

3-18

Function Introduced

v0.11.0

See Also

Other data formatting functions: data_color(), fmt(), fmt_auto(), fmt_bins(), fmt_bytes(), fmt_chem(), fmt_country(), fmt_currency(), fmt_date(), fmt_datetime(), fmt_duration(), fmt_email(), fmt_engineering(), fmt_flag(), fmt_fraction(), fmt_icon(), fmt_image(), fmt_index(), fmt_integer(), fmt_markdown(), fmt_number(), fmt_partsper(), fmt_passthrough(), fmt_percent(), fmt_roman(), fmt_scientific(), fmt_spelled_num(), fmt_time(), fmt_units(), fmt_url(), sub_large_vals(), sub_missing(), sub_small_vals(), sub_values(), sub_zero()

gt documentation built on Sept. 11, 2024, 5:15 p.m.