| fmt_number {gt} | R Documentation |
Format numeric values
Description
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
Usage
fmt_number(
data,
columns = everything(),
rows = everything(),
decimals = 2,
n_sigfig = NULL,
drop_trailing_zeros = FALSE,
drop_trailing_dec_mark = TRUE,
use_seps = TRUE,
accounting = FALSE,
scale_by = 1,
suffixing = FALSE,
pattern = "{x}",
sep_mark = ",",
dec_mark = ".",
force_sign = FALSE,
system = c("intl", "ind"),
locale = NULL
)
Arguments
data |
The gt table data object
This is the gt table object that is commonly created through use of the
|
columns |
Columns to target
Can either be a series of column names provided in |
rows |
Rows to target
In conjunction with |
decimals |
Number of decimal places
This corresponds to the exact number of decimal places to use. A value
such as |
n_sigfig |
Number of significant figures
A option to format numbers to n significant figures. By default, this is
|
drop_trailing_zeros |
Drop any trailing zeros
A logical value that allows for removal of trailing zeros (those redundant zeros after the decimal mark). |
drop_trailing_dec_mark |
Drop the trailing decimal 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., |
use_seps |
Use digit group separators
An option to use digit group separators. The type of digit group separator
is set by |
accounting |
Use accounting style
An option to use accounting style for values. Normally, negative values will be shown with a minus sign but using accounting style will instead put any negative values in parentheses. |
scale_by |
Scale values by a fixed multiplier
All numeric values will be multiplied by the |
suffixing |
Specification for large-number suffixing
The We can alternatively provide a character vector that serves as a
specification for which symbols are to used for each of the value ranges.
These preferred symbols will replace the defaults (e.g.,
Including Any use of If using |
pattern |
Specification of the formatting pattern
A formatting pattern that allows for decoration of the formatted value. The
formatted value is represented by the |
sep_mark |
Separator mark for digit grouping
The string to use as a separator between groups of digits. For example,
using |
dec_mark |
Decimal mark
The string to be used as the decimal mark. For example, using
|
force_sign |
Forcing the display of a positive sign
Should the positive sign be shown for positive values (effectively showing
a sign for all values except zero)? If so, use |
system |
Numbering system for grouping separators
The international numbering system (keyword: |
locale |
Locale identifier
An optional locale identifier that can be used for formatting values
according the locale's rules. Examples include |
Value
An object of class gt_tbl.
Compatibility of formatting function with data values
fmt_number() is compatible with body cells that are of the "numeric" or
"integer" 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.
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_number() 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():
-
decimals -
n_sigfig -
drop_trailing_zeros -
drop_trailing_dec_mark -
use_seps -
accounting -
scale_by -
suffixing -
pattern -
sep_mark -
dec_mark -
force_sign -
system -
locale
Please note that for all 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.
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). The use of a valid locale ID here means separator and
decimal marks will be correct for the given locale. Should any values be
provided in sep_mark or dec_mark, they will be overridden by the locale's
preferred values.
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 the exibble dataset to create a gt table. With
fmt_number(), we'll format the num column to have three decimal
places (with decimals = 3) and omit the use of digit separators (with
use_seps = FALSE).
exibble |>
gt() |>
fmt_number(
columns = num,
decimals = 3,
use_seps = FALSE
)
Use a modified version of the countrypops dataset to create a gt
table with row labels. Format all columns to use large-number suffixing
(e.g., where "10,000,000" becomes "10M") with the suffixing = TRUE
option.
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(suffixing = TRUE)
In a variation of the previous table, we can combine large-number suffixing
with a declaration of the number of significant digits to use. With things
like population figures, n_sigfig = 3 is a very good option.
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(suffixing = TRUE, n_sigfig = 3)
There can be cases where you want to show numbers to a large number of
decimal places but also drop the unnecessary trailing zeros for low-precision
values. Let's take a portion of the towny dataset and format the
latitude and longitude columns with fmt_number(). We'll have up to 5
digits displayed as decimal values, but we'll also unconditionally drop any
runs of trailing zeros in the decimal part with drop_trailing_zeros = TRUE.
towny |>
dplyr::select(name, latitude, longitude) |>
dplyr::slice_head(n = 10) |>
gt() |>
fmt_number(decimals = 5, drop_trailing_zeros = TRUE) |>
cols_merge(columns = -name, pattern = "{1}, {2}") |>
cols_label(
name ~ "Municipality",
latitude = "Location"
)
Another strategy for dealing with precision of decimals is to have a separate
column of values that specify how many decimal digits to retain. Such a
column can be added via cols_add() or it can be part of the input table for
gt(). With that column available, it can be referenced in the decimals
argument with from_column(). This approach yields a display of coordinate
values that reflects the measurement precision of each value.
towny |>
dplyr::select(name, latitude, longitude) |>
dplyr::slice_head(n = 10) |>
gt() |>
cols_add(dec_digits = c(1, 2, 2, 5, 5, 2, 3, 2, 3, 3)) |>
fmt_number(decimals = from_column(column = "dec_digits")) |>
cols_merge(columns = -name, pattern = "{1}, {2}") |>
cols_label(
name ~ "Municipality",
latitude = "Location"
)
Function ID
3-1
Function Introduced
v0.2.0.5 (March 31, 2020)
See Also
The integer-formatting function (format rounded values (i.e., no decimals shown and
input values are rounded as necessary): fmt_integer().
The vector-formatting version of this function: vec_fmt_number()
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_partsper(),
fmt_passthrough(),
fmt_percent(),
fmt_roman(),
fmt_scientific(),
fmt_spelled_num(),
fmt_tf(),
fmt_time(),
fmt_units(),
fmt_url(),
sub_large_vals(),
sub_missing(),
sub_small_vals(),
sub_values(),
sub_zero()