With numeric values in a gt table, we can perform number-based formatting so that the targeted values are rendered with a higher consideration for tabular presentation. Furthermore, there is finer control over numeric formatting with the following options:

  • decimals: choice of the number of decimal places, option to drop trailing zeros, and a choice of the decimal symbol

  • digit grouping separators: options to enable/disable digit separators and provide a choice of separator symbol

  • scaling: we can choose to scale targeted values by a multiplier value

  • large-number suffixing: larger figures (thousands, millions, etc.) can be autoscaled and decorated with the appropriate suffixes

  • pattern: option to use a text pattern for decoration of the formatted values

  • locale-based formatting: providing a locale ID will result in number formatting specific to the chosen locale

fmt_number(
  data,
  columns,
  rows = NULL,
  decimals = 2,
  n_sigfig = NULL,
  drop_trailing_zeros = FALSE,
  drop_trailing_dec_mark = TRUE,
  use_seps = TRUE,
  scale_by = 1,
  suffixing = FALSE,
  pattern = "{x}",
  sep_mark = ",",
  dec_mark = ".",
  locale = NULL
)

Arguments

data

A table object that is created using the gt() function.

columns

The columns to format. Can either be a series of column names provided in vars(), a vector of column indices, or a helper function focused on selections. The select helper functions are: starts_with(), ends_with(), contains(), matches(), one_of(), and everything().

rows

Optional rows to format. Not providing any value results in all rows in columns being formatted. Can either be a vector of row captions provided c(), a vector of row indices, or a helper function focused on selections. The select helper functions are: starts_with(), ends_with(), contains(), matches(), one_of(), and everything(). We can also use expressions to filter down to the rows we need (e.g., [colname_1] > 100 & [colname_2] < 50).

decimals

An option to specify the exact number of decimal places to use. The default number of decimal places is 2.

n_sigfig

A option to format numbers to n significant figures. By default, this is NULL and thus number values will be formatted according to the number of decimal places set via decimals. If opting to format according to the rules of significant figures, n_sigfig must be a number greater than or equal to 1. Any values passed to the decimals and drop_trailing_zeros arguments will be ignored.

drop_trailing_zeros

A logical value that allows for removal of trailing zeros (those redundant zeros after the decimal mark).

drop_trailing_dec_mark

A logical value that determines whether decimal marks should always appear even if there are no decimal digits to display after formatting (e.g, 23 becomes 23.). The default for this is TRUE, which means that trailing decimal marks are not shown.

use_seps

An option to use digit group separators. The type of digit group separator is set by sep_mark and overridden if a locale ID is provided to locale. This setting is TRUE by default.

scale_by

A value to scale the input. The default is 1.0. All numeric values will be multiplied by this value first before undergoing formatting. This value will be ignored if using any of the suffixing options (i.e., where suffixing is not set to FALSE).

suffixing

An option to scale and apply suffixes to larger numbers (e.g., 1924000 can be transformed to 1.92M). This option can accept a logical value, where FALSE (the default) will not perform this transformation and TRUE will apply thousands (K), millions (M), billions (B), and trillions (T) suffixes after automatic value scaling. We can also specify which symbols to use for each of the value ranges by using a character vector of the preferred symbols to replace the defaults (e.g., c("k", "Ml", "Bn", "Tr")).

Including NA values in the vector will ensure that the particular range will either not be included in the transformation (e.g, c(NA, "M", "B", "T") won't modify numbers in the thousands range) or the range will inherit a previous suffix (e.g., with c("K", "M", NA, "T"), all numbers in the range of millions and billions will be in terms of millions).

Any use of suffixing (where it is not set expressly as FALSE) means that any value provided to scale_by will be ignored.

pattern

A formatting pattern that allows for decoration of the formatted value. The value itself is represented by {x} and all other characters are taken to be string literals.

sep_mark

The mark to use as a separator between groups of digits (e.g., using sep_mark = "," with 1000 would result in a formatted value of 1,000).

dec_mark

The character to use as a decimal mark (e.g., using dec_mark = "," with 0.152 would result in a formatted value of 0,152).

locale

An optional locale ID that can be used for formatting the value according the locale's rules. Examples include "en_US" for English (United States) and "fr_FR" for French (France). The use of a valid locale ID will override any values provided in sep_mark and dec_mark. We can use the info_locales() function as a useful reference for all of the locales that are supported.

Value

An object of class gt_tbl.

Details

Targeting of values is done through columns and additionally by rows (if nothing is provided for rows then entire columns are selected). A number of helper functions exist to make targeting more effective. Conditional formatting is possible by providing a conditional expression to the rows argument. See the Arguments section for more information on this.

Figures

Function ID

3-1

See also

Examples

library(tidyr) # Use `exibble` to create a gt table; # format the `num` column as numeric # with three decimal places and with no # use of digit separators tab_1 <- exibble %>% gt() %>% fmt_number( columns = vars(num), decimals = 3, use_seps = FALSE ) # Use `countrypops` to create a gt # table; format all numeric columns # to use large-number suffixing tab_2 <- countrypops %>% dplyr::select(country_code_3, year, population) %>% dplyr::filter( country_code_3 %in% c( "CHN", "IND", "USA", "PAK", "IDN") ) %>% dplyr::filter(year > 1975 & year %% 5 == 0) %>% tidyr::spread(year, population) %>% dplyr::arrange(desc(`2015`)) %>% gt(rowname_col = "country_code_3") %>% fmt_number( columns = 2:9, decimals = 2, suffixing = TRUE )